This lesson shows how a supervisor fans typed work to specialized workers in parallel. Single-agent pipelines waste wall time when research, writing, and summarization can overlap. A shared dispatch path records completions, timing, and speedup for operators. A FastAPI dashboard makes those signals visible after each demo.
Supervisor/worker fan-out with
ThreadPoolExecutor.Typed tasks: research, write, and summarise.
Parallel wall-clock vs serial estimate speedup metrics.
Live dashboard for dispatch, completion, and recent task events.
An
orchestrationpackage: workers, supervisor, metrics store, and service layer.HTTP APIs for
/dispatch,/demo,/metrics,/health, and/dashboard.CLI demo that runs six parallel tasks across three worker specialties.
Docker image, compose file, and lifecycle scripts for run, test, and cleanup.
Day 7 sandboxed untrusted code so agents could execute snippets safely. Day 8 assumes that guardrail and focuses on coordinating multiple agents without serializing every step.
Parallel dispatch is the substrate for a ReAct loop: the planner chooses tools, workers execute in parallel where safe, and the loop merges results before the next thought step.
Where this component sits: between the planner/API gateway and specialized worker agents.
Why it exists: multi-step agent work stalls if every specialty runs one after another.
Problem solved: concurrent typed dispatch with observable speedup and task outcomes.
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