Overview
Direct Answer
A chain of agents is an agentic workflow architecture in which multiple specialised AI agents operate in a predetermined sequence, where each agent receives input from its predecessor, performs a discrete task, and passes structured output to the next agent in the pipeline. This pattern ensures task decomposition and sequential dependency management.
How It Works
Each agent in the sequence specialises in a single domain or capability—such as data extraction, validation, transformation, or reasoning. The first agent receives the initial prompt or data, executes its function, and transfers its output as input to the subsequent agent. Intermediate results flow linearly through the pipeline, with each agent potentially refining, enriching, or translating the data before handoff, until the final agent produces the end result.
Why It Matters
Sequential agent architectures improve accuracy and auditability by isolating concerns and allowing verification at each stage. They reduce hallucination risk compared to monolithic models by breaking complex tasks into verifiable substeps, and they enable organisations to add domain-specific validation, compliance checks, or human-in-the-loop reviews between stages without rebuilding entire systems.
Common Applications
Applications include document processing workflows (extraction → validation → enrichment → storage), customer service scenarios (intent recognition → knowledge retrieval → response generation → quality assurance), and research pipelines (data gathering → summarisation → fact-checking → report generation). Regulatory and financial institutions use such chains for audit trails and compliance verification.
Key Considerations
Sequential execution introduces latency penalties compared to parallel architectures. Error propagation can compound if upstream agents produce degraded output; robust error handling and fallback mechanisms are essential. Defining clear input–output contracts between agents and managing state consistency across stages adds operational complexity.
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