Orchestration · Software component
Multi-Agent Coordinator
Software componentOrchestrationOrchestration & ToolsVariation point (abstract)arc:MultiAgentCoordinator
An abstract coordination component that determines how, and in what order, specialised agents contribute to a shared multi-agent goal.
Responsibility. Coordinates the contributions of multiple specialised agents toward one collaborative outcome.
Also known as: Multi-agent coordination model, Crew process, Collaboration model
Variant of Workflow Orchestrator abstract
Variants
| Variant | When to choose |
|---|---|
| Conversational Agent Coordinator | Choose when collaboration benefits from flexible dialogue (agents challenging outputs, requesting clarification, negotiating), for exploratory workflows, prototyping and human-overseen scenarios where readable transcripts aid transparency; avoid where determinism and structured audit trails are required. |
| Role-Based Task Orchestrator | Choose when the workflow mirrors an organisational structure with clear roles, predictable task dependencies and sequential or hierarchical delegation; avoid for dynamic workflows needing conditional branching or iterative self-correction loops. |
| Supervisor Agent | Choose when a workflow maps to an organizational team with clear role specialization and needs validation gates and iterative refinement until outputs meet quality standards. Avoid when agents need frequent ad-hoc back-and-forth interaction or when roles overlap or capabilities are vaguely defined. |
Relationships
is configured by structural
emits telemetry to dynamic
is constrained by control
orchestrates control
- Worker Agent abstract Ch2.4
is evaluated by assurance
is monitored by assurance
Design guidance
- SHOULD be preferred over a single generalist agent only when the workflow benefits from distinct specialist expertise and collaboration.
- SHOULD delegate complex state management or iterative refinement to graph-based single-agent workflows and keep multi-agent coordination at the workflow level, maintaining clear boundaries.
Classification
- Patterns
- Specialization and collaborationSeparation of concerns across agents
- Technologies
- AutoGenCrewAI
- Quality attributes
- Maintainability (ISO/IEC 25010)Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
- Risks mitigated
- Context-switching inefficiency of a single generalist agentSequential execution of independent tasksDuplicate effortDropped tasksGoal divergenceAgent de-synchronisation
Sources
- Ch2.4: T. Nguyen, "Multi-Agent Frameworks," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.4. ISBN: 9798244538229.
- Ch3.9: T. Nguyen, "Reasoning Quality," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.9. ISBN: 9798244538229.
- Ch3.10: T. Nguyen, "Efficiency Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.10. ISBN: 9798244538229.
- Ch4.4: T. Nguyen, "Performance Profiling and Optimization," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.4. ISBN: 9798244538229.
- Ch10.5: T. Nguyen, "Human-over-the-Loop," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.5. ISBN: 9798244538229.