Multi Agent Coordinator is a skill published by agensi that gives an AI agent a structured methodology for decomposing large, ambiguous, or high-risk tasks into specialized sub-agent roles rather than attempting everything in a single pass. When a task is too broad or error-prone for one agent to handle reliably, the skill acts as a coordination framework: it triages the task, selects the smallest effective team of roles (such as Planner, Researcher, Implementer, Reviewer, and Verifier), and sequences their work with explicit handoff rules and state management to prevent duplicated effort or reasoning from stale artifacts.
Parallel execution logic allows independent research and review steps to run simultaneously, reducing round-trips. When agent outputs conflict, specialized debug modes mediate disagreements through evidence-backed synthesis rather than arbitrary tie-breaking. Every run concludes with a standardized report that records which roles were used, what actions each role took, the verified evidence (commands and files referenced), inferences made, and any remaining unknowns.
The skill is designed for scenarios where the cost of error is high: cross-file refactoring, complex bug investigations, and architectural planning. It addresses a known failure mode of directly prompting a single agent to simulate a team, which tends to cause role confusion and hallucination. No environment variables or external service credentials are required.