What is Agent Handoff?
Agentic AIThe transfer of a task from one agent to another inside a multi-agent run. Every handoff is a context boundary: what the receiving agent sees is a design decision, not an accident.
Why It Matters
A handoff is where a multi-agent system is most honest about its design. The receiving agent knows nothing except what crosses the boundary: whatever context was packaged, whatever constraints were written down, whatever the sending agent chose to include. Everything else is gone.
That makes each handoff a compression point and a reinterpretation point at once. Instructions get summarized, context gets dropped, and the receiving agent fills the gaps with its own priors. The system can only be as reliable as its weakest transfer.
What Crosses the Boundary
Task spec. What the receiving agent is being asked to do, stated precisely enough that it cannot be reinterpreted into a different task.
Context slice. The subset of prior work the receiver needs: relevant results, constraints, and history, bounded because dumping the full transcript imports the sender’s mistakes.
Artifacts. The concrete outputs being passed: documents, data, code, decisions already made.
Provenance. Where the task came from and which policies apply to it, so downstream agents inherit the right constraints rather than starting clean.
Where It Breaks
The first failure is context loss: the receiver gets the task but not the reason, and optimizes for the letter of the request against the intent. The second is reinterpretation drift across a chain: each handoff shifts the task a little, and five handoffs later the agent is doing a different job than the one that was assigned.
The third is the handoff loop, where two agents delegate the same task back and forth. Each agent’s own trace looks healthy; the failure lives between them, which is why a trajectory that spans the whole run matters more than per-agent logs.
How Flytebit Handles It
We design handoffs as contracts, not conversations: structured payloads with explicit task specs and bounded context, handoff budgets that cap delegation chains, and run-wide tracing so the space between agents is observable. The failure taxonomy this defends against is documented in Agentic AI Horror Stories, and the build pattern is part of our agentic AI systems work.