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.
Use cases
- Designing a code generation pipeline where a planner agent breaks down tasks and hands them to a coder agent for implementation
- Building a review pipeline where an executor agent produces code, a reviewer agent evaluates it, and a integrator agent merges approved changes
- Coordinating specialist agents where one agent's output is another agent's input and the handoff must not lose information
- Designing error recovery across agents where one agent fails and the next agent needs to understand what partial progress was made
- Building a pipeline where agents work in parallel and their outputs need to be merged by a coordinator agent
Key features
- Define agent roles and boundaries explicitly—what each agent is responsible for, what it owns, and what it should never touch
- Establish shared state contracts: the format and schema of the data that passes between agents must be versioned and validated at each handoff
- Design error escalation paths: when agent B receives incomplete or malformed output from agent A, what should it do? Retry, fallback, or escalate to human?
- Test handoff failure modes by injecting malformed output, missing fields, and timeout conditions at each handoff boundary
- Instrument handoffs with logging so you can trace which agent produced which intermediate result and where a pipeline failure occurred
When to Use This Skill
- When building a multi-agent pipeline where outputs from one agent become inputs to another
- When designing error recovery for a production agentic system that cannot simply restart from the beginning
- When parallel agents produce overlapping or potentially conflicting outputs that need a coordination protocol
Expected Output
A handoff protocol specification with agent roles, shared state schemas, error escalation paths, and documented failure injection test results.
Frequently Asked Questions
- What is the most common multi-agent handoff failure?
- Implicit format assumptions—agent A produces output in a format that agent B parses incorrectly, causing silent failures or wrong outputs. Treat the handoff format as a contract: define it explicitly, validate it at the boundary, and version it when it changes.
- How do you prevent circular dependencies in multi-agent handoffs?
- Enforce a strict DAG (directed acyclic graph) structure: agent A can hand off to agent B, but agent B cannot hand back to agent A. If bidirectional communication is needed, introduce a coordinator agent that mediates the exchange without creating a cycle.
- Should agent handoffs be synchronous or asynchronous?
- Use synchronous handoffs for low-latency tasks where the next agent needs an immediate response. Use asynchronous handoffs (via a queue or shared state) for long-running tasks where agents may operate at different speeds or be retried independently.
Related
Related
3 Indexed items
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.
Multi-agent orchestration
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.
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.