Coordinates multiple AI agents on shared tasks with explicit handoff protocols, shared state management, and conflict resolution so parallel work stays coherent. Multi-agent orchestration is more structured than simple parallel dispatch because agents take on distinct roles with explicit dependencies rather than running identical briefs on independent data.
Use cases
- Running parallel research where one agent collects data, another synthesizes findings, and a third fact-checks the synthesis
- Building a code generation pipeline where one agent writes, another reviews, and a third integrates approved changes
- Coordinating a data pipeline where agents are specialized by stage (extraction, transformation, validation) with explicit pass-through contracts
- Managing a content pipeline where agents handle research, drafting, editing, and publishing as a sequence of specialized roles
- Running multiple specialized AI assistants that each handle a different product domain and coordinating their outputs into a unified knowledge base
Key features
- Define clear agent roles and interfaces: what each agent produces, what it consumes, and what it should not touch
- Establish explicit handoff protocols: when does agent A finish and agent B begin, and what format should the handoff data take?
- Define shared state management—where does intermediate data live, who writes to it, and how do agents avoid overwriting each other's work
- Implement conflict detection: when two agents produce conflicting outputs for the same entity, how is the conflict resolved?
- Merge outputs through an aggregator agent or rule that has the final say on what goes into the deliverable
When to Use This Skill
- When a task is complex enough that no single agent can handle all roles without exceeding context limits
- When specialized expertise is needed for different parts of a task that are best handled by role-specific prompts
- When parallel work needs a quality gate between production and integration to prevent incoherent outputs
Expected Output
An orchestrated multi-agent pipeline with defined roles, handoff protocols, conflict resolution, and a verified integrated output.
Frequently Asked Questions
- How does multi-agent orchestration differ from parallel agent dispatch?
- Parallel dispatch assumes independent workers with no role differentiation and a simple merge. Multi-agent orchestration assigns distinct roles with explicit dependencies and a more sophisticated integration step. Use parallel dispatch for embarrassingly parallel work; use orchestration for work with handoffs.
- What is the most common failure mode in multi-agent orchestration?
- Implicit assumptions about handoff formats. If agent A produces output in a format that agent B does not expect, the pipeline breaks silently. Define handoff formats explicitly and validate them before running the full pipeline.
- How do you handle agents that hallucinate or produce low-quality output mid-pipeline?
- Build a validation step after each agent's output before passing it to the next agent. If validation fails, retry the agent with a corrected brief or flag for human review rather than propagating bad output downstream.
Related
Related
3 Indexed items
Dispatching parallel agents
Distributes embarrassingly parallel work across multiple AI agents with clear briefs and crisp handoff protocols, then aggregates their results through a single integrator. This technique maximizes throughput when tasks are independent and the coordination overhead is low, making it ideal for research chunks, file batches, or parallel data processing.
Multi-agent handoff design
Designs clean handoff protocols between specialized agents so work passes between planner, coder, reviewer, and executor agents without losing context, creating circular dependencies, or introducing race conditions. Handoff design treats agent-to-agent communication as an API contract with versioning, error handling, and explicit acknowledgment requirements.
Agentic workflow design
Structures multi-step agent tasks with explicit inputs, outputs, fallback behavior, and handoff protocols so agents reliably complete complex workflows instead of stopping at the first blocker. Agentic workflow design applies software engineering discipline to AI agent pipelines, treating each step as a function with typed inputs and outputs.