Supervisor-Worker
Decompose, Delegate, Supervise
Instead of one AI producing a monolithic response, a Supervisor Agent decomposes the task and delegates to specialized workers. Review, approve, or revise each piece independently.
Framing
The problem
Monolithic AI outputs hide decomposition and make review too coarse.
The pattern
Split work into specialist agents under a supervising layer.
Why chat breaks here
One chat thread bundles planning, execution, and review into one opaque turn.
Risks
Coordination overhead can outweigh the benefit for narrow tasks.
Avoid when
A single agent or human can handle the work directly.
Use when
Work decomposes into steps that benefit from specialized agents and a coordinating layer.
DOPE evaluation
- Directability
- Approve, revise, or redirect any agent without affecting others
- Observability
- Each agent's task, status, and output are independently visible
- Predictability
- Defined agent roles produce specialized, consistent outputs
- Explainability
- Each worker agent's task assignment and decision rationale are independently visible
In the wild
- Claude Code Subagents (Anthropic) — Orchestrator + isolated specialists with their own context, tools, and permissions. The strongest end-user-visible expression of the pattern in 2026.
- CrewAI Hierarchical (CrewAI) — Hierarchical process with auto-created manager agent that delegates to role-defined workers. Explicit supervisor-worker process at the framework level.
- Microsoft Agent Framework (Microsoft) — AutoGen successor (Apr 2026 v1.0) — graph-based workflow engine for multi-agent coordination. AutoGen entered maintenance mode Mar 2026; this is where the lineage goes.
- Manus AI Sub-Agents (Manus) — Explicit planning / browsing / code / file-management sub-agents under a coordinating layer, visible per phase with live replay.
FAQ
When should I use the Supervisor-Worker pattern?
Work decomposes into steps that benefit from specialized agents and a coordinating layer.
When should I avoid the Supervisor-Worker pattern?
A single agent or human can handle the work directly.
What problem does Supervisor-Worker solve?
Monolithic AI outputs hide decomposition and make review too coarse.
Why is chat the wrong fit for this?
One chat thread bundles planning, execution, and review into one opaque turn.
Related patterns
- Often paired with: Agentic Dashboard — Decomposed agents need a control room to monitor each independently.
- Often paired with: Editable Plan — The supervisor produces a plan; users edit it before workers execute.
- Often paired with: Approval Gates — Per-worker checkpoints catch off-track output before it propagates.