Chain of Agents Pattern for Sequential Task Processing

Large language models excel at individual tasks, but complex workflows often require multiple specialized operations that must execute in sequence. The chain of agents pattern addresses this challenge by coordinating multiple AI agents, each handling a specific stage of a workflow. This approach transforms Claude Code from a single conversational assistant into a powerful orchestration engine capable of executing sophisticated, multi-step processes.

Understanding the Chain of Agents Pattern

The chain of agents pattern structures a workflow as a pipeline where each agent performs a dedicated task and passes its output to the next agent in the sequence. Unlike a single agent handling everything, this pattern enables specialization—each agent can be optimized for its specific function, whether that is data extraction, analysis, transformation, or validation.

This architecture proves particularly valuable for tasks like processing user-submitted content through multiple validation and enhancement stages, analyzing codebases by chaining together understanding, dependency mapping, and reporting agents, or orchestrating document workflows that move through drafting, review, refinement, and publishing phases.

Claude Code’s tool-calling capabilities and skill system provide the foundation for implementing this pattern effectively.

Implementing the Pattern with Claude Code

Defining Agent Responsibilities

The first step involves breaking down your workflow into discrete stages with clear inputs and outputs. For instance, a content moderation pipeline might include an agent that identifies and flags sensitive content, followed by another that categorizes and tags acceptable content, a third that summarizes the content, and a final agent that generates metadata and prepares it for storage.

Each stage should represent a single, well-defined responsibility.

Passing Context Between Agents

Claude Code handles sequential processing naturally through conversation context. When Agent A completes its task, its response becomes part of the shared context that Agent B references. The skill system amplifies this by allowing you to invoke specialized tools at each stage using the get_skill() function to load domain-specific capabilities when needed.

Example: Multi-Stage Code Review Pipeline

Consider implementing a code review workflow with three sequential agents. The first agent performs static analysis to identify potential bugs, the second agent focuses on security vulnerabilities and recommendations, and the third agent compiles a comprehensive review report.

Claude Code orchestrates this pipeline through its conversation context management. Each agent receives the necessary context from previous stages and produces structured output for subsequent agents. The key is designing clear prompts that define each agent’s role and expected output format.

Example: Content Processing Pipeline

A content processing pipeline demonstrates another practical implementation. The first agent extracts and structures raw content from various input sources. The second agent enriches this content with relevant metadata, context, and cross-references. The third agent applies formatting rules and transforms the content into the desired output format. Finally, a validation agent verifies the processed content against predefined schemas and business rules.

This pipeline uses different Claude Code skills at each stage. You might use the docx skill for document processing, the pdf skill for PDF generation, or specialized skills for data extraction and validation. The modular nature of the skill system allows you to assemble the exact capabilities needed for each pipeline stage.

Handling Branching and Parallelization

The chain of agents pattern also supports more complex flow control. You can implement conditional branching where different agents handle different paths based on intermediate results. For example, a document processing pipeline might route technical documents to a code analysis agent while sending marketing content to a tone adjustment agent.

Claude Code’s conversational interface makes this straightforward—you write logic in your prompts that evaluates outputs and determines the next appropriate agent.

Practical Considerations

Several factors determine success when implementing chain of agents workflows in Claude Code.

Context Management: Longer pipelines can exceed token limits. Consider summarizing intermediate results or using a dedicated skill like supermemory to store and retrieve context across stages.

Error Handling: Each agent should validate its inputs and outputs. Build checkpoint logic that catches failures early rather than propagating them through the entire chain.

State Persistence: For complex workflows, use external storage for pipeline state. This enables recovery from interruptions and provides audit trails for debugging.

Token Optimization: The chain of agents pattern consumes more tokens than single-agent approaches because you’re processing input multiple times. Use targeted prompts and focused skill invocations to minimize unnecessary context.

Real-World Applications

The chain of agents pattern enables sophisticated automation scenarios that would be difficult or impossible for a single AI agent to handle.

A software development workflow might chain agents that analyze requirements, generate initial code, run tests, identify failures, and then iteratively refine the code until tests pass. Each agent specializes in its specific task—code generation, test execution, or debugging—and passes quality artifacts forward.

An automated reporting system could chain agents that gather data from multiple sources, perform statistical analysis, generate visualizations, compose narrative sections, and assemble everything into a final report document. The xlsx skill handles data work, while pptx or pdf skills produce the final output.

Customer service automation benefits from chaining agents that classify incoming requests, retrieve relevant knowledge base articles, generate response drafts, apply brand voice guidelines, and queue approved responses for delivery.

Conclusion

The chain of agents pattern transforms Claude Code into a flexible workflow orchestration system. By breaking complex tasks into specialized stages and using Claude Code’s skill system, you can build sophisticated pipelines that combine multiple AI capabilities into coherent, automated processes. Start with simple two-agent chains and progressively add complexity as you become comfortable with the pattern.

The key is treating each agent as a focused specialist, passing clear outputs between stages, and using Claude Code’s conversational context to maintain workflow state. This pattern unlocks automation scenarios that go far beyond what any single AI assistant could achieve alone.

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