Agent-Based Search Architectures

Updated 18 November 2025

Agent-Based Search Architectures are modular frameworks where autonomous agents coordinate to optimize search, decision-making, and information retrieval tasks.
They employ diverse methodologies including RL-based orchestration, graph/DAG workflow planning, and market-based protocols to balance exploration, cost, and resource management.
Applications span web search, medical diagnosis, game testing, and robotic exploration, demonstrating scalability, adaptability, and practical performance improvements.

Agent-based search architectures comprise a diverse set of computational frameworks in which autonomous components—agents—coordinate to solve information retrieval, optimization, or decision-making tasks. Each agent typically perceives its environment, maintains internal state, and selects actions aimed at discovering solutions that maximize task-specific objectives. These architectures leverage modularity, decentralization, and collaboration to address challenges in scalability, adaptability, cost efficiency, and robustness across advanced search domains such as instant search, retrieval-augmented generation, neural architecture search, large-scale agentic web ranking, deep multi-step reasoning, and cooperative optimization.

1. Core Architectural Components and Paradigms

Agent-based search systems exhibit a high degree of heterogeneity in agent granularity, inter-agent protocols, and task decomposition. Key architectural motifs include:

Monolithic RL-Based Search Orchestrators: Single reinforcement learning (RL) agents mediate interaction between user input and a black-box backend, learning policies for triggering or suppressing search operations to optimize resource use; e.g., Deep Q-Network agents for selective instant search (Arora et al., 2022).
Explicit Modular Multi-Agent Systems: Distinct agents are assigned specialized search, planning, reasoning, memory, or tool-invocation tasks, forming hierarchical or peer-to-peer assemblies with uniform interfaces, as in ManuSearch (Huang et al., 23 May 2025), TURA (Zhao et al., 6 Aug 2025), and AI Search Paradigm (Li et al., 20 Jun 2025).
Graph- or DAG-Based Workflow Search: Workflow-level agent search spaces are modeled as directed graphs where nodes encapsulate agent modules (prompts, tools, control subroutines) and edges denote data or control flow. Automated design methodologies search over these workflow graphs using structural optimization, including node-level, control-level, and framework-level changes as seen in medical agent workflow optimization (Zhuang et al., 15 Apr 2025).
Market-Based and Swarm Architectures: Sub-agents represent different specializations or search “goods” and negotiate resource allocation, division of labor, and bid-based action selection in internal economic environments (Sudhir et al., 5 Mar 2025).
Automated Multi-Agent Architecture Search: Instead of seeking a single best agentic design, recent frameworks (MaAS (Zhang et al., 6 Feb 2025), AgentSquare (Shang et al., 8 Oct 2024)) learn probabilistic distributions over modular agentic compositions, efficiently sampling query-dependent agent subnets for each input while trading off performance and cost.

2. Agent Coordination, Communication, and Decision Processes

Agent-based search architectures employ diverse mechanisms for agent coordination:

Centralized vs. Decentralized Control: Architectures vary from fully centralized controllers (RL-based instant search agents (Arora et al., 2022)) to strongly decentralized frameworks where agents asynchronously share information, e.g., as in decentralized multi-agent active search with Thompson sampling (Ghods et al., 2020) or via asynchronous message passing with limited information exchange (CAST (Banerjee et al., 2022)).
Workflow Planning and Task Allocation: Many agentic pipelines explicitly decompose complex queries into sub-queries or subtasks, constructing DAGs of dependencies (TURA (Zhao et al., 6 Aug 2025), AI Search Paradigm (Li et al., 20 Jun 2025)). Assignment of subtasks to agents or tools is mediated via static rules, LLM-based planning, or automated graph search over modular workflow spaces (Zhuang et al., 15 Apr 2025).
Collaborative Reasoning and Evidence Aggregation: Systems such as ManuSearch (Huang et al., 23 May 2025) couple iterative task planning (SPA), web evidence collection (ISA), and evidence extraction (WRA), with channels for sub-question passing, evidence aggregation, and solution synthesis.
Swarm and Market Protocols: In multi-agent optimization on fitness landscapes (NK/NKZE models), agents blend individual exploration with guided imitation or group-memory—regulating the flow of best-so-far policies (StealthL/StructC (Lim et al., 2022)), while market-based RL (Sudhir et al., 5 Mar 2025) utilizes bid, price, and wealth dynamics to allocate search effort and adaptively specialize agents.

3. Learning and Adaptation in Agent-Based Search

Learning mechanisms in agent-based architectures encompass:

Deep Reinforcement Learning: End-to-end optimization of search/email policies via deep Q-networks (Bi-LSTM or Transformer-based) is central to systems such as instant search click suppression (Arora et al., 2022) and RL-based Neural Architecture Search (NAS) (Cassimon et al., 2 Oct 2024).
- MDP formulations define state (e.g., token prefixes/suffixes for instant search; graph-based architectures for NAS), actions (WAIT/SEARCH, architecture edits), and reward signals (e.g., MAP improvements, accuracy gains).
Bandit/Online Learning and Structural Optimization: MANAS frames NAS as a multi-agent adversarial bandit problem, where each agent controls an architectural decision (edge operation selection), updating (EXP3 or least-squares) policies based on shared validation reward. Cumulative regret bounds of O(√T) are proven (Lopes et al., 2019).
Automated Workflow Search: Architectures such as those in medical diagnosis (Zhuang et al., 15 Apr 2025) and MaAS (Zhang et al., 6 Feb 2025) search over workflow graphs or agentic supernets using operators at node, structural, and reasoning paradigm levels, optimizing for accuracy, resource cost, and generalization.
- Iterative Self-Improvement: LLM-based meta-agents diagnose errors, propose edits, and select among structural and prompt-level alternatives using bandit-like or evolutionary search loops.
Distillation and Surrogate Learning: Distilled agent executors (TURA (Zhao et al., 6 Aug 2025)) are trained from expert LLM trajectories, curating and fine-tuning smaller agents for real-time tool-calling with substantial latency reductions.
Preference- and Feedback-Driven Training: Architecture components are aligned and hardened using RL from human behavior, direct preference optimization, and adversarial robustness training (e.g., Writer in AI Search Paradigm (Li et al., 20 Jun 2025)).

4. Evaluation Methodologies and Benchmarks

Agent-based search frameworks are evaluated using both synthetic and real-world metrics:

Retrieval and Ranking Metrics: Instant search agents are assessed via triggered search rate (TS), user effort (keystrokes to target MAP), and overall MAP—as compared with per-token and last-token baseline strategies (Arora et al., 2022).
Task Success and Latency: Large-scale search agents (TURA) report accuracy, recall@K, precision@K across retrieved tool servers, task latency per DAG plan, and tool-calling correctness as compared to baseline RAG and LLM systems (Zhao et al., 6 Aug 2025).
Benchmarks for Multi-Agent Reasoning: ManuSearch introduces the ORION dataset, emphasizing open-web reasoning on long-tail entities, requiring complex fact, numerical, and temporal reasoning, and provides accuracy comparisons against both open-source and proprietary systems (Huang et al., 23 May 2025).
Optimization and Consensus Metrics: Cooperative fitness landscape search measures group best-so-far and average agent fitness, adaptation speed, and error rates as functions of group composition, ruggedness (K), landscape malleability (E), and shaper fraction β (Lim et al., 2022).
Cost and Resource Efficiency: MaAS quantifies performance in terms of both solution quality (accuracy, pass@1) and cost (LLM call tokens, API/concurrency cost), with ablation for different controller designs and architecture depths (Zhang et al., 6 Feb 2025).
Automated Search Scientific Benchmarks: RL-based NAS agents are evaluated on NAS-Bench-101/301, with measures including test accuracy per query budget, wall-clock training time, scalability to search-space size, and robustness to hyperparameters (Cassimon et al., 2 Oct 2024, Lopes et al., 2019).

5. Applications and State-of-the-Art Deployments

Agent-based search architectures underpin systems in multiple domains:

Web and Conversational Search: DQN-powered instant search (reducing backend load by 45–74% with minimal effort increase) (Arora et al., 2022), scalable multi-agent RAG systems (TURA, serving tens of millions of queries per day, yielding +8.9% SSR over LLM+RAG baselines) (Zhao et al., 6 Aug 2025), and multi-stage LLM tool pipelines (AI Search Paradigm) (Li et al., 20 Jun 2025).
Structured Reasoning and Evidence Synthesis: Transparent, modular agents with traceable reasoning steps and extensible tool registries (ManuSearch (Huang et al., 23 May 2025)).
Medical Diagnosis and Workflow Automation: Hierarchical workflow search enabling adaptation and performance improvements over chain-of-thought and multi-agent roundtable prompting in skin lesion classification (Zhuang et al., 15 Apr 2025).
Game Testing and Search Planning: Online agent-based search for automated game verification exploits EFSM model construction and symbolic reasoning for efficient task success in maze-like environments (Shirzadehhajimahmood et al., 2022).
Decentralized Robotic and Active Search: Multi-agent asynchronous Thompson sampling and advanced lookahead (TS+MCTS+LCB front) enable cost-aware search under communication and computation constraints (Banerjee et al., 2022, Ghods et al., 2020).
Agentic Web and Ecosystem Ranking: DOVIS protocol + AgentRank-UC fuses usage/competence telemetry for trust-aware, sybil-resistant agent ranking on the open internet, providing monotonicity, cold-start fairness, and linear convergence guarantees in large-scale simulations (Krishnamachari et al., 5 Sep 2025).

6. Design Principles, Best Practices, and Limitations

Documented insights into agent-based search architectures include:

Exploration–Exploitation Balancing: Over-exploitation (e.g., excessive imitation, large groupthink) degrades solution quality (Reia et al., 2018, Lim et al., 2022); optimal performance typically requires calibrated reconstruction or imitation probability, memory-based exploitation throttled over time (epsilon decay), and controlled information flow (e.g., limits in blackboard capacity).
Workflow Modularity: Modular search spaces (AgentSquare (Shang et al., 8 Oct 2024), MaAS (Zhang et al., 6 Feb 2025)) with plug-and-play modules in planning, reasoning, tool, and memory dimensions—support transferability and efficient search for query-adaptive architectures.
Scalability and Communication Efficiency: Sparse, decentralized update protocols (asynchronous TS, Pareto-LCB) offer linear scaling with agent count and robust performance under communication loss (Banerjee et al., 2022, Ghods et al., 2020).
Transparency and Traceability: Clear separation, logging, and ablation of agent behaviors—combined with explainable module design—are beneficial for diagnosis and iterative improvement (see ManuSearch, AgentSquare).
Limitations and Gaps: Some architectures lack formal performance guarantees, rely on manually tuned heuristics, or do not provide empirical validation (e.g., semantic agent frameworks predating modern LLMs (Ahmed et al., 2010)). Robustness to hyperparameter changes remains challenging in RL-based NAS (Cassimon et al., 2 Oct 2024).
Resource and Cost Management: Multi-agent architecture search explicitly incorporates inference and compute cost into its optimization objectives (e.g., MaAS), providing sample-efficient, cost-minimizing agentic subnet deployment (Zhang et al., 6 Feb 2025).

7. Future Directions and Research Frontiers

Advancements in agent-based search are expected along several axes:

Automated Agent Design and Supernet Sampling: Efforts such as MaAS and AgentSquare point to the future wherein optimal agentic architectures are not statically designed but sampled on demand from finely controlled modular or probabilistic supernets.
Integration with Advanced Preference Modeling and Feedback: Further work on aligning agentic workflows with real user feedback, adversarial robustness, and direct preference optimization is ongoing in multi-agent RAG and writing modules (Li et al., 20 Jun 2025).
Federated Agentic Web and Trust Infrastructure: Transparent, privacy-preserving telemetry and dynamic competence-aware agent ranking (DOVIS/AgentRank-UC) are foundational for next-generation open internet ecosystems (Krishnamachari et al., 5 Sep 2025).
Domain-Generalization and Transferability: Modular agent search and evolutionary workflow optimization frameworks demonstrate transferability across LLM backbones, datasets, and domains—enabling rapid adaptation.
Hybrid Market and Neural Reasoning Models: Emerging work (market-based RL, reasoning markets) explores the full generality of agent-based search paradigms by unifying neural, symbolic, and economic computation into unified architectures for scalable, robust intelligent systems (Sudhir et al., 5 Mar 2025).

In summary, agent-based search architectures define the state of the art for modular, adaptive, and scalable search and reasoning systems, spanning granular RL orchestrators, transparent modular multi-agent frameworks, decentralized cooperative swarms, automated workflow search, and web-scale agentic ranking substrates. Their ongoing evolution is rapidly transforming information retrieval and decision-making across technical domains.