Multi-Agent Pipeline for Scalable Task Solving

• Multi-Agent Pipeline is a structured computational framework that decomposes complex tasks into modular operations executed by specialized agents.
• It integrates functions like candidate generation, expert reasoning, and post-processing to enhance scalability, interpretability, and overall performance.
• Empirical studies, such as those using MAPEX, demonstrate measurable performance gains and robust generalization across keyphrase extraction and reasoning tasks.

A multi-agent pipeline is a structured computational architecture that coordinates multiple specialized agents—often instantiated as LLMs, deterministic code modules, or hybrid systems—in a serial or parallel configuration to solve complex tasks that are beyond the scope of a single monolithic model. Unlike single-stage methods, a multi-agent pipeline decomposes the workflow into atomic modules, each responsible for a discrete operation such as candidate generation, semantic filtering, expert reasoning, post-processing, or interaction with external knowledge sources. This decomposition enables enhanced flexibility, modularity, scalable collaboration, and can leverage both agent specialization and inter-agent communication strategies to yield superior generalization, interpretability, and efficiency. Empirical studies demonstrate significant gains from multi-agent architectures in domains such as keyphrase extraction (Zhang et al., 23 Sep 2025), multi-modal reasoning, path planning, AutoML, data visualization, and software verification.

1. Architectural Principles and Agent Roles

The design of a multi-agent pipeline hinges on precise module boundaries and inter-agent communication protocols. In paradigmatic systems such as MAPEX (Zhang et al., 23 Sep 2025), agent roles are defined as follows:

• Expert Recruitment: Assigns an application-specific expert role to subsequent reasoning agents, tailored to input context. Example: for a chemistry text, the role may be "computational chemistry expert."
• Candidate Extraction: Implements a domain-neutral agent responsible for broad candidate generation (e.g., keyphrase set $C$), typically using a generic LLM prompt.
• Domain Expert: Performs path-specific semantic filtering and ranking tasks—leveraging either knowledge augmentation or topic-guidance—conditioned on both the expert role and intermediate outputs.
• Post-processing: Applies rule-based cleaning, normalization, and redundancy removal to eliminate noise and output a finalized result set.

This modularization allows for adaptive routing of data between agents and supports soft model specialization. In complex pipelines, additional agents may be introduced for monitoring, orchestration, semantic validation, tool integration, or human-in-the-loop feedback (see (Wolter et al., 30 Aug 2025, Qiang et al., 8 Oct 2025, Chakravarty et al., 20 Jun 2025)).

2. Adaptive Routing and Dual-Path Strategies

Multi-agent pipelines frequently embed conditional routing logic to partition the workflow. In MAPEX (Zhang et al., 23 Sep 2025), document length $|d|$ determines the route:

$$g(d) = \begin{cases} 1, & |d| < \ell \quad (\text{knowledge path})\ 0, & |d| \geq \ell \quad (\text{topic path}) \end{cases}$$

with $\ell=512$. Short documents trigger knowledge-driven extraction (candidate set $C$ aggregated with external definitions $W$), whereas long documents invoke topic-guided extraction (core topic set $T$ extracted, then used to filter $C$ by semantic proximity).

This type of routing enables dynamic adaptation to input complexity, resource budgets, or situational demands—foundational for pipelines targeting variable-length inputs, multi-modal data, or agent-driven task delegation.

3. Inter-Agent Communication, Prompt Design, and Pseudocode

Robust inter-agent handoff is achieved through structured messaging (often JSON, Pydantic models) and schema-enforced data contracts. Key aspects include:

• Prompt Specialization: Each agent is invoked with a tailored system prompt, user message, and intermediate artifacts. For example, the MAPEX Candidate Extractor employs the prompt: "You are a neutral Keyphrase Extractor. Provide a list of all potential keyphrases (single- and multi-word) in the text below."
• Pseudocode Example (MAPEX Algorithm 1):

(r, j) ← ExpertRecruiterAgent.prompt(d) C ← CandidateExtractorAgent.prompt(d) if |d| < ℓ: W = ∪_{c∈C} WikiQuery(c) K̂ ← DomainExpertAgent.prompt(role=r, context=W, candidates=C) else: T ← DomainExpertAgent.prompt(role=r, context=d, task="extract-topics") K̂ ← DomainExpertAgent.prompt(role=r, context=T, candidates=C) K* ← PostProcessor(K̂) return K*

Strict typing and modular outputs facilitate auditing, debugging, error isolation, and "surgical" updates to intermediate results without end-to-end regeneration (Wolter et al., 30 Aug 2025).

4. Quantitative Evaluation and Ablation Analysis

Multi-agent pipelines demonstrate empirically validated improvements over single-agent and baseline LLM approaches. Notable findings include:

Method	Inspec	Sem2017	Sem2010	DUC2001	NUS	Krapivin	AVG
PromptRank (T5)	31.73	27.14	17.24	27.39	17.24	16.11	22.81
Base (Qwen2.5-7B)	35.23	22.56	16.74	25.26	20.88	18.29	23.16
MAPEX (Qwen2.5-7B)	35.43	24.14	16.44	25.81	22.46	21.54	24.30

• Average F1@5 gain vs. PromptRank: +2.44 points.
• Average gain vs. LLM base: +4.01 points (Zhang et al., 23 Sep 2025).

Ablation studies confirm additive benefit from each module: expert role yields incremental improvements, dual-path strategies overcome semantic dilution and improve context grounding, and post-processing reduces noise (+0.5–1.0 points). Length-threshold selection (based on log-length crossovers) justifies parameterization.

5. Generalization, Universality, and Cross-Domain Applicability

Multi-agent pipeline frameworks demonstrate robust generalization across datasets, domains, and LLM backbones (Zhang et al., 23 Sep 2025). Evaluation on six publicly available keyphrase datasets (Inspec, SemEval-2010/2017, DUC2001, NUS, Krapivin) confirms strong universality. Cross-backbone performance is validated using Mistral-7B-Instruct, Qwen2-7B, and Qwen2.5-7B.

Analysis of module contribution and length-adaptive routing verifies that the multi-agent scheme can be ported to other extraction, reasoning, and analytic tasks. Modular agent design, externalization of deterministic heuristics, and schema-enforced outputs are explicitly cited as key enablers of transparent extension and maintainability (Wolter et al., 30 Aug 2025).

6. Limitations and Open Challenges

Known constraints include:

• Routing depends critically on correct length-threshold selection (crossover at $\ell=512$ for MAPEX (Zhang et al., 23 Sep 2025)); domain-specific calibration may be required for other pipelines.
• Certain tasks (e.g., rare semantic relations, highly technical jargon) may challenge the context integration and ranking capabilities of agents.
• The inherently serial nature of multi-agent pipelines can yield latency increases proportional to agent count; parallelization must balance against communication overhead.
• While generalization is empirically strong, transfer to non-text modalities or tasks requiring deep external world knowledge demands additional agent development or tool coupling.

7. Impact and Future Directions

Multi-agent pipelines are advancing state-of-the-art performance and reliability across an array of tasks, including natural language processing, data visualization, reasoning, and autonomous system design. The formalization of module interfaces, adaptive routing via dual-path or branching strategies, thorough ablation, and cross-domain benchmarking set methodological standards for future research.

Emerging trends and recommendations include:

• Design pipelines with clear agent roles and strict I/O contracts.
• Instrument agent handoff for traceability and error diagnosis.
• Apply dynamic routing conditioned on task features (length, modality, complexity).
• Consider external knowledge, topic-guided filtering, and post-processing to reduce hallucination and improve domain alignment.

Continued work is anticipated in the integration of tool-supported agents, dynamic graph-based topologies, and multi-modal reasoning modules. These developments are expected to enhance the universality and utility of multi-agent pipelines in increasingly complex, heterogeneous environments.