OpenClaw

OpenClaw Agent Handoff Rules: Keep Multi-Agent Work Clean

By Thomas McLoughlin · 24 Feb 2026

This guide is for teams using OpenClaw with multiple agents. You will learn simple handoff rules that reduce confusion, stop duplicate work, and protect quality when tasks move between agents.

OpenClawAI AgentsOperationsWorkflow

Why handoffs break

Most agent failures are not model failures. They are handoff failures.

One agent starts without enough context. Another agent repeats work. A third agent makes edits with the wrong goal. Output becomes messy, even if each agent is smart.

Good handoffs solve this.

What a good handoff includes

Every handoff should include five things:

Goal: One clear outcome.
Scope: What is in and out.
Inputs: Files, links, or data to use.
Constraints: Rules that must be followed.
Done state: What “finished” looks like.

If any one of these is missing, errors increase fast.

The 5-step OpenClaw handoff method

Step 1: Start with role clarity

Each agent needs a role. Keep roles narrow.

Research agent finds facts.
Drafting agent writes first version.
QA agent checks rule compliance.
Publish agent applies final changes.

Do not ask one agent to do all roles at once unless the task is tiny.

Step 2: Use a fixed handoff block

Use the same handoff format every time. This makes work predictable.

Task ID
Owner agent
Required inputs
Output format
Deadline or run window

Consistency is a quality tool, not admin overhead.

Step 3: Attach acceptance checks

Acceptance checks prevent “looks done” output.

Word count range
Required sections
Metadata fields present
No broken links

Checks should be binary when possible. Pass or fail is better than vague review notes.

Step 4: Keep memory and logs tidy

Agents need context, but too much context creates noise.

Store only key decisions in shared memory.
Log final outputs and blockers.
Avoid dumping long raw transcripts unless needed.

Good logs speed up future runs and reduce repeated mistakes.

Step 5: Run a short close-out review

After each workflow, run a 5-minute review:

What was delivered?
Where did handoff quality drop?
What one rule should change next run?

Small review loops create strong operational learning.

Handoff template you can copy

Use this basic format inside your OpenClaw run notes:

Goal: Publish 4 new articles in approved format.
Scope: Blog files + articles index only.
Inputs: CONTENT-RULES.md + metadata conventions.
Constraints: 800–1200 words, practical tone, checklist required.
Done: Files created, index updated, commit and push complete.

This tiny block removes most confusion before work starts.

Common mistakes in multi-agent workflows

Vague prompts: “Do SEO content” is not a task.
No owner: Everyone touches the task, no one owns quality.
No final checker: Errors reach publish stage.
Changing rules mid-run: Agents work from different instructions.
No post-run notes: Team repeats preventable errors.

Most of these are process problems, not AI problems.

Quality checklist for every handoff

✅ Goal is clear in one sentence
✅ Scope is explicit (what to touch and not touch)
✅ Inputs are linked or named
✅ Constraints are measurable
✅ Output format is defined
✅ Acceptance checks are binary where possible
✅ Final owner is named
✅ Close-out notes are logged

How this helps SEO and content teams

When handoffs are clean, teams publish faster with less rework. You get more consistent voice, fewer rule breaks, and clearer reporting.

Faster cycle times
Fewer missed requirements
Better trust in agent output
More time for strategy work

That is the real value of agent operations.

A real-world handoff example

Imagine your team needs four new articles in one run.

Planner agent: picks topics and filenames.
Writer agent: drafts all four within content rules.
Metadata agent: applies canonical, OG, and schema blocks.
Index agent: adds article cards and checks links.
Release agent: commits, pushes, and reports output.

Each agent has one clear lane. This reduces collisions and confusion.

Simple scorecard for handoff quality

Track handoff quality each run with a basic score out of 10.

2 points: task goal was clear
2 points: scope stayed controlled
2 points: output met acceptance checks
2 points: rework was low
2 points: close-out notes were complete

If a run scores below 8, improve one rule before the next run.

Before you launch: 60-second preflight

Confirm each agent has one owner and one task.
Confirm shared files are named and accessible.
Confirm acceptance checks are visible to all agents.
Confirm final reviewer and release owner are set.

This tiny preflight catches most avoidable failures before they become expensive rework.

Final takeaway

OpenClaw can scale output, but only if handoffs are strong. Treat handoffs like product specs: clear, short, and testable.

Start with one fixed handoff template this week. Use it on your next workflow. Then refine one rule after each run.