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2025

Autonomous Multi-Agent Workflow Orchestrator

Five Claude agents collaborating via typed JSON contracts to produce a stakeholder briefing pack in under 90 seconds.

Year
2025
Stack
6 technologies
Deep-dive
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PythonClaude APIChromaDBStreamlitpydanticTool Use

Highlights

  • 01Complete briefing pack generated from a single prompt in under 90 seconds
  • 02Five specialised agents (researcher, analyst, writer, QA, output) with schema-validated handoffs
  • 03Persistent ChromaDB session memory; only QA-approved outputs are ever embedded

Designed and shipped a production multi-agent orchestration system in Python, where five specialised Claude agents collaborate via typed JSON contracts (pydantic-validated) to complete complex business reporting tasks end-to-end, with retry-and-fallback routing so the pipeline degrades gracefully rather than failing silently.

Orchestrated tool use across agents using the Claude API: web search, PDF ingestion, structured data extraction, and a citation tracker that registers every source and rejects any finding referencing an unregistered citation id. Hallucinated sources stopped being a class of bug.

Built a real-time Streamlit execution dashboard that streams every inter-agent JSON message as a colour-coded card, so non-technical operators can see exactly which agent is on the critical path of a run. Persistent ChromaDB memory per stakeholder lets the researcher skip work already verified in previous runs.

Related essay

Five Agents in a Trench Coat: What I Learned Building a Production Multi-Agent Orchestrator

A walk through the autonomous multi-agent system I shipped on top of Claude, where a researcher, analyst, writer, QA, and output agent collaborate via typed JSON contracts to turn one prompt into a full briefing pack in under ninety seconds.

14 min · Read the deep-dive

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RAG Knowledge Agent with Persistent Memory