Overview
Direct Answer
Agent chaining is an architectural pattern in which multiple specialised AI agents are orchestrated to execute sequentially, with each agent's output serving as direct input to the next stage, creating deterministic workflows for complex problem-solving. This differs from single-agent systems by decomposing tasks into discrete, modular steps executed by agents optimised for specific domains or functions.
How It Works
In a chaining architecture, an orchestrator manages task routing and state propagation between agents. Agent A completes its designated function and passes structured output to Agent B, which validates, transforms, or extends the result before forwarding to Agent C. Control flow is typically linear or conditionally branched, with each step waiting for completion before advancing, ensuring dependencies are resolved and data integrity is maintained across stages.
Why It Matters
Organisations deploy this pattern to reduce latency and error rates in knowledge-intensive workflows by leveraging specialised capabilities—legal review agents, data validation agents, and synthesis agents working in tandem. Chaining enables explainability and auditability by creating clear handoff points, critical for regulated industries, whilst distributing computational load and improving failure isolation compared to monolithic agent designs.
Common Applications
Document processing pipelines extract information, validate compliance, and generate summaries sequentially. Research workflows combine information retrieval, synthesis, and fact-checking stages. Customer support systems route inquiries through intent classification, knowledge retrieval, and response generation agents. Software development workflows employ chaining for requirements analysis, code generation, testing, and documentation stages.
Key Considerations
Latency compounds across stages, potentially making chaining unsuitable for real-time applications. Error propagation can cascade; failures in early agents compromise downstream execution, necessitating robust exception handling and rollback mechanisms. State management complexity increases with chain length, requiring careful schema definition at handoff points.
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