Agentic Broad Information-Seeking

• Agentic broad information-seeking is a paradigm where autonomous agents iteratively transition through dynamic information states using context-aware actions.
• It leverages LLM-driven reasoning, multi-turn interactions, and integrated tool use to decompose queries and synthesize complex information.
• This approach shows promise in fields like financial analytics, network diagnostics, and data synthesis while highlighting challenges in query decomposition and evidence attribution.

Agentic broad information-seeking is the paradigm in which autonomous AI agents or systems—most notably those leveraging LLMs—pursue large-scale, multi-faceted, and dynamically evolving information collection and synthesis tasks. Unlike traditional IR systems that statically retrieve documents from fixed corpora, agentic broad information-seeking emphasizes iterative, multi-turn interaction and active, context-dependent state transformation to achieve rich, contextually aligned information goals. This paradigm shift is enabled by LLM-driven reasoning, tool use, memory integration, external environment interaction, and multi-agent orchestration, broadening the horizon of what is possible in practical and research-level information retrieval.

1. Conceptual Foundations and Evolution

Agentic broad information-seeking departs from classical IR by abandoning the notion of static relevance ranking over a pre-defined corpus. Traditional IR architectures rely on one-step filters (e.g., inverted indexes, TF-IDF, BM25, or semantic retrievers) to return document sets based on fixed queries. In contrast, agentic IR systems define “information” as a dynamic information state $s$, encompassing not just retrieved items but also user preferences, environmental context, history, and ongoing decision processes (Zhang et al., 13 Oct 2024). The objective is to iteratively transition from an initial state $s_0$ toward a target state $s^*$ via a sequence of agent-guided actions, culminating when a predefined verifier $r(s^*, s_T)$ is satisfied.

Agentic broad information-seeking thereby generalizes IR to an environment- and context-driven process supported by observation–reasoning–action recurrences in agent architectures. This represents a fundamental evolution from static, reactive retrieval systems to goal-driven, adaptive, and proactive agents.

2. Task Formulation, Architectures, and Methodological Approaches

Agentic information-seeking is formalized as a sequential decision process. At each time step $t$, an agent determines an action $a_t \sim \pi(\cdot\mid x(s_t))$ given its current textual state representation $x(s_t) = g(s_t, h_t, Mem, Tht, Tool)$. The full trajectory evolves according to $s_{t+1} \sim p(\cdot\mid s_t, a_t)$, with the objective:

$$\operatorname{maximize}_\pi\quad \mathbb{E}_{s^*}\left[ r(s^*, s_T) \right] \quad \text{subject to}~ s_{t+1} \sim p(\cdot\mid s_t, a_t),~ a_t \sim \pi(\cdot\mid x(s_t))$$

Architectural elements are unified around three core modules:

• Memory (Mem): Persisting history and previously observed evidence in structured or unstructured form.
• Thought (Tht): Internal reasoning, updated in the LLM context and often represented as an explicit chain-of-thought trace, recursive prompt, or state vector.
• Tools (Tool): External interfaces enabling web search, code execution, access to APIs, or structured database queries.

Advanced systems may employ multi-agent orchestration, as in AgenticIR frameworks with task decomposition and collaborative agent modules (Tian et al., 19 Apr 2025). Such frameworks support both collaborative and hierarchical decomposition, promoting granular, aspect-targeted search, retrieval, and synthesis.

3. Evaluation, Benchmarks, and Performance Challenges

Recent benchmarks, such as WideSearch (Wong et al., 11 Aug 2025), InfoDeepSeek (Xi et al., 21 May 2025), and Mind2Web 2 (Gou et al., 26 Jun 2025), expose the formidable demands of broad information-seeking:

• WideSearch emphasizes “wide-context” collection: agents must assemble comprehensive, atomic-scale tabular information across hundreds of entities and attributes. Only perfect, all-cell-complete tables are scored as successful, resulting in near 0% success for most state-of-the-art agents (with best systems at 5.1%; humans at 20% on first try).
• InfoDeepSeek focuses on dynamic, multi-hop querying in live web environments, with metrics such as Answer Accuracy (ACC), Information Accuracy at top-k (IA@k), Effective Evidence Utilization (EEU), and Information Compactness (IC). State-of-the-art LLM agents reach just 22–23% ACC on challenging dynamic tasks.
• Mind2Web 2 tasks demand long-horizon, real-time browsing and synthesis with rigorous, tree-structured rubric evaluation. The best agentic systems achieve 50–70% of human-level performance while halving task completion time.

Experimental findings consistently indicate that current agents struggle with complete query decomposition, fail to reliably reflect on errors or re-plan, and often exhibit suboptimal evidence utilization and attribution.

4. Agentic Reasoning, Multi-step Planning, and Tool Use

Broad information-seeking requires sophisticated agentic reasoning, integrating planning, reflection, and tool interaction. Modern paradigms leverage:

• Chain-of-thought and Hierarchical Decomposition: Agent plans are framed as multi-step reasoning graphs, decomposing high-level questions into sub-questions, each linked to tool invocations or further retrieval (Zhang et al., 13 Oct 2024, Tian et al., 19 Apr 2025).
• Self-Reflection and Self-Verification: Systems iteratively refine their outputs, identifying incomplete evidence or inconsistencies, and re-invoking retrieval or synthesis as needed (Zhang et al., 24 Feb 2025, Xi et al., 21 May 2025).
• External Tool Integration: Agentic systems increasingly incorporate search engine querying, structured database access, document retrieval (vector search, knowledge graphs), and even feedback-driven code execution (Zhang et al., 24 Feb 2025, Wong et al., 11 Aug 2025).
• Memory and State Management: Persistent memory modules—tracking previous search attempts, user preference drift, and verified facts—are essential for avoiding redundant actions and enabling cross-turn reasoning (Zhang et al., 13 Oct 2024, Sun et al., 7 Aug 2025).

Reinforcement learning and confidence-calibrated search (e.g., β-GRPO (Wu et al., 22 May 2025)) further enable agents to self-regulate when to engage in retrieval, reducing both over- and under-search.

5. Practical Applications and Case Study Domains

Agentic broad information-seeking has demonstrated utility across heterogeneous domains:

• Life, Business, and Coding Assistants: Agents anticipate user needs, decompose and resolve complex queries, integrate multi-type evidence (documents, code, context), and maintain updated user or code “states” (Zhang et al., 13 Oct 2024).
• Telecommunications Networks: Multi-hop and multi-source agentic retrieval frameworks enhance decision-making in dynamic network management, planning, and diagnostics, with experimental improvements in accuracy and explanation consistency (Zhang et al., 24 Feb 2025).
• Financial Analytics: AgenticIR and DecomposedIR enable multi-section template report generation with granular coverage and modular prompt chaining, outperforming monolithic approaches on both accuracy and domain fidelity (Tian et al., 19 Apr 2025).
• Data Analytics: AgenticData (Sun et al., 7 Aug 2025) frameworks allow natural language query-driven analytics on heterogeneous data, with agentic collaboration between profiling, planning, validation, and memory modules.
• Large-scale Tabular Synthesis and Knowledge Base Population: WideSearch tasks highlight the need for robust agentic strategies in wide-context, attribute-rich collection—crucial for professional research and business intelligence.

6. Persistent Challenges, Limitations, and Future Directions

Despite recent advances, agentic broad information-seeking remains hampered by:

• Insufficient Query Decomposition and Planning: Agents frequently omit necessary sub-queries or lack adaptive task planning for wide-context, multi-attribute challenges (Wong et al., 11 Aug 2025).
• Deficient Reflection and Adjustment: Agents rarely diagnose retrieval failures and fail to replan accordingly, leading to incomplete or erroneous outputs (Wong et al., 11 Aug 2025, Xi et al., 21 May 2025).
• Ineffective Evidence Attribution and Compactness: Systems sometimes misattribute or redundantly cite evidence, resulting in low information compactness and credibility issues (Xi et al., 21 May 2025, Gou et al., 26 Jun 2025).
• Scalability, Computation, and Latency: Performing broad, multi-turn, multi-tool information-seeking at scale is computationally intensive, with significant inference costs (Zhang et al., 13 Oct 2024).
• Safety, Fairness, and Normative Alignment: Ensuring safe transitions, bias detection, and compliance with social/ethical norms are open problems, as outlined in typological and bias-aware agent frameworks (Singh et al., 27 Mar 2025, Wissuchek et al., 7 Jul 2025).
• Benchmark-Exposed Gaps: State-of-the-art systems exhibit near-zero success rates on all-or-nothing tabular benchmarks, with human-level performance still out of reach on many broad tasks (Wong et al., 11 Aug 2025).

Anticipated future directions include refinement of multi-agent frameworks, enhanced hierarchical planning, agentic resource allocation (dynamic computational scaling), improved memory and reflection modules, standardized large-scale evaluation protocols, and tighter integration of feedback/revision loops. Safety protocols, regulatory alignment, and data-centric training (e.g., formalization-driven synthesis (Tao et al., 20 Jul 2025)) will also be essential for trustworthy deployment.

7. Position in the Broader AI Landscape

Agentic broad information-seeking underpins the transition from static, retrieval-focused systems to fully autonomous, interactive, and contextually reasoning agents. It forms the backbone of prospective developments in generalist AI agents—those capable of synthesizing and acting across diverse information environments. These paradigms are integral to future scenarios such as agentic economies (reconfiguring market and social dynamics (Rothschild et al., 21 May 2025)), generalizable autonomous research, and high-reliability data analytics.

By reconceptualizing information seeking as a journey over complex, dynamic informational states, and by developing architectural and evaluative tools to operationalize this vision, agentic broad information-seeking is set to define the next era of information retrieval research and deployment.