AgentsGuide6 min

Design agent handoffs for specialized workflows

A practical way to split complex AI workflows into specialized agents without making the product unpredictable.

Real example

Example: split proposal generation into extract, qualify, draft, and verify

A single prompt tries to read a tender, decide fit, draft a proposal outline, and check compliance. It becomes hard to debug when the final output is wrong.

Use specialized steps: extraction creates facts, qualification scores fit, drafting writes sections, verification checks required documents and claims. Each step passes structured data to the next.

The team can see which step failed and improve that part without rewriting the entire workflow.

Tutorial path

How to implement it

Step 01

Split the workflow by responsibility, such as classify, retrieve, draft, verify, and escalate.

Step 02

Define what each step receives and what it must return before the next step runs.

Step 03

Keep shared memory small and pass structured summaries instead of raw conversation history.

Step 04

Add stop conditions for missing data, low confidence, and unsafe requests.

Step 05

Show the handoff trail in logs or an operator-facing timeline.

Checklist

Ready when these are true

Agent responsibilities are distinct

Structured handoff payloads

Stop conditions exist

Shared context minimized

Handoff timeline visible

Field notes

What matters in practice

A handoff is a product contract, not just a prompt instruction.

Specialized agents need clear ownership, inputs, outputs, and stop conditions.

Users should be able to tell which step produced a recommendation.

Avoid these mistakes

Common failure modes

Do not create agents without distinct responsibilities.

Do not pass full raw context through every handoff.

Do not let later steps overwrite earlier factual extraction without review.

Practical tip

A handoff payload should look like a typed interface, not a hidden conversation summary.

Apply this to a build