Multi-Agent Orchestration
Commander's core differentiator is its ability to orchestrate multiple agents across 5 canonical topologies, aligned with Anthropic's "Building effective agents" ontology. Nine legacy topology names remain as aliases for backward compatibility during a 2-version migration window.
Canonical Topologies
| Topology | Description | Legacy Alias |
|---|---|---|
| SINGLE | One agent handles the entire task | — |
| CHAIN | Sequential pipeline, each agent builds on previous output | SEQUENTIAL |
| DISPATCH | Independent subtasks run concurrently, results synthesized | PARALLEL |
| ORCHESTRATOR | Lead agent decomposes and delegates to specialists | HIERARCHICAL / HYBRID |
| REVIEW | Generate → critique → refine loop | DEBATE / ENSEMBLE / EVALUATOR-OPT |
Topology Selection
The deliberation engine (deliberation.ts) classifies every task and selects the optimal topology:
| Complexity | Dependencies | Selected Topology |
|---|---|---|
| Trivial | None | SINGLE |
| Low | Sequential | CHAIN |
| Low | Independent | DISPATCH |
| Medium | Mixed | ORCHESTRATOR |
| High | Mixed | ORCHESTRATOR |
| High-risk | Any | REVIEW |
| Critical | Any | REVIEW |
| Iterative | Any | REVIEW |
Topology Details
SINGLE
One agent handles the entire task. Best for simple, well-scoped requests.
CHAIN
Agents execute in order, each building on the previous output via artifact references. Best for multi-step transformations.
DISPATCH
Independent subtasks run concurrently via sub-agents. Results are synthesized at the end. Best for parallelizable work.
ORCHESTRATOR
A lead agent decomposes the task and delegates subtasks to specialist agents, then synthesizes results. Adaptive rerouting allows mixed parallel/sequential execution.
REVIEW
Multiple agents independently produce solutions, then cross-validate and refine. Includes debate (cross-validation), ensemble (weighted voting), and evaluator-optimizer (generate-critique-refine) patterns.
Agent Scaling
The effortScaler.ts module scales the number of agents dynamically:
- Simple tasks: 1 agent
- Moderate tasks: 2–5 agents
- Complex tasks: 5–10 agents
- Research tasks: 10–20 agents
Agent Communication
Agents communicate through:
- Message bus (
messageBus.ts): Pub/sub for inter-agent and system events - Agent handoff (
agentHandoff.ts): Direct agent-to-agent handoff with persistent inbox - Artifact system (
artifactSystem.ts): Reference-based communication to prevent information loss - Three-layer memory: Shared working/episodic/long-term memory for context across agents