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Documentation Index

Fetch the complete documentation index at: https://docs.mixus.ai/llms.txt

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Break big goals into tiny, specialised agents that work together. Each agent does one clear job, then passes the results to the next agent in line. You get sharper answers, faster runs, and simpler approvals. ![Illustration – three linked agents passing a baton – PLACEHOLDER]

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Why chain micro-agents?

  • Higher quality – Smaller prompts focus the AI on one task at a time.
  • Lower cost – Each agent uses less context, so you pay fewer tokens.
  • Targeted approval – Different teammates can approve only the steps they own.
  • Easy retries – If one agent fails you rerun just that piece, not the whole flow.

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How it works

  1. Create Agent A for task 1 (for example, Research). Its last step says: “Run Agent B with the findings.”
  2. Create Agent B for task 2 (for example, Analyse). Its last step runs Agent C.
  3. Create Agent C for task 3 (for example, Report).
  4. Each run starts a new chat. Results flow downstream automatically.
  5. Use the chat history search tool to collect results later and build dashboards, digests, or reports.

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Real-world examples

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Research → analyze → report

  • Sarah verifies the research agent.
  • Jack verifies the final report.
  • The analyze agent needs no approval — it runs automatically.

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Hourly KPI monitor

  • A “metrics-collector” agent runs every hour.
  • A “performance-review” agent (scheduled daily) searches the collector’s chats, crunches numbers, and emails a daily digest.

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Marketing content pipeline

  • Idea Agent → Draft Agent → Design Agent → Publish Agent
  • Different team members approve their own stage only.

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Code quality loop

  • Static-analysis agent leaves comments → Unit-test agent checks coverage → PR-comment agent posts a summary.

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Setting it up in mixus

  1. Keep each agent short – Aim for 1-3 steps.
  2. Last step = hand-off – Say “Run <next-agent-name> with the output of this step.”
  3. Use clear names – Downstream agents are found by name search.
  4. Add human checks only where needed – Use the verification toggle per step.
  5. Analyse later – Create another agent that queries chats with the search chat history tool.

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Tips & best practices

  • Test each micro-agent on its own before chaining.
  • Keep the passed context concise – just the output, not the whole chat.
  • Watch your daily execution limits if you schedule many agents.
  • Use scheduling to pace chains (e.g., run Analyze 10 mins after Research finishes).
  • Combine with integrations – every micro-agent can call any of your connected services.

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Relationship to Agent Collaboration

Micro-agent chaining is one specific pattern within the broader concept of Agent Collaboration. While agent collaboration covers various approaches to multi-agent workflows, micro-agent chaining focuses specifically on sequential workflows where small, focused agents pass work to the next agent in a chain. Key differences:
  • Micro-Agent Chaining: Sequential, linear workflows with clear handoffs
  • Agent Collaboration: Includes hierarchical, peer-to-peer, and dynamic collaboration patterns
When to use micro-agent chaining vs other collaboration patterns:
  • Use micro-agent chaining for: Linear processes, step-by-step workflows, simple verification chains
  • Use broader collaboration for: Complex coordination, consensus-building, parallel processing, dynamic task allocation

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Comprehensive Guides

For advanced micro-agent patterns and implementation details, explore these in-depth guides:

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Core Concepts

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Implementation Guides

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Advanced Applications

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What’s next?

Weʼre adding tools to read execution logs directly, so youʼll soon be able to chart performance without using chat search. Until then, micro-agent chaining plus chat-history search already unlocks surprisingly advanced workflows. Need inspiration? Check the agent templates gallery or ask our support team for examples tailored to your use case.