The Rise of the Research Agent: Augmenting Large Language Models with Web Search for Accelerated Discovery

1. Executive Summary

• This report explores the synergistic relationship between Large Language Models (LLMs) and web search, defining a new paradigm for AI-driven research agents. It addresses the growing need for autonomous research tools capable of navigating the complexities of modern information landscapes, analyzing vast datasets, and generating actionable insights. By integrating LLMs with retrieval-augmented generation (RAG) and planning frameworks like ReAct, these agents are transforming the research landscape across various domains.

• Key findings highlight the significant performance gains achieved through agentic architectures. For instance, ReAct-based agents demonstrate improved accuracy in multi-hop question answering, while multimodal RAG systems enhance contextual understanding by integrating diverse data sources. The report also addresses critical challenges, such as mitigating hallucination and ensuring ethical alignment, providing practical recommendations for responsible AI development. Market trends indicate a surge in demand for agentic AI talent, with median AI researcher salaries in Seoul reaching ₩70 million to ₩100 million annually. Looking ahead, the report outlines a technology roadmap for agentic AI, emphasizing the importance of iterative planning frameworks and prompt engineering best practices to ensure safety and alignment.

2. Introduction

• Imagine a world where complex research tasks are autonomously handled by AI agents, sifting through vast amounts of data, synthesizing information, and generating novel insights at unprecedented speed. This vision is rapidly becoming a reality with the rise of research agents, powered by Large Language Models (LLMs) and augmented with web search capabilities. But how effectively do these agents perform, and what are the key architectural and ethical considerations?

• The integration of LLMs with external knowledge sources represents a paradigm shift in AI-driven research. Traditional LLMs, limited by their static knowledge, struggle with tasks requiring up-to-date information or multi-step reasoning. By incorporating retrieval-augmented generation (RAG) and planning frameworks like ReAct, these research agents can overcome these limitations, accessing and processing real-time information to generate more accurate and reliable outputs.

• This report provides a comprehensive overview of the research agent landscape, examining the conceptual foundations, market trends, architectural underpinnings, and ethical considerations. It explores the capabilities and limitations of LLMs in both standalone and augmented modes, highlighting the benefits of RAG and ReAct in enhancing research agent performance. Through case studies and empirical validation, the report illustrates the transformative potential of agentic AI in accelerating discovery and informing decision-making across various domains.

• The structure of this report is designed to provide a holistic understanding of research agents. It begins by defining the conceptual foundations of agentic research systems, followed by an analysis of market and recruitment signals. The report then delves into the architectural details of LLMs, RAG, and planning frameworks, providing empirical validation through case studies and performance metrics. Finally, it addresses the critical aspects of human oversight and ethical governance, outlining a technology roadmap and providing strategic recommendations for practitioners.

3. The Synergy of LLMs and Web Search: Defining the Research Agent Paradigm

• 3-1. Conceptual Foundations of Agentic Research Systems

• This subsection lays the groundwork for the entire report by defining key concepts related to agentic research systems, particularly focusing on the roles and interplay between LLMs, RAG architectures, and planning mechanisms like ReAct. It also begins addressing the ethical considerations vital to responsible AI research, establishing the analysis frame for the later sections.

• The Deep Research Bench (DRB) report offers a structured taxonomy to distinguish between various AI research agent architectures. Traditional LLMs, while powerful, are limited by their static knowledge and struggle with tasks requiring external information. This contrasts sharply with Retrieval-Augmented Generation (RAG) systems, which enhance LLMs by integrating external knowledge retrieval mechanisms, significantly improving their accuracy and contextual awareness.

• Agentic architectures, as defined by DRB, represent the most advanced category. They incorporate planning and iterative refinement capabilities, allowing them to handle complex research tasks through a series of reasoned actions. Architectures like ReAct, which alternate between reasoning and acting, exemplify this approach, mimicking human researchers' problem-solving strategies. Understanding these distinctions is crucial for evaluating the capabilities and limitations of different AI research tools.

• The DRB benchmark assesses these architectures across various tasks, providing empirical evidence of their strengths and weaknesses (ref_idx 15). For instance, ReAct-based agents often outperform standalone LLMs in multi-hop question answering tasks, demonstrating the value of planning mechanisms. However, even agentic systems can struggle with tasks requiring creative reasoning or critical argumentation, highlighting the ongoing need for innovation.

• Strategically, this taxonomy provides a framework for organizations to assess their AI research needs and select the appropriate tools. For basic information retrieval, RAG systems may suffice. However, for more complex research tasks, agentic architectures with planning capabilities are essential. As AI research agents become more sophisticated, understanding these architectural nuances will be key to maximizing their effectiveness and ensuring responsible development.

• Recommendations for practitioners include conducting thorough evaluations of AI research tools using benchmarks like DRB and carefully considering the specific requirements of the research task. Investing in agentic architectures and exploring advanced planning mechanisms such as ReAct is essential for organizations seeking to leverage AI for deep research.

• Planning mechanisms like ReAct significantly enhance the autonomy of LLM-based research agents. ReAct allows agents to dynamically adjust their approach based on intermediate results, leading to more accurate and reliable outputs. This iterative process contrasts with the static, one-shot approach of traditional LLMs, which often struggle with complex tasks requiring multiple steps.

• ReAct's 'Reason + Act' cycle emulates human problem-solving by alternating between internal reasoning and external actions, such as web searches. This allows agents to gather relevant information, evaluate its credibility, and refine their understanding of the task at hand. By incorporating external knowledge, ReAct can mitigate the risk of hallucination, a common problem with LLMs that rely solely on their internal knowledge.

• Empirical studies, like those leveraging the Deep Research Bench, validate ReAct's effectiveness in multi-hop question answering and other complex tasks (ref_idx 63). These studies demonstrate that ReAct-based agents often outperform baseline methods, highlighting the benefits of iterative planning and external knowledge integration. Furthermore, research indicates that fine-tuning planning models with knowledge graph data can lead to substantial performance improvements.

• Strategically, the integration of ReAct-based planning offers a significant advantage for organizations seeking to automate complex research tasks. By reducing hallucination risks and enhancing accuracy, ReAct can improve the reliability of AI-generated insights, enabling organizations to make more informed decisions. This is particularly valuable in domains such as scientific research and policy analysis, where accuracy is paramount.

• Implementation-focused recommendations include adopting iterative planning frameworks like ReAct, prioritizing fine-tuning of planning models with relevant data, and rigorously evaluating the performance of agentic systems using established benchmarks. Investing in ReAct-based planning can substantially improve the quality and reliability of AI-driven research.

• Ethical alignment frameworks are crucial for ensuring the responsible development and deployment of LLM-based research agents, particularly in sensitive domains like healthcare. Retrieval-Augmented Generation (RAG) systems, while enhancing accuracy, can also amplify existing biases if not carefully designed. Constitutional AI offers a promising approach to embedding ethical principles directly into agentic AI systems.

• Constitutional AI frameworks, such as those developed by Anthropic, ensure alignment with societal norms and regulatory standards by incorporating ethical guidelines into the RAG process. In medical RAG applications, this involves cross-referencing diagnoses against patient autonomy principles and peer-reviewed journals, enhancing both diagnostic accuracy and ethical reliability. This dual-layered validation ensures that outputs are medically sound and ethically aligned with patient-centric care models (ref_idx 55).

• Infosys' Tech Navigator report emphasizes the importance of multi-agent integration pipelines for precise knowledge extraction and contextual relevance during text generation. These pipelines dynamically coordinate agents responsible for perception, reasoning, and action, addressing the risks of misinformation inherent in large-scale AI systems. For example, during diagnosis generation, one agent retrieves relevant medical literature while another evaluates its applicability based on patient-specific data.

• Strategically, the adoption of ethical alignment frameworks is essential for building trust in AI-driven research. By prioritizing transparency, auditability, and accountability, organizations can mitigate the risks of bias and misinformation, fostering responsible innovation. This is particularly critical in highly regulated industries such as healthcare, where ethical considerations are paramount.

• Recommendations for practitioners include implementing constitutional AI approaches in RAG systems, leveraging multi-agent integration pipelines for enhanced transparency, and adhering to regulatory mandates such as the EU's explainability by design directive. By prioritizing ethical considerations, organizations can ensure that AI research agents are used responsibly and contribute to societal well-being.

• 3-2. Market and Recruitment Signals for Agentic AI

• Building upon the conceptual groundwork laid in the previous subsection, this section transitions to practical market dynamics. By analyzing recruitment trends and salary benchmarks, it aims to provide a tangible understanding of the growing demand for agentic AI expertise.

• The demand for agentic AI talent is experiencing a significant surge, reflecting the growing adoption and strategic importance of these technologies. Analyzing LinkedIn job postings provides a quantifiable measure of this trend. In Q1 2025, a search for roles specifically requiring 'agentic AI' skills reveals a substantial increase compared to previous years, signaling a shift in industry priorities (ref_idx 420). This demand spans various sectors, from technology and finance to healthcare and manufacturing, underscoring the broad applicability of agentic AI.

• Several factors contribute to this heightened demand. Firstly, early adopters of generative AI are now integrating agentic AI to drive greater automation and efficiency in business operations. Capgemini's research indicates that approximately 30% of GenAI adopters have already incorporated AI agents, with a projected 48% rise by the end of 2025 (ref_idx 402). Secondly, advancements in multimodal RAG and planning frameworks like ReAct are creating new opportunities for AI-driven solutions, requiring specialized expertise in these areas (ref_idx 30). Finally, increasing investments in AI infrastructure and research hubs, such as the National AI Research Hub in Seoul, are fueling the need for skilled AI professionals (ref_idx 483).

• Job postings for roles like 'AI Coding Agent' and 'Multimodal RAG Researcher' highlight the specific skills in demand (ref_idx 30). These roles often require expertise in LLM architecture optimization, prompt engineering, reinforcement learning, and multimodal data processing. A review of job descriptions reveals a strong emphasis on practical experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and a track record of publications in top-tier AI conferences (e.g., NeurIPS, ICML).

• Strategically, this surge in demand presents both opportunities and challenges for organizations. Companies that proactively invest in building their agentic AI talent pool will gain a competitive advantage in developing and deploying innovative AI solutions. However, the limited supply of skilled professionals necessitates a focus on talent acquisition, training, and retention strategies. Organizations should consider partnerships with universities and research institutions to access cutting-edge expertise and develop internal training programs to upskill their existing workforce.

• To address the talent shortage, recommendations include establishing AI-focused internship programs, offering competitive compensation packages, and fostering a culture of innovation and continuous learning. Additionally, organizations should actively participate in AI research communities and contribute to open-source projects to attract top talent and enhance their brand reputation.

• The intensifying competition for AI talent is driving up salaries, particularly in key technology hubs like Seoul. Analyzing compensation data provides valuable insights into the market value of AI expertise. While precise, real-time salary figures are difficult to obtain due to the dynamic nature of the job market, Glassdoor and other salary benchmarking platforms offer indicative ranges for AI researcher roles in Seoul.

• Based on available data and industry reports, the median salary for AI researchers in Seoul with 3-5 years of experience is estimated to be between ₩70 million and ₩100 million annually as of Q2 2025. Senior AI scientists with 8+ years of experience and a strong publication record can command salaries exceeding ₩150 million (ref_idx 480). These figures represent a significant premium compared to traditional software engineering roles, reflecting the specialized skills and high demand for AI expertise.

• A recent Be Korea-savvy report highlighted the mismatch between salary expectations of South Korean graduates and actual offers, with graduates desiring an average of ₩40.23 million while companies offered ₩37.08 million (ref_idx 478). However, this gap is likely narrower for AI-related roles due to the intense competition for talent. Government initiatives, such as the InnoCore Research Program, which offers annual salaries of ₩90 million to postdoctoral researchers, further contribute to the upward pressure on AI salaries (ref_idx 481).

• Strategically, organizations need to be prepared to offer competitive compensation packages to attract and retain top AI talent. This includes not only base salary but also benefits, equity, and opportunities for professional development. Furthermore, companies should consider the long-term cost implications of escalating AI salaries and explore alternative talent models, such as remote work and distributed teams.

• Implementation-focused recommendations include conducting regular salary benchmarking exercises, developing transparent compensation policies, and offering performance-based bonuses to incentivize innovation and productivity. Additionally, organizations should invest in employee well-being and create a supportive work environment to foster loyalty and reduce employee turnover.

4. Architectural Deep Dive: LLMs, RAG, and Planning Frameworks

• 4-1. The LLM as Knowledge Generator and Contextualizer

• This subsection delves into the inherent capabilities and shortcomings of LLMs when used as standalone knowledge resources. It sets the stage for subsequent discussions on RAG and ReAct architectures by highlighting the necessity of external knowledge integration and iterative planning for reliable research agent performance.

• Large language models, while powerful, exhibit performance degradation in multi-turn question answering scenarios. This stems from an inability to maintain contextual coherence over extended dialogues, leading to inaccurate or irrelevant responses. As AI systems increasingly engage in complex tasks requiring sustained reasoning, this limitation poses a significant challenge.

• Research indicates that LLMs struggle to retain information from earlier turns, resulting in 'context processing failure' [ref_idx 5]. This manifests as premature problem-solving attempts, over-reliance on prior incorrect answers, and susceptibility to the most recent conversational inputs. This is especially critical in academic QA which inherently requires multi-hop reasoning and complex information synthesis.

• For example, a study evaluating 15 LLMs on coding, SQL writing, and API calling tasks found an average performance drop of 39% when instructions were delivered in multiple turns [ref_idx 5]. This highlights a significant gap between single-turn proficiency and real-world applicability, where tasks often necessitate iterative information exchange.

• To address this, strategic prompt engineering becomes crucial. Techniques such as prompt chaining, where outputs from one prompt serve as inputs for the next, can help maintain context and guide the LLM towards a more accurate final answer [ref_idx 36]. However, this relies on human oversight to design effective prompts, limiting full autonomy.

• Therefore, augmenting LLMs with external knowledge retrieval mechanisms is essential to compensate for inherent memory limitations and enhance long-term contextual understanding. By grounding responses in verifiable data sources, we can mitigate performance degradation and improve the reliability of LLM-driven research agents.

• The method of delivering instructions to an LLM—whether as a single, comprehensive prompt (full prompt) or as a series of smaller, incremental prompts (sharded prompt)—significantly impacts response accuracy and conversational flow. While full prompts provide complete context upfront, they can overwhelm the model with information, leading to processing inefficiencies. Sharded prompts, on the other hand, risk losing critical context across turns.

• Comparative studies reveal a trade-off between these two strategies. Full prompts tend to yield higher accuracy in single-turn tasks where all necessary information is readily available [ref_idx 5]. However, in more complex scenarios, the sheer volume of information can hinder the model's ability to focus on relevant details. The study [ref_idx 5] uses CONCAT strategy, in which shards are bundled and provided at once, to show that the information overload is detrimental to performance.

• Conversely, sharded prompts enable a more interactive and adaptive approach. By breaking down complex tasks into smaller, manageable steps, these prompts allow for iterative refinement and error correction [ref_idx 36]. However, if not carefully designed, they can also lead to context fragmentation and a loss of overall coherence.

• Consider a scenario where an LLM is tasked with summarizing a lengthy research paper. A full prompt might include the entire paper at once, potentially causing the model to miss key arguments. A sharded prompt, in contrast, could guide the model through specific sections, prompting it to identify the main points and supporting evidence step by step.

• The optimal prompting strategy depends on the specific task, the complexity of the information, and the desired level of user interaction. However, augmenting either approach with RAG can improve accuracy by grounding the LLM's responses in external, verifiable knowledge sources. Further, incorporating ReAct planning frameworks helps to strategically determine when and how to utilize web search to complement the information provided in initial prompts.

• Retrieval-Augmented Generation (RAG) has emerged as a promising technique to enhance LLM performance in academic question answering (QA) by integrating web search capabilities. By fetching relevant external documents, RAG systems can provide LLMs with up-to-date and contextually rich information, thereby mitigating hallucination and improving response accuracy [ref_idx 281].

• Several case studies highlight the effectiveness of RAG in academic QA. For instance, RAG models have been deployed to answer doctors' queries in the medical field. They use RAG systems to retrieve information from past patient records and the latest medical literature [ref_idx 49]. Also, a specialized RAG model developed in 2024 was developed for cancer clinical trials and its key performance results are documented.

• However, RAG performance is contingent on the quality of retrieved documents and the LLM's ability to effectively integrate them [ref_idx 283]. Research indicates that RAG systems can sometimes underperform compared to using user-uploaded files for context, due to the increased complexity of the retrieval workflow. As LLMs can be misled by hallucinated data, the performance improvement depends on whether there is sufficient data in context [ref_idx 288].

• Quantitative benchmarks further illustrate the benefits of RAG. For example, RAG-Evaluation-Dataset-KO offers performance metrics for various LLMs on RAG tasks across different domains like finance, public, medical, law, and commerce [ref_idx 369]. Models like claude3.5-sonnet and gpt-4o demonstrate strong performance, with average correctness scores exceeding 0.8 in several categories.

• Moving forward, optimizing RAG systems for academic QA requires a focus on refining retrieval strategies, improving LLM integration techniques, and developing more robust evaluation methodologies. By addressing these challenges, we can unlock the full potential of RAG to transform LLMs into reliable and accurate research agents.

• 4-2. RAG System Design and Optimization

• This subsection builds upon the previous discussion of LLM limitations and RAG's role in augmenting knowledge. It focuses on the practical design and optimization of RAG systems, providing empirical benchmarks and analysis of search engine ranking strategies relevant for academic question answering. It transitions from theoretical capabilities to concrete implementation considerations.

• Optimizing search engine ranking within RAG systems is crucial for retrieving the most relevant documents to augment LLMs. Effective ranking directly impacts the quality of information provided to the LLM, influencing the accuracy and coherence of generated responses. Poor ranking can lead to the inclusion of irrelevant or misleading documents, hindering the LLM's ability to synthesize accurate answers.

• Various factors influence search engine ranking in RAG, including indexing strategies, query formulation, and ranking algorithms. For academic QA, focusing on scholarly databases and search engines is essential. A key consideration is the trade-off between precision and recall. High precision ensures that retrieved documents are relevant, while high recall ensures that all relevant documents are retrieved. A balance must be struck to optimize overall RAG performance.

• Several benchmarks exist to evaluate search engine ranking optimization in RAG. Datasets like RAG-Evaluation-Dataset-KO provide performance metrics for various LLMs on RAG tasks across different domains, including finance, public, medical, law, and commerce [ref_idx 369]. These benchmarks assess the accuracy of retrieved documents and the LLM's ability to integrate them into coherent answers. Also, in terms of search engine optimization in general, properly utilizing HTML tags and meta tags enhances ranking [ref_idx 434].

• Infosys's Market Scan Report emphasizes RankRAG, a unified approach using a single LLM for both context ranking and answer generation, outperforming existing methods [ref_idx 433]. This demonstrates the potential of instruction fine-tuning to improve ranking and generation. Another key point to note is that the optimization of the retrieval process is not just about improving recall rates, but also the positioning of the document itself [ref_idx 445].

• Strategic implications involve prioritizing ranking algorithms that consider both relevance and diversity, optimizing query formulation through techniques like query expansion and paraphrasing, and continuously monitoring and evaluating ranking performance using appropriate metrics. In practice, this means adopting a systematic approach to experimentation and optimization, where different ranking strategies are tested and refined based on empirical results.

• Accurate question answering (QA) on academic databases is a critical measure of RAG system effectiveness in research contexts. High accuracy rates indicate that the RAG system can retrieve relevant information from scholarly sources and synthesize coherent, correct answers. Low accuracy rates, on the other hand, suggest limitations in retrieval strategies, LLM integration, or both.

• Several factors influence QA accuracy rates on academic databases, including the quality of indexing, the sophistication of the retrieval algorithm, and the LLM's ability to interpret and synthesize information. Access control to databases also plays a key role. As seen in [ref_idx 499], support for keyboard accessibility, title settings, and labels influence scholarly database QA rates.

• Available research indicates varying performance levels across different RAG systems and academic databases. For example, Table 2 of ref_idx 63 shows that the proposed framework outperforms the baseline methods on the majority of datasets. Notably, compared to ReAct and Self-Ask, the approach demonstrates significant improvement. LPKG(CodeQwen) and LPKG(Llama3) in the table represent the framework with fine-tuned CodeQwen and fine-tuned Llama3, with exact match results listed.

• A Databricks blog post highlights that retrieval performance can lead to improved RAG performance on FinanceBench, but not Databricks DocsQA [ref_idx 441]. Open RAG Eval is also leveraged to give enterprises insight into how to fine-tune hybrid search parameters and chunking strategies [ref_idx 440].

• Strategically, it is recommended to leverage benchmarks to establish performance baselines, experiment with various retrieval and ranking algorithms, and continually refine RAG systems based on empirical results. The integration of domain-specific knowledge and ontologies can also improve accuracy rates. An actionable step is to prioritize RAG systems capable of adapting to the specific nuances and requirements of different academic databases.

• 4-3. ReAct-Based Planning and Iterative Refinement

• This subsection explores the ReAct planning and iterative refinement framework, emphasizing how it can improve accuracy and reduce hallucination risks in LLM-based research agents. It provides a detailed examination of how ReAct integrates web search to enhance response reliability.

• ReAct (Reason + Act) is a planning framework designed to enhance the accuracy of LLM responses by integrating reasoning and action steps [ref_idx 63]. This iterative process allows the model to interact with its environment, typically through web search, to gather relevant information and refine its answers. The core idea is to break down complex tasks into manageable steps, where each step involves reasoning about the current state and deciding on the next action, which may include querying a search engine.

• The effectiveness of ReAct in improving web search response accuracy stems from its ability to mitigate the limitations of standalone LLMs. As discussed in the previous subsections, LLMs often struggle with multi-turn reasoning and can suffer from context processing failures. ReAct addresses these issues by providing a structured approach to information retrieval and synthesis. The model explicitly reasons about what information it needs, formulates a search query, and then integrates the search results into its response. This iterative process helps maintain contextual coherence and reduces the reliance on the model's internal knowledge, which may be outdated or inaccurate.

• Empirical evidence supports the benefits of ReAct in improving accuracy. Table 2 of ref_idx 63 shows that a framework incorporating ReAct outperforms baseline methods on several complex question answering datasets. Specifically, compared to approaches like Self-Ask, ReAct demonstrates significant improvement by decoupling planning and RAG. The results indicate fine-tuning with knowledge graph data further enhances the planning capabilities of ReAct.

• However, the accuracy gains achieved by ReAct are contingent on the quality of the planning process and the relevance of the search results. Poorly formulated queries or irrelevant search results can lead to inaccurate responses, even with ReAct's iterative refinement. Also, the speed with which real estate agents can access information is important, and ReAct can help in that endeavor [ref_idx 535]. Therefore, optimizing the planning component and improving the retrieval strategies are crucial for maximizing the benefits of ReAct.

• Strategically, prioritize the development of robust planning modules capable of generating effective search queries. Implement continuous monitoring and evaluation mechanisms to assess the accuracy of ReAct-based responses and identify areas for improvement. The integration of domain-specific knowledge and ontologies can also enhance the relevance of search results and improve overall accuracy.

• Hallucination, the generation of incorrect or nonsensical information, is a significant challenge in LLM-based research agents. Web search-based hallucination mitigation techniques aim to reduce these inaccuracies by grounding LLM responses in verifiable external knowledge. By retrieving relevant documents and integrating them into the response generation process, these techniques can help prevent the model from relying on its internal, potentially flawed, knowledge [ref_idx 572].

• One common approach is Retrieval-Augmented Generation (RAG), where the LLM retrieves relevant documents from a knowledge base and uses them to generate its response. The effectiveness of RAG in reducing hallucinations depends on several factors, including the quality of the retrieved documents, the LLM's ability to effectively integrate the retrieved information, and the presence of appropriate validation mechanisms. Also, various techniques for RAG evaluation are presented in [ref_idx 528], providing metrics for coverage and retrieval.

• Research indicates that while RAG can reduce hallucinations, it does not eliminate them entirely [ref_idx 577]. A study by Transluce found that even with web search capabilities, LLMs can still make up actions and provide inaccurate narrations. Similarly, a study on legal AI tools found that RAG-based systems hallucinate between 17% and 33% of the time [ref_idx 377]. These findings highlight the limitations of RAG and the need for additional mitigation strategies.

• Several techniques can be used to further reduce hallucinations in web search-based systems. These include fact-checking mechanisms, human-in-the-loop validation, and reinforcement learning from human feedback (RLHF). Context-Aware Decoding (CAD) uses the difference in output probabilities to emphasize contextual information [ref_idx 567]. Additionally, techniques like chain-of-thought prompting and step-back prompting can enhance self-verification and improve accuracy.

• To minimize hallucination risks, implement a multi-layered approach that combines RAG with additional validation and refinement techniques. Prioritize the development of robust fact-checking mechanisms capable of verifying the accuracy of retrieved information. Continuously monitor and evaluate the performance of LLM-based research agents to identify and address sources of hallucinations. The development of high-quality, reliable research primitives, such as the ability to verify and find the original source of facts, is also critical [ref_idx 573].

5. Empirical Validation: Case Studies and Performance Metrics

• 5-1. DRB Benchmark Results and Practical Implications

• This subsection synthesizes the empirical findings from the Deep Research Bench (DRB) report, offering a critical assessment of AI research agents' performance across diverse tasks. By highlighting specific accuracy metrics and contextual understanding gaps, we set the stage for a nuanced discussion of AI's current capabilities and limitations, informing subsequent analyses of collaborative AI labs and HCI applications.

• The Deep Research Bench (DRB) report (ref_idx 15) provides a rigorous evaluation of AI research agents across a spectrum of web-based research tasks. These tasks, ranging from fact retrieval to claim validation and dataset compilation, mirror the complex challenges faced by human analysts and policymakers. Initial assessments reveal varying degrees of success, highlighting the need for task-specific performance evaluations to understand AI's true research aptitude.

• DRB employs the ReAct architecture, enabling agents to iteratively reason, act (e.g., web search), and observe. This approach aims to replicate human research methodologies. However, the report underscores performance discrepancies across different tasks. For instance, while AI agents demonstrate proficiency in numerical fact retrieval (e.g., identifying the number of FDA Class II medical device recalls), they often struggle with more nuanced tasks requiring multi-step reasoning or conflicting information assessment.

• Concrete accuracy metrics across various DRB tasks are crucial for pinpointing areas of strength and weakness. Tasks like 'claim validation' often expose vulnerabilities, where agents may fail to critically evaluate sources or synthesize contradictory evidence effectively. Similarly, 'data set compilation' tasks reveal limitations in aggregating and structuring information from multiple web sources into coherent datasets.

• The task-wise accuracy discrepancies underscore the need for specialized AI agent designs tailored to specific research domains. While general-purpose LLMs can handle simpler information retrieval, complex research questions necessitate architectures optimized for reasoning, source evaluation, and data synthesis. This necessitates moving beyond 'one-size-fits-all' models toward task-aware AI research agents.

• To improve task-wise accuracy, we recommend focusing on prompt engineering techniques that explicitly guide the agent's reasoning process, incorporating external knowledge verification modules, and developing specialized training datasets for complex research tasks. Regularly benchmarking and analyzing task-specific accuracy will be crucial for iterative refinement and targeted development.

• Beyond task completion accuracy, the DRB report also evaluates AI research agents based on their recall and contextual understanding (ref_idx 34). Recall measures the completeness of the information retrieved, while contextual understanding assesses the agent's ability to synthesize information within the broader research context. Analyzing these metrics reveals critical gaps in creative reasoning and critical argumentation.

• The DRB employs a 'RetroSearch' environment, which offers a static and controlled web environment for evaluating AI agents, eliminating the fluctuations of the live web. Even within this controlled environment, the report notes that AI agents often exhibit limitations in contextual comprehension. Agents may accurately retrieve relevant facts but fail to fully grasp the nuances or implications within the research problem.

• Low recall scores often stem from the agent's inability to identify all relevant sources or to effectively filter out irrelevant information. This highlights the need for improved information retrieval strategies that can navigate complex web environments and prioritize high-quality sources. Combining LLMs with RAG systems can significantly improve this.

• Lowered context F1 scores demonstrate challenges in synthesizing disparate information and constructing coherent arguments. AI agents struggle in critical argumentation and creative reasoning, even when equipped with the necessary factual data. This often results in superficial analyses or conclusions that lack depth and originality.

• Addressing these limitations requires a multi-pronged approach. We recommend incorporating advanced reasoning engines that can simulate human-like critical thinking, integrating domain-specific knowledge bases to enhance contextual understanding, and refining training datasets to emphasize argumentation and creative synthesis skills. Further research is needed to develop metrics beyond the DRB to better evaluate creativity.

• 5-2. Collaborative AI Lab Case Study

• Having established a baseline understanding of AI research agent performance through DRB benchmarks, this subsection delves into a case study of a collaborative AI lab. This analysis provides real-world insights into multi-agent workflows and the interplay between autonomous and human-supervised research modes.

• The case study of the AI research lab (ref_idx 18) highlights the impact of human oversight on the quality of research outputs. The lab operates in two modes: a fully autonomous mode where AI agents handle the entire research pipeline, and a collaborative mode where human researchers supervise and refine the AI's outputs. Comparative analysis reveals significant quality gains in the collaborative mode, underscoring the continued importance of human expertise.

• In the fully autonomous mode, AI agents autonomously perform tasks such as literature review, experiment design, data preparation, result analysis, and manuscript drafting. While this mode demonstrates the potential for end-to-end automation, the resulting manuscripts often suffer from issues such as hallucination and lack of critical depth. The 'professor AI agent', responsible for drafting and polishing the final manuscript, may incorporate inaccurate information or fail to adequately address nuanced arguments.

• The collaborative mode introduces human researchers to review the AI's outputs at each stage of the research pipeline. This oversight allows for the correction of errors, refinement of experimental designs, and incorporation of deeper insights. Specifically, human researchers can identify and rectify instances of hallucination, ensuring the accuracy and reliability of the research findings. They can also enhance the manuscript's argumentation and contextual understanding.

• Quantifying the quality gain from human supervision is crucial for justifying the collaborative approach. While ref_idx 18 does not provide precise percentage improvements, it indicates that the collaborative mode consistently produces higher-quality manuscripts than the fully autonomous mode. This suggests that human oversight adds significant value, particularly in tasks requiring critical reasoning, source evaluation, and creative synthesis.

• To maximize the benefits of the collaborative approach, we recommend focusing human supervision on tasks that AI agents struggle with, such as literature review, experimental design, and manuscript drafting. Implementing robust quality control measures, such as peer review and expert validation, is essential for ensuring the accuracy and reliability of research outputs. This iterative refinement process ultimately yields higher-quality manuscripts that are more likely to advance scientific knowledge.

• Beyond quality enhancements, the AI research lab case study (ref_idx 18) also demonstrates significant runtime and cost efficiencies. By automating various research tasks, the lab reduces the amount of time and resources required to complete a research project. This efficiency gain allows researchers to explore a wider range of research questions and accelerate the pace of scientific discovery.

• In a traditional research setting, tasks such as literature review, data collection, and experiment execution can consume a significant amount of time and resources. The AI research lab automates these tasks using specialized AI agents, freeing up human researchers to focus on higher-level activities such as problem formulation, hypothesis generation, and critical analysis. This division of labor maximizes the productivity of the research team.

• The 'PhD student agent', for example, is tasked with identifying and summarizing relevant prior literature, significantly reducing the time required for literature reviews. The 'machine learning engineer agent' automates data preparation and experiment execution, accelerating the experimental process. The AI agents also facilitate communication and collaboration within the research team, streamlining the overall workflow.

• While ref_idx 18 does not provide specific percentage reductions in runtime or cost, it highlights the potential for substantial efficiency gains. The report notes that AI agents can handle a significant portion of the research workload, allowing a smaller team to conduct more experiments at a lower cost. This increased efficiency can be particularly beneficial for resource-constrained research institutions or projects with tight deadlines.

• To further enhance runtime and cost efficiencies, we recommend optimizing the AI agents' performance through continuous training and refinement. Implementing efficient data management strategies and optimizing the research workflow can also contribute to significant savings. Quantifying the specific cost and time reductions achieved through AI automation is essential for demonstrating the value proposition of AI-driven research labs and attracting further investment.

• 5-3. HCI and Clinical Applications

• Building upon the insights from the collaborative AI lab case study, this subsection examines HCI (Human-Computer Interaction) and clinical applications of agentic AI systems. This will illustrate user-centered design principles and ethical considerations in the development of AI-driven research tools.

• AI-driven diary studies offer a novel approach to capturing user experiences in real-time, providing rich qualitative data on user behavior and satisfaction. The methodological rigor of these studies is crucial for ensuring the reliability and validity of the insights gained. Understanding user satisfaction levels is paramount for gauging the effectiveness of AI-driven diary tools in HCI.

• Traditional diary studies have been adapted to incorporate AI for automated data collection and analysis (ref_idx 21). These AI-enhanced methods can streamline the diary entry process, provide personalized prompts, and automatically categorize user feedback. However, maintaining user engagement and ensuring data quality remain significant challenges. Factors such as the frequency of prompts, the complexity of the entry format, and the perceived value of participation can all impact user satisfaction.

• While specific user satisfaction percentages for AI-driven diary studies are not explicitly provided in ref_idx 21, related research (ref_idx 540, 541) indicates that AI tools generally correlate with increased job satisfaction and productivity among knowledge workers. Extrapolating from these findings suggests that well-designed AI diary tools, which reduce user burden and provide actionable insights, can lead to higher satisfaction levels.

• To maximize user satisfaction, diary studies should focus on user-centered design principles, such as simplicity, personalization, and feedback integration. Tools that offer intuitive interfaces, customizable prompts, and clear benefits for users are more likely to be well-received. Furthermore, researchers must address ethical considerations related to data privacy and security, ensuring that user data is handled responsibly and transparently.

• Recommendations for improving user satisfaction in AI diary studies include conducting thorough pilot testing to identify usability issues, providing clear instructions and support to participants, and offering incentives to encourage continued engagement. Regular monitoring of user feedback and iterative design adjustments can further enhance the overall user experience.

• Clinical drafting agents represent a promising application of AI in healthcare, with the potential to automate the creation of medical documents, such as patient notes, discharge summaries, and research reports. However, ensuring the accuracy and compliance of these agents with medical ethics is critical for patient safety and regulatory adherence. The error rate in clinical drafting directly impacts the reliability and trustworthiness of these AI systems.

• Clinical drafting agents rely on natural language processing (NLP) and machine learning (ML) algorithms to analyze patient data and generate relevant text (ref_idx 42). These algorithms can be susceptible to errors, biases, and hallucinations, which can lead to inaccuracies in the generated documents. Furthermore, clinical drafting agents must adhere to strict medical coding standards and regulatory guidelines, such as HIPAA, to protect patient privacy and confidentiality.

• Ref_idx 42 describes a bilingual on-premise AI agent for clinical drafting, highlighting the importance of validation and formal analysis to ensure accuracy. While specific error rates are not quantified, the study emphasizes the need for robust testing and quality control measures to minimize errors. Error rate metrics may include hallucination rates, factual correctness, adherence to medical terminology, and compliance with regulatory guidelines.

• To minimize error rates, clinical drafting agents should incorporate advanced error detection mechanisms, such as rule-based validation, statistical anomaly detection, and human-in-the-loop review. Training datasets must be carefully curated to avoid biases and ensure representation of diverse patient populations. Additionally, ongoing monitoring and feedback mechanisms are essential for identifying and addressing emerging errors.

• Recommendations for improving the accuracy and compliance of clinical drafting agents include implementing rigorous testing protocols, incorporating domain-specific knowledge bases, and establishing clear ethical guidelines for AI development and deployment. Furthermore, healthcare organizations should invest in training and education programs to ensure that clinicians can effectively use and oversee AI-driven drafting tools.

6. Human Oversight and Ethical Governance

• 6-1. Prompt Engineering for Alignment and Safety

• This subsection explores the critical role of prompt engineering in shaping the behavior and reliability of agentic AI systems. It builds upon the preceding section's discussion of core AI architectures by focusing on how carefully crafted prompts can enhance alignment, reduce undesirable outputs like hallucinations, and ultimately improve the overall safety and trustworthiness of AI agents in research and other applications.

• Hallucinations, where AI models generate factually incorrect or misleading information, pose a significant challenge to the deployment of reliable research agents. Prompt chaining, a strategy involving the decomposition of complex tasks into a sequence of simpler prompts, offers a promising avenue for mitigating these risks by allowing LLMs to focus on specific sub-tasks, thereby reducing cognitive overload and potential for error.

• The core mechanism behind hallucination reduction via prompt chaining lies in its ability to guide the LLM through a structured reasoning process. By breaking down a complex query into smaller, more manageable steps, each prompt can be designed to elicit a specific type of information or reasoning, allowing the LLM to concentrate its resources and expertise on that particular aspect of the problem. This is in contrast to monolithic prompting, where the LLM is presented with the entire problem at once, potentially leading to confusion and inaccuracies (ref_idx 308). Furthermore, the intermediate steps in a prompt chain can be validated, ensuring procedural accuracy (ref_idx 248).

• Consider a scenario where a research agent is tasked with summarizing a complex scientific paper. Using prompt chaining, the task can be divided into sub-prompts such as: (1) identify the key research question, (2) summarize the methodology, (3) highlight the main findings, and (4) discuss the implications. By addressing each of these sub-tasks separately, the LLM can generate a more accurate and comprehensive summary than if it were simply asked to summarize the entire paper in a single prompt (ref_idx 247). IBM also notes prompt chaining as an advanced implementation of prompt engineering, leading to more accurate responses (ref_idx 250).

• The strategic implication is that prompt chaining should be a primary design consideration for research agents dealing with complex or nuanced tasks. By carefully structuring the problem and guiding the LLM through a series of focused steps, researchers and developers can significantly reduce the likelihood of hallucinations and improve the overall reliability of the AI agent. This is particularly important in domains where accuracy is paramount, such as scientific research or medical diagnosis (ref_idx 246).

• For implementation, organizations should prioritize the development of robust prompt libraries and frameworks that support the creation and management of prompt chains. These frameworks should include tools for defining sub-tasks, validating intermediate outputs, and tracking the overall performance of the prompt chain. Additionally, researchers should invest in empirical studies to quantify the hallucination reduction achieved by different prompt chaining strategies and identify best practices for their application (ref_idx 308).

• While prompt chaining offers benefits in mitigating hallucinations, it's crucial to understand its impact on overall accuracy compared to full-prompt strategies. Full-prompt strategies involve providing all instructions at once, while prompt chaining breaks the task into sequential prompts. The trade-off lies in potentially sacrificing some immediate efficiency for enhanced accuracy and reduced hallucination rates.

• The mechanism at play involves a balance between contextual understanding and cognitive load. Full prompts provide the LLM with complete context upfront, potentially allowing it to grasp the nuances of the task more effectively. However, this approach can also overwhelm the LLM, leading to errors or inaccuracies, particularly in complex tasks. Prompt chaining, conversely, reduces the cognitive burden by focusing the LLM on smaller, more manageable sub-tasks (ref_idx 308). Some studies indicate that multi-turn interactions might cause a performance drop, possibly due to the model losing track of the conversation or trying to solve the problem prematurely (ref_idx 5).

• Research indicates mixed results when comparing the accuracy of prompt chaining versus full prompts. Some studies suggest that breaking down tasks can lead to more accurate and efficient results, especially in scenarios requiring multi-step reasoning (ref_idx 249). For instance, in generating a complex legal document, prompt chaining could involve separate prompts for outlining the structure, drafting specific clauses, and ensuring compliance with regulations. However, other studies show that providing all the information upfront can sometimes yield better results, particularly when the task requires a holistic understanding of the context (ref_idx 5). In ZURU's floor plan generation experiments with Amazon Bedrock, prompt engineering increased instruction adherence, which underscores the importance of the right approach in achieving the desired output (ref_idx 319).

• Strategically, the choice between prompt chaining and full-prompt strategies should be driven by the specific characteristics of the task at hand. For tasks requiring high precision and minimal hallucinations, such as scientific research or legal drafting, prompt chaining is likely the superior approach. Conversely, for tasks requiring creativity or broad contextual understanding, a full-prompt strategy may be more appropriate, provided that adequate measures are taken to mitigate hallucination risks (ref_idx 309). SAP acknowledges that different LLMs may require unique prompts for the best results (ref_idx 312).

• For implementation, organizations should conduct rigorous testing to compare the accuracy and hallucination rates of different prompting strategies for various tasks. This testing should involve both quantitative metrics and qualitative assessments to identify the optimal approach for each use case. Furthermore, prompt engineering should be viewed as an iterative process, with continuous refinement and optimization based on empirical results. Tom's Guide suggests that breaking tasks into simpler prompts leads to clearer results (ref_idx 309).

• 6-2. Constitutional AI and Ethical Validation

• Building on the critical role of prompt engineering in ensuring AI alignment and safety, this subsection delves into the ethical dimension by examining constitutional AI and frameworks for embedding ethical principles into agentic AI systems.

• Constitutional AI offers a structured approach to embedding ethical principles directly into AI systems, particularly relevant for sensitive applications like medical Retrieval-Augmented Generation (RAG). Unlike traditional methods that rely on post-hoc evaluations, Constitutional AI integrates ethical guidelines into the RAG process itself, shaping the AI's behavior from the outset.

• The core mechanism involves training the AI to cross-reference its outputs against a set of predefined ethical principles, such as patient autonomy and data privacy. In medical RAG, this means that diagnoses and treatment recommendations are validated not only against medical literature but also against these ethical constraints. Infosys' Tech Navigator report highlights medical RAG's cross-referencing of diagnoses against patient autonomy principles and peer-reviewed journals to enhance diagnostic accuracy and reliability in clinical decision-making (ref_idx 55). This dual-layered validation ensures outputs are medically sound and ethically aligned with patient-centric care models. However, this also introduces the risk of reducing the AI's ability to provide novel solutions.

• For instance, consider a medical RAG system designed to assist in cancer clinical trials. Using Constitutional AI, the system would not only retrieve relevant trial data but also assess the ethical implications of enrolling specific patient populations, ensuring that vulnerable groups are not disproportionately burdened. Furthermore, the system can be designed to flag potential conflicts of interest or biases in the trial design, promoting fairness and transparency. Table 2 from 'RAG LLMs are Not Safer' compares probabilities for generating unsafe responses in non-RAG and RAG settings. This highlights the need to consider safety, even when using a RAG system.

• The strategic implication is that Constitutional AI can significantly enhance the trustworthiness and reliability of medical RAG systems, mitigating risks associated with bias, misinformation, and ethical violations. By embedding ethical principles into the AI's decision-making process, healthcare organizations can ensure that AI-driven tools align with their values and regulatory requirements.

• For implementation, healthcare organizations should prioritize the development of comprehensive ethical guidelines tailored to specific medical contexts. These guidelines should be integrated into the training data and reward functions of the AI system, ensuring that ethical considerations are consistently prioritized. Organizations should also establish robust monitoring and auditing mechanisms to detect and address any ethical violations or unintended consequences (ref_idx 55).

• System theory provides a holistic framework for understanding and governing complex AI systems, particularly agentic AI. Agentic AI, characterized by autonomous agents interacting within a broader ecosystem, requires a governance approach that considers the interdependencies and feedback loops between individual agents and the overall system.

• The core principle of system theory is that the behavior of a system is not simply the sum of its parts but rather emerges from the interactions and relationships between those parts. In the context of agentic AI, this means that governance mechanisms must account for how individual agent decisions can cascade through the system, creating unintended consequences or emergent risks. Miehling, Ramamurthy, Varshney, Riemer, and Bouneffouf's research emphasizes that agentic AI needs a systems theory to address the complexities of these interactions (ref_idx 228).

• Consider a scenario where multiple AI agents are deployed to manage different aspects of a supply chain. If each agent optimizes its own performance without considering the broader system, it could lead to bottlenecks, inefficiencies, or even cascading failures. For example, an agent responsible for inventory management might order excessive quantities of a particular product without considering the capacity of the transportation network, leading to delays and increased costs. General Systems Performance Theory (GSPT) can be used to optimize human performance (ref_idx 463).

• The strategic implication is that system theory provides a valuable lens for designing effective governance frameworks for agentic AI. By considering the interdependencies and feedback loops within the system, organizations can develop governance mechanisms that promote overall system stability, resilience, and ethical behavior.

• For implementation, organizations should adopt a system-oriented approach to AI governance, focusing on the design of clear communication channels, feedback mechanisms, and accountability structures. This may involve establishing cross-functional teams responsible for monitoring system-wide performance, identifying potential risks, and implementing corrective actions. Additionally, organizations should invest in tools and techniques for visualizing and simulating system behavior, allowing them to anticipate and mitigate potential unintended consequences (ref_idx 228).

7. Future Trajectories and Strategic Recommendations

• 7-1. Technology Roadmap for Agentic AI

• This subsection outlines a technology roadmap for agentic AI, focusing on defining short-term R&D milestones and projecting long-term goals aligned with multimodal and agentic RAG trends. It builds upon the previous sections by translating the theoretical foundations and empirical validations into actionable strategic recommendations, setting the stage for practical implementation.

• Defining concrete milestones for agentic AI development between 2025 and 2026 requires a focus on translating current R&D priorities into tangible objectives. The primary challenge lies in coordinating advancements across multiple domains, including multimodal RAG, coding agents, and responsible AI, as highlighted by recruitment trends (ref_idx 30). Success hinges on strategic resource allocation and a clear understanding of the technological dependencies between these areas.

• The core mechanisms for achieving these milestones involve optimizing LLM architectures, mitigating hallucination, and developing domain-specific agents. Job postings (ref_idx 30) emphasize the need for expertise in pruning, merging, and quantization techniques to enhance LLM performance. Simultaneously, research into hallucination detection and mitigation, coupled with prompt engineering and instruction tuning, is crucial for ensuring the reliability and safety of agentic AI systems.

• Case studies from AI research labs (ref_idx 18) demonstrate the effectiveness of multi-agent systems in accelerating research pipelines. By assigning specialized tasks to distinct agents, such as literature review, methodology alignment, and document formatting, these systems significantly reduce drafting time and improve narrative cohesion. Similarly, in coding agent development, linguistic and systemic feedback-based reinforcement learning can enhance reasoning performance.

• The strategic implication is that organizations must prioritize R&D efforts on foundational technologies like LLM optimization, hallucination mitigation, and domain-specific agent development to achieve short-term milestones. This involves fostering collaboration between researchers, engineers, and ethicists to ensure that agentic AI systems are not only powerful but also responsible and aligned with human values.

• Recommendations include establishing dedicated R&D teams focused on each core priority, implementing rigorous testing and validation protocols, and investing in talent development programs to cultivate expertise in agentic AI. Furthermore, organizations should actively participate in industry consortia and open-source initiatives to share knowledge and accelerate innovation.

• Projecting long-term adoption forecasts for multimodal RAG between 2027 and 2030 requires anticipating regulatory shifts, market trends, and technological advancements. The central challenge lies in accurately estimating the impact of factors such as data privacy regulations, ethical AI frameworks, and the increasing availability of multimodal data on the adoption of RAG technologies.

• The core mechanisms driving multimodal RAG adoption include advancements in computer vision, natural language processing, and sensor fusion, along with the development of robust AI governance platforms. Multimodal RAG systems will increasingly integrate data from diverse sources, such as images, audio, video, and sensor data, to provide more comprehensive and context-aware responses (ref_idx 320). This requires the development of sophisticated algorithms for data fusion, knowledge representation, and reasoning.

• Market analysis suggests that by 2027, over 50% of generative AI models used by enterprises will be domain-specific, a sharp rise from just 1% in 2023 (ref_idx 254). This trend reflects the growing demand for customized RAG systems that can address the unique needs of specific industries and applications. Furthermore, the adoption of agent-powered RAG systems will enable intelligent data retrieval from diverse sources (ref_idx 321), ensuring the delivery of the most relevant and context-aware outputs.

• The strategic implication is that organizations must invest in developing multimodal RAG capabilities and preparing for the ethical and regulatory challenges associated with their deployment to achieve long-term goals. This involves building expertise in multimodal data processing, AI governance, and ethical AI frameworks, as well as actively engaging with policymakers and regulators to shape the future of AI governance.

• Recommendations include establishing partnerships with academic institutions and research labs to advance multimodal RAG technologies, developing AI governance frameworks that align with ethical principles and regulatory requirements, and investing in training programs to cultivate expertise in multimodal data processing and AI governance. Moreover, organizations should closely monitor market trends and regulatory developments to adapt their strategies and ensure long-term success.

• 7-2. Policy and Market Outlook

• This subsection anticipates regulatory and market shifts influencing agentic AI adoption, providing a crucial bridge between technological roadmaps and strategic market positioning. It builds upon the previous discussion of short and long-term R&D milestones, now translating these advancements into an understanding of expected investment patterns and policy changes. This sets the stage for actionable recommendations tailored to practitioners.

• Mapping recruitment trends to expected investment patterns in agentic AI from 2025 to 2027 requires a nuanced understanding of how talent acquisition translates into financial commitments. While recruitment signals indicate strong momentum, a more granular analysis is needed to quantify the anticipated investment growth. The central challenge involves deciphering the correlation between the demand for specific skill sets (e.g., multimodal RAG, coding agents) and the allocation of capital towards related R&D and deployment initiatives (ref_idx 30).

• According to a Capgemini Research Institute report, AI is currently driving positive returns on investment, with an average return of nearly 1.7 times (ref_idx 399). This positive ROI is encouraging enterprises to increase their GenAI investments, with 62% of surveyed organizations planning to grow their investment this year compared to last year (ref_idx 402). This trend is echoed by TD Securities, estimating that spend on Agentic AI software will increase from US$3.4 billion in 2025 to US$9.6 billion in 2026, heading to US$23.6 billion in 2027 and US$51.5 billion in 2028, representing an approximate 150% 3-yr compound annual growth rate (CAGR) (ref_idx 400).

• IBM's Institute for Business Value study, surveying 2,900 executives worldwide, reveals a significant shift towards AI implementation in business processes, with AI-enabled workflows expected to surge from 3% to 25% by the end of 2025. Furthermore, 70% of executives consider agentic AI crucial for their organization's future, and AI investment is expected to rise from 12% to 20% of IT spend by 2026 (ref_idx 403). These findings suggest a substantial reallocation of IT budgets towards agentic AI capabilities.

• The strategic implication is that organizations must align their recruitment and investment strategies to capitalize on the projected growth in agentic AI. This requires not only attracting and retaining talent with expertise in key areas but also ensuring that investments are directed towards initiatives that deliver tangible business value. A potential pitfall is overstating adoption rates due to varying definitions of AI agents versus Gen AI assistants. As highlighted by the Capgemini Research Institute, the survey’s broad phrasing of “AI agent use” may include everything from pilots to full-scale deployments (ref_idx 256).

• Recommendations include establishing clear metrics for measuring the ROI of agentic AI investments, fostering collaboration between HR and finance departments to ensure that talent acquisition aligns with budgetary priorities, and actively monitoring industry trends to identify emerging investment opportunities. Furthermore, organizations should address ethical concerns and ensure compliance with relevant regulations to build trust and mitigate risks.

• Identifying recent regulatory shifts impacting agentic AI adoption is crucial for ensuring informed policy outlook. The central challenge involves navigating the evolving landscape of AI governance, which is characterized by differing approaches across jurisdictions and a growing emphasis on ethical considerations. Organizations must stay abreast of these changes to ensure compliance and mitigate potential risks. Furthermore, organizations must be aware that the increase of AI agent use may face more limited adoption, as actual AI agent adoption could be more limited. The survey’s broad phrasing of “AI agent use” may include everything from pilots to full-scale deployments (ref_idx 256).

• Recent developments include the EU AI Act, which sets out a comprehensive framework for AI governance, classifying AI systems based on risk levels and imposing corresponding obligations (ref_idx 454). Although the United Kingdom is no longer an EU member, the Act has significant implications for UK businesses, as it extends beyond EU borders, requiring compliance for AI systems interacting with EU users or customers (ref_idx 454). In tandem, the EU plans to establish a new European AI Office to develop EU expertise in the field of AI and to contribute to the implementation of EU legislation (ref_idx 455).

• In the United States, while Congress has not yet passed comprehensive legislation to regulate the AI industry, several states have taken action, and the White House has issued guidance outlining principles for AI development. The AI Bill of Rights Blueprint and the Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence highlight the need for algorithmic discrimination protection, data privacy, and human oversight (ref_idx 461).

• The strategic implication is that organizations must adopt a proactive approach to AI compliance, developing frameworks that align with evolving regulatory requirements and ethical guidelines. This involves establishing clear lines of responsibility, conducting risk assessments, and implementing safeguards to mitigate potential harms. The need to increase focus on ethical concerns is shown through the Gartner prediction that 40% of agentic AI projects will be abandoned due to poor risk management, unclear business benefits, or growing costs (ref_idx 394).

• Recommendations include establishing partnerships with legal and ethical experts to navigate the complex regulatory landscape, implementing AI governance frameworks that align with industry best practices, and actively engaging with policymakers and regulators to shape the future of AI governance. Furthermore, organizations should embrace transparency and explainability to build trust and foster responsible AI innovation.

• 7-3. Final Recommendations for Practitioners

• This subsection provides actionable guidance for integrating agentic AI into research workflows, synthesizing the insights from previous sections into practical recommendations. It directly addresses the user's initial question by outlining how practitioners can effectively leverage agentic AI tools and techniques, ensuring responsible and impactful adoption.

• Prioritizing iterative planning frameworks like ReAct is crucial for practitioners seeking to effectively integrate agentic AI into research workflows. The central challenge lies in selecting and implementing frameworks that enhance the reliability, accuracy, and adaptability of AI agents in complex tasks. While specific adoption rate data for 2025 is currently unavailable, the demonstrated benefits of ReAct in mitigating hallucination and improving contextual understanding strongly advocate for its adoption (ref_idx 63).

• ReAct's core mechanism involves interleaving reasoning and acting, allowing AI agents to dynamically adjust their plans based on environmental feedback and retrieved information. This iterative process enables agents to recover from errors, refine their strategies, and ultimately achieve higher performance in multi-hop question answering and other complex tasks (ref_idx 63). Unlike chain-of-thought approaches, ReAct explicitly models the interaction between reasoning and action, leading to more robust and adaptable behavior.

• Empirical validation of ReAct's effectiveness can be seen in its superior performance compared to baseline methods like Self-Ask in complex QA tasks. By decoupling planning and retrieval-augmented generation into separate models, ReAct allows each component to focus more intensively on its individual task, leading to significant improvements in accuracy and contextual understanding (ref_idx 63).

• The strategic implication is that organizations must invest in the development and deployment of iterative planning frameworks like ReAct to unlock the full potential of agentic AI in research. This involves integrating ReAct into existing workflows, training researchers in its effective use, and continuously monitoring and evaluating its performance to identify areas for improvement.

• Recommendations include conducting pilot projects to evaluate the feasibility and benefits of ReAct in specific research contexts, developing best practices for prompt engineering and knowledge retrieval within the ReAct framework, and fostering collaboration between researchers and AI experts to ensure that the framework is effectively tailored to the unique needs of each organization.

• Adopting prompt engineering best practices is paramount for ensuring the safety, alignment, and overall effectiveness of agentic AI in research. The primary challenge lies in designing prompts that elicit desired agent behaviors while minimizing the risk of unintended consequences, such as hallucination, bias, or unethical actions. This requires a deep understanding of prompt chaining strategies and their impact on task decomposition (ref_idx 36).

• The core mechanism of prompt engineering involves crafting prompts that guide the AI agent towards specific goals, constraints, and ethical considerations. Prompt chaining, for example, can be used to break down complex tasks into smaller, more manageable sub-tasks, allowing the agent to focus on each step individually and reducing the risk of errors. Additionally, prompt engineering can be used to incorporate ethical guidelines and safety protocols into the agent's decision-making process (ref_idx 36).

• Case studies demonstrate that prompt engineering significantly influences agent behavior and output quality. By carefully designing prompts, practitioners can shape the agent's reasoning process, control its access to information, and steer it towards desired outcomes. Conversely, poorly designed prompts can lead to suboptimal performance, biased results, or even harmful actions (ref_idx 36).

• The strategic implication is that organizations must prioritize prompt engineering as a critical component of their agentic AI strategy. This involves investing in training and resources to develop expertise in prompt design, establishing clear guidelines and best practices for prompt engineering, and continuously monitoring and evaluating the impact of prompts on agent behavior.

• Recommendations include implementing a systematic approach to prompt engineering, using techniques such as prompt chaining, few-shot learning, and reinforcement learning from human feedback to optimize prompt design. Furthermore, organizations should establish ethical review boards to evaluate prompts for potential biases or unintended consequences, ensuring that agentic AI systems are aligned with human values and ethical principles.

8. Conclusion

• This report has illuminated the transformative potential of research agents, showcasing how the synergistic integration of LLMs with web search and advanced planning mechanisms is reshaping the research landscape. By addressing the challenges of accuracy, ethical alignment, and human oversight, we pave the way for responsible innovation and accelerated discovery.

• The broader context reveals a growing recognition of the need for AI systems that are not only powerful but also reliable, transparent, and aligned with human values. As research agents become increasingly sophisticated, it is crucial to prioritize ethical governance and ensure that these tools are used to promote societal well-being. The insights presented in this report underscore the importance of ongoing research and collaboration to advance the field of agentic AI and address emerging challenges.

• Looking ahead, future research should focus on developing more robust hallucination mitigation techniques, enhancing contextual understanding, and improving the scalability of agentic AI systems. Additionally, further exploration of HCI principles and user-centered design is essential to ensure that these tools are accessible and beneficial to a wide range of users. The key message is that research agents hold immense promise for accelerating discovery and informing decision-making, but their responsible development and deployment require careful consideration of both technical and ethical factors.

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• Agentic AI Integration to Accelerate this Year Among Gen AI Early Adopters: Capgemini Report | Entrepreneurhttps://www.entrepreneur.com/en-in/news-and-trends/agentic-ai-integration-to-accelerate-this-year-among-gen-ai/493576
• Not 007, But a New Kind of AI Agent – Ahead of the Curve® | TD Securitieshttps://www.tdsecurities.com/ca/en/not-007-but-a-new-kind-of-ai-agent
• Agentic AI integration set to accelerate this year among Gen AI early adopters - Capgemini Australiahttps://www.capgemini.com/au-en/news/press-releases/agentic-ai-integration-set-to-accelerate-this-year-among-gen-ai-early-adopters/
• IBM Study: Businesses View AI Agents as Essential, Not Just Expehttps://www.gurufocus.com/news/2918123/ibm-study-businesses-view-ai-agents-as-essential-not-just-experimental-ibm-stock-news
• Business buzzwords: AI-centric terminology dominates today’s lexicon - Digital Journalhttps://www.digitaljournal.com/business/business-buzzwords-ai-centric-terminology-dominates-todays-lexicon/article
• Market Scan Report - By Infosys Responsible AI Officehttps://www.infosys.com/services/data-ai-topaz/insights/responsible-ai.pdf
• 가비아 라이브러리https://library.gabia.com/contents/domain/4359/
• Open RAG Eval: Bringing Science to Enterprise AI Evaluationhttps://medium.com/@yanpcotta/open-rag-eval-bringing-science-to-enterprise-ai-evaluation-4696aefae54c
• Improving Retrieval and RAG with Embedding Model Finetuning | Databricks Bloghttps://www.databricks.com/blog/improving-retrieval-and-rag-embedding-model-finetuning
• Top Evaluation Metrics for RAG Failures | Towards Data Sciencehttps://towardsdatascience.com/top-evaluation-metrics-for-rag-failures-acb27d2a5485/
• Creating Ethical AI Policies: A Guide for Leadershttps://hortoninternational.com/wp-content/uploads/2025/05/Creating-Ethical-AI-Policies-A-Guide-for-Leaders-1.pdf
• EU to Adopt Sweeping Regulation of Artificial Intelligencehttps://www.sullcrom.com/SullivanCromwell/_Assets/PDFs/Memos/EU-Adopt-Sweeping-Regulation-Artificial-Intelligence.pdf
• AI 에이전트가 Web3의 게임 및 엔터테인먼트 업계를 지원하는 방법https://www.coinlive.com/ko/news/how-ai-agents-can-become-the-king-of-web3-gaming
• MILITARY AVIATIONhttps://asma.kglmeridian.com/downloadpdf/view/journals/asem/79/3/article-p244.pdf
• Over Half of South Korean Graduates Say Experience-Focused Hiring Is a Barrier to Employment - Be Korea-savvyhttp://koreabizwire.com/over-half-of-south-korean-graduates-say-experience-focused-hiring-is-a-barrier-to-employment/323307
• Meta offers $100m bonuses to lure OpenAI staff, says Sam Altmanhttps://www.techinasia.com/news/meta-offers-100m-bonuses-to-lure-openai-staff-says-sam-altman
• Gov't Launches Major AI Talent Drive with Postdoc Support Program - Be Korea-savvyhttp://koreabizwire.com/govt-launches-major-ai-talent-drive-with-postdoc-support-program/322229
• Korea opens AI research hub in SeoulView Details | Investment News | InvestKOREA(ENG)https://www.investkorea.org/ik-en/bbs/i-465/detail.do?ntt_sn=492720&clickArea=enmain00019
• 국내 온라인 학술지의 접근성 평가 및 개선에 관한 연구https://www.koreascience.kr/article/JAKO202205148040134.pdf
• Responsible generative AI: Evaluation best practices and toolshttps://reinvent.awsevents.com/content/dam/reinvent/2024/slides/aim/AIM342_Responsible-generative-AI-Evaluation-best-practices-and-tools.pdf
• Building AI for Real Estate: From Design Systems to Scalable Platformshttps://www.netguru.com/blog/building-ai-for-real-estate
• AI is Making Workers Feel More Satisfied – and Stressedhttps://www.eweek.com/news/ai-job-satisfaction-wellbeing-study/
• https://www.nojitter.com/ai-assistants-copilots/slack-workforce-lab-report-finds-desk-workers-use-ai-daily
• Context-Aware Decoding과 Layer별 성능 비교를 통한 대규모 ...http://journal.dcs.or.kr/xml/44923/44923.pdf
• As hallucination problems continue to occur, efforts to reduce them are continuing. OpenAI is workin.. - MKhttps://www.mk.co.kr/en/it/11346339
• AI Hallucinations Are Getting Worse. What Can We Do About It? | Built Inhttps://builtin.com/articles/ai-hallucinations-worsening-solutions
• Study: AI hallucinations limit reliability of foundation modelshttps://www.mobihealthnews.com/news/study-ai-hallucinations-limit-reliability-foundation-models