Learn orchestration patterns for routing, sequencing, parallel work, supervisor-worker systems, shared state, retries and observability.
Complete 12 of 24 practices (50%) and enter your name to unlock the Certificate of Participation.
Orchestration coordinates specialized steps, models or agents when one monolithic loop becomes hard to control or inefficient.
Break work apart only when the task structure justifies it.
workflow = route(task)
result = workflow.run(task)Orchestration should simplify control of complex work, not create complexity for its own sake.
Sequential orchestration passes the output of one stage into the next when order and dependency are clear.
A pipeline is often safer than a free-form multi-agent conversation.
a = extract(doc)
b = validate(a)
c = summarize(b)Use sequential flows for tasks with clear dependencies and stable contracts.
A router chooses the most appropriate workflow, toolset or specialist based on the request.
Routing is useful when task classes genuinely need different capabilities.
route = classifier(request)
handler = ROUTES[route]A router should be evaluated for routing accuracy, not only final answer quality.
Independent subtasks can run concurrently to reduce latency or gather diverse evidence.
Parallelism helps only when branches are sufficiently independent.
results = await gather(search_a(), search_b(), calc())Parallel orchestration needs a merge policy and failure handling for partial results.
A supervisor can decompose a task, assign bounded work to specialists and synthesize results.
The supervisor should coordinate, not grant unlimited autonomy to every worker.
tasks = supervisor.plan(goal)
results = [worker.run(t) for t in tasks]Use supervisor-worker when decomposition is dynamic and specialists have distinct bounded roles.
Orchestrated workflows need a shared representation of goals, artifacts, completed work and unresolved issues.
Handoffs fail when context is implicit or inconsistent.
state = {'goal':goal,'artifacts':{},'open_items':[]}Shared state should be explicit enough that any stage can explain what it received and produced.
Multi-step workflows need policies for branch failure, timeout, retry, compensation and graceful degradation.
One failed branch should not automatically corrupt the entire workflow.
result = run_with_timeout(task, 30)
if failed: fallback(task)Design failure semantics before production, not during the incident.
Orchestration needs end-to-end traces across routing, agents, tools, state transitions and costs.
Without a trace, multi-agent failures are almost impossible to diagnose.
trace(workflow_id, stage, input_id, output_id, latency)Operate orchestration as a workflow system with explicit ownership and telemetry.
Open each item after answering it in your own words.
Use orchestration when distinct responsibilities benefit from explicit coordination.
Use explicit stage contracts and validation between dependent steps.
Route tasks to bounded specialists and measure routing accuracy.
Parallelize independent work and define deterministic merge behavior.
Keep workers bounded and make the supervisor validate outputs.
| Layer | Purpose |
|---|---|
| Why Orchestration Exists | Use orchestration when distinct responsibilities benefit from explicit coordination. |
| Sequential Workflows | Use explicit stage contracts and validation between dependent steps. |
| Routing and Specialist Selection | Route tasks to bounded specialists and measure routing accuracy. |
| Parallel Work | Parallelize independent work and define deterministic merge behavior. |
| Supervisor-Worker Patterns | Keep workers bounded and make the supervisor validate outputs. |
| Shared State and Handoffs | Use structured handoff state with clear artifact ownership. |
| Retries, Timeouts and Partial Failure | Define explicit retry, timeout and partial-failure behavior. |
| Observability and Governance | Trace end-to-end workflow behavior and evaluate both stages and final outcomes. |
Complete at least 12 of the 24 practice cases (50%) and enter your name.
More agents do not automatically mean better results. Prefer the simplest orchestration pattern that measurably improves task success, latency or maintainability.