Agent Handoff
A pattern where one AI agent passes context and state to another specialized agent to keep multi-step automation modular.
Agent handoff is the practice of passing work from one specialized agent to another with preserved context and state. Each agent focuses on a narrow task and hands back structured results.
In business flows, this shows up in research-to-draft, draft-to-QA, triage-to-resolution, or intake-to-enrichment pipelines. One agent gathers data, another reasons over it, a third formats or executes actions.
It improves reliability by isolating responsibilities and reducing prompt sprawl. Proper handoffs mean context packets include inputs, decisions made, constraints, and open questions so downstream agents can act without rework.
Frequently Asked Questions
Why use multiple agents instead of one?
Specialized agents are easier to reason about, cheaper to run, and simpler to debug. They also let you swap or version steps independently.
What should be included in a handoff packet?
The original request, normalized facts, decisions made, constraints, unresolved items, and proposed next steps. Keep it structured (JSON) to avoid context loss.
How do I prevent context blow-up?
Summarize aggressively, drop irrelevant fields, and standardize schemas. Use retrieval for long histories instead of pushing everything forward.
How do I track accountability across agents?
Log which agent handled each step, its inputs, outputs, and confidence. Include trace IDs so you can audit the full chain.
When should a handoff include a human?
Insert humans when risk is high—payments, legal comms, customer escalations—or when confidence is low. Give reviewers the full context packet to minimize time.
Can agents run in parallel?
Yes, for independent sub-tasks like gathering data from multiple systems. Merge outputs with validation before acting on them.
How do I keep prompts consistent between agents?
Use shared schemas, style guides, and validation rules. Store them centrally and version them alongside code.
What failures are common in handoffs?
Missing context, mismatched schemas, or ambiguous instructions. Mitigate with strict contracts, required fields, and fallback summaries.
How do I measure success?
Track rework rate between agents, total cycle time, error categories, and human escalations. Lower rework and faster resolution indicate good handoffs.
Agentic AI
An AI approach where models autonomously plan next steps, choose tools, and iterate toward an objective within guardrails.
Agentic Workflow
A sequence where an AI agent plans, executes tool calls, evaluates results, and loops until success criteria are met.
Agentflow
A meta-framework for composing multi-step, tool-using agents with routing, memory, and validation built in.
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