OpenClaw Agent Team: How to Decide, Organize, and Validate Multi-Agent Work

When a task becomes complex, a single Agent is often not "not smart enough." The real issue is usually the lack of clear division of labor, an independent validation perspective, and one role that is genuinely responsible for the final judgment.

This does not only happen in coding, research, or content creation. A crawfish farmer deciding whether to expand this year's ponds, a founder deciding whether to build a new product, or a content team deciding whether a video script is worth producing all face the same problem: too much information, too many risks, and each person only seeing part of the picture.

OpenClaw is one of the Agent collaboration systems DeepCarry is exploring. OpenClaw Agent Team is designed to solve the organizational problem behind complex tasks: the lead Agent breaks down work, defines standards, collects results, and makes the final judgment like a project owner; multiple sub-agents complete independent work units within clear boundaries.

In one sentence:

The lead Agent is the owner, and sub-agents are independent specialists. The lead Agent breaks down the task, defines standards, collects results, and makes the judgment; sub-agents complete focused work within clear boundaries.

More plainly: do not ask one AI to answer everything in one pass. Ask several AIs to examine the same problem from different angles, then let one AI synthesize the evidence and make the final judgment.

The goal of an Agent Team is not to generate more answers. It is to improve judgment quality, execution speed, blind-spot coverage, and verifiability in complex tasks.

Why Agent Teams Are Needed

A single Agent is excellent for short tasks, quick judgments, and linear execution. But when the task becomes content planning, product evaluation, code review, competitor research, investment research, or a real business decision, single-threaded handling quickly hits several limits:

1. There are more information sources, and one Agent processing them sequentially becomes slow.
2. The perspective can become narrow, missing user, distribution, technical, commercial, or risk dimensions.
3. Long tasks easily mix research, analysis, execution, and review, making the final result scattered.
4. Important tasks need a second perspective to reduce the bias of a single judgment.

The value of an Agent Team is to break a complex task into independent, verifiable, parallelizable work units, then let the lead Agent synthesize them into a final judgment.

This is not "the more Agents, the better." It is about making complex work organized, inspectable, and reviewable.

Current Practice Stage

If we describe capability in maturity levels, OpenClaw Agent Team is no longer a one-off experiment. It has entered a stable, usable stage for small-team collaboration.

At this stage, it can already handle:

• Splitting subtasks according to task structure.
• Defining each sub-agent's role and input scope.
• Launching multiple sub-agents in parallel.
• Requiring structured output from sub-agents.
• Waiting for multiple results to return.
• Cross-checking, deduplicating, and extracting the main decision.
• Turning process and conclusions into reusable methods.

The next stage is not about adding more Agents. It is about improving acceptance and validation standards:

• Define clearer pass / fail standards before the task begins.
• Cover more real task types: research, code, data, competitor analysis, and execution.
• Use real metrics to prove the benefit of Agent Teams, such as time saved, omissions discovered, rework reduced, and final decision quality improved.

Boundaries Between the Lead Agent and Sub-Agents

The most important principle of an Agent Team is: the lead Agent must not outsource the final judgment to sub-agents.

Sub-agents can provide evidence, perspectives, reviews, and suggestions. But the final decision must be made by the lead Agent.

Think of it as a real team:

• The lead Agent is like the person in charge: deciding how to split the problem, whose input to trust, and what to do in the end.
• Sub-agents are like specialist consultants responsible for one area, such as market, cost, risk, technology, or execution.
• The testing and acceptance Agent is like a QA or acceptance owner, checking whether the final result meets the original goals and standards.
• The final deliverable is not a copy-paste bundle of several people's comments. It is a clear judgment.

There is one more important rule: testing and acceptance cannot be delegated only to a sub-agent. A validation Agent can help detect problems and point out where the result fails to meet the goal, but the lead Agent is still responsible for the final outcome. It is like a department leader: having QA colleagues does not remove the leader's responsibility for delivery quality.

What the Lead Agent Owns

The lead Agent is the overall owner. It is responsible for:

1. Defining the task objective.
2. Deciding whether an Agent Team is needed.
3. Deciding how many sub-agents to create.
4. Splitting the task.
5. Setting input scope.
6. Setting output format.
7. Setting acceptance criteria.
8. Collecting results.
9. Deduplicating, cross-checking, and identifying conflicts.
10. Organizing testing and acceptance, checking whether the result meets the goal, constraints, and expectations.
11. Producing the final decision and deliverable.
12. Capturing reusable lessons into project memory, system memory, or team documentation.

What Sub-Agents Own

A sub-agent is an independent work unit. It is suitable for narrow, clearly bounded, verifiable tasks.

A sub-agent is responsible for:

1. Reading the specified input.
2. Analyzing the problem from the specified perspective.
3. Producing structured conclusions.
4. Providing evidence and reasoning.
5. Not expanding the task boundary on its own.
6. Not taking responsibility for the final decision.

One type of sub-agent is especially important: the testing and acceptance Agent.

The testing and acceptance Agent does not produce the main solution. It checks:

1. Whether the final result answers the original question.
2. Whether it meets the success criteria defined at the beginning.
3. Whether key constraints, risks, or boundary conditions were missed.
4. Whether the evidence is strong enough to support the conclusion.
5. Whether the output can be executed, published, delivered, or reviewed afterward.

If an Agent Team is meant to produce reliable results, not just results that look complete, it must have a validation mindset. For complex tasks, at least one sub-agent should take the acceptance perspective. More importantly, the lead Agent must use that feedback to make the final call.

Only when this boundary is clear does an Agent Team stop being a simple bundle of multiple answers.

How to Decide How Many Sub-Agents to Create

The number of Agents should be determined by task structure. More is not always better.

0 Agents: Simple Tasks

Use this for:

• Revising one sentence.
• Summarizing a short paragraph.
• Checking one specific file.
• Making one focused judgment.

The lead Agent should handle these directly and avoid extra coordination cost.

1 Agent: Independent Review or Second Perspective

Use this for:

• Reviewing a plan for flaws.
• Reading through a project folder.
• Checking a competitor's core information.
• Critiquing the lead Agent's first draft independently.

A typical structure is: the lead Agent drafts a solution, the sub-agent finds weaknesses, and the lead Agent revises the final deliverable.

2-3 Agents: Multi-Dimensional Complex Tasks

This is the most stable Agent Team setup.

Use it for:

• Content plan review.
• Product proposal evaluation.
• Business strategy decisions.
• Project retrospectives.
• Risk evaluation.

For example, a content plan can be split into:

• Distribution review Agent: platform fit, title, opening, and distribution strategy.
• User perspective Agent: whether users would click, finish watching, and follow the advice.
• Production execution Agent: whether recording, editing, and publishing are feasible.

A product proposal can be split into:

• User value Agent: pain points, scenarios, and usage frequency.
• Technical feasibility Agent: implementation path, dependencies, and risks.
• Monetization Agent: pricing, conversion, ROI, and competitive landscape.
• Testing and acceptance Agent: whether the final proposal meets the goal, is executable, and misses any key risks.

If the task must deliver a clear result, such as an article, product proposal, code change, research conclusion, or business decision, a testing and acceptance Agent should be included by default. It does not have to participate in early creation, but it should perform an independent check before or after the lead Agent synthesizes the result.

3-5 Agents: Multi-Source Research

Use this for:

• Competitor research.
• Industry trend analysis.
• Multi-platform content research.
• Technical selection research.

These tasks are better split by information source:

• Official website / documentation Agent.
• GitHub / technical Agent.
• X / community Agent.
• YouTube / Bilibili / Xiaohongshu Agent.
• Competitor comparison Agent.

More Than 5 Agents: Large, Clearly Parallel Tasks

Only use more than five sub-agents when the task boundaries are extremely clear. Examples:

• Breaking down several competitors at once.
• Researching multiple platforms independently.
• Scanning several modules in a large codebase.
• Analyzing multiple markets, languages, or regions.

The risk is that integration cost rises quickly. Without a clear structure, do not start with too many Agents.

Five Variables for Deciding Whether to Use an Agent Team

At the beginning of each task, the lead Agent can check five variables:

1. Divisibility: Can the task be split into non-overlapping parts?
2. Independence: Does each part have independent input and output?
3. Parallel value: Will parallel work save time or increase coverage?
4. Blind-spot value: Will independent perspectives reduce bias?
5. Integration ability: Can the lead Agent synthesize the results and make the final judgment?

If variables 1, 2, and 5 are not true, use fewer sub-agents or none at all.

For ordinary users, you do not need to memorize these five variables. Ask yourself three questions:

1. Does this problem need to be viewed from several angles?
2. Am I worried about missing important risks?
3. Do I need a clear decision at the end, not just more information?

If all three answers are yes, an Agent Team is a good fit.

Standard Workflow

Ordinary users can start with the simplest version:

1. State the problem clearly.
2. Ask AI to split into 2-3 roles.
3. Tell each role what it should inspect and judge.
4. Ask each role to provide evidence, risks, and suggestions.
5. Add a testing and acceptance perspective to check whether the result meets the goal.
6. Ask the lead Agent to synthesize a final decision and take responsibility for the result.

Below is the fuller workflow.

Step 1: Classify the Task

First decide the task type:

• Simple execution.
• Independent review.
• Multi-dimensional proposal.
• Multi-source research.
• Large-scale execution.

The task type determines whether an Agent Team is needed, and whether the split should be by role or by information source.

Step 2: Define Acceptance Criteria

Before launching sub-agents, define:

• Success criteria.
• Failure criteria.
• Required fields in the response.
• Time budget.
• Evidence requirements.

For example:

The test passes if all three sub-agents return results; each result includes a score, risks, and improvement suggestions; and the lead Agent can produce at least one main decision supported by multiple perspectives.

Without acceptance criteria, an Agent Team can easily look busy while remaining impossible to evaluate.

Step 3: Split Roles

Roles should complement each other and avoid duplication.

Good roles include:

• User value.
• Technical feasibility.
• Monetization.
• Risk and compliance.
• Content distribution.
• Execution.
• Competitor source.
• Community feedback.

Poor roles include:

• General analysis Agent.
• Comprehensive research Agent.
• Take a look Agent.
• Help me think Agent.

The more specific the role, the more useful the output.

Step 4: Limit Input Scope

Each sub-agent should know what it can inspect.

Good input scope:

• 2-5 specified files.
• Several specified URLs.
• A specified directory or module.
• A specified platform or data source.

Poor input scope:

• "Look at the whole project."
• "Research all materials."
• "Analyze the entire industry."

The clearer the input boundary, the easier it is for the sub-agent to produce verifiable conclusions.

Step 5: Define Output Format

Sub-agent output should be short, concrete, and comparable.

Recommended structure:

• Most important conclusion.
• Biggest risk.
• Three improvement suggestions.
• Score.
• Evidence citations.
• Recommended next step.

Structured output lets the lead Agent compare perspectives quickly instead of drowning in several long responses.

Step 6: Lead Agent Synthesis

After sub-agents return, the lead Agent must synthesize rather than paste their answers together.

When synthesizing, focus on four things:

1. Consensus: Issues mentioned by multiple Agents have the highest priority.
2. Conflict: When Agents disagree, compare the strength of their evidence.
3. Gaps: Check whether any key issue was not covered.
4. Action: Decide what to change, what to do first, and how to verify it.

The lead Agent's most important job is to turn multiple sub-agent outputs into one clear judgment.

Step 7: Testing and Acceptance

Before final delivery, the Agent Team must run a testing and acceptance pass.

Validation should answer five questions:

1. Does the result answer the original question?
2. Does it meet the success criteria?
3. Did it miss any key constraint or risk?
4. Is the evidence strong enough to support the conclusion?
5. Are the next actions clear and executable?

This step can be performed first by a testing and acceptance Agent, but the lead Agent must still make the final call. The lead Agent is responsible for the whole Agent Team, not only for assigning work.

Step 8: Record the Retrospective

After each Agent Team task, record at least three things:

1. How many Agents were used, and why.
2. Which Agent contributed the most.
3. How the split should be improved next time.

This step turns Agent Teams from a one-time trick into a long-term capability.

Reusable Sub-Agent Prompt Template

You are an independent <role> sub-agent.

Only read the following inputs:
1. <file / URL / data source>
2. <file / URL / data source>

Goal: <one sentence describing the problem this sub-agent should solve>

Output at most <N> items, and must include:
1. <key conclusion>
2. <biggest risk>
3. <3 improvement suggestions>
4. <score or pass/fail>
5. <specific evidence>

Requirements:
- Cite specific files, pages, code, fragments, or data as evidence.
- Provide executable suggestions.
- Focus on your role perspective.
- Do not expand beyond the input scope.
- Do not output long background explanation.

Standard Synthesis Report Template

# <Task Name> Multi-Agent Division Report

## Task Objective

## Sub-Agent Setup

| Agent | Role | Input | Output Requirements |
|---|---|---|---|

## Sub-Agent Result Summary

### Agent 1

- Score:
- Key conclusion:
- Risk:
- Suggestion:

### Agent 2

- Score:
- Key conclusion:
- Risk:
- Suggestion:

## Lead Agent Synthesis

## Testing and Acceptance Conclusion

## Cross-Validation Conclusion

## Conflicts and Uncertainty

## Final Decision

## Next Steps

## Retrospective

- What worked:
- Problems:
- Next improvements:

A Simple Business Example: Deciding Whether to Expand a Crawfish Farm

To make the method easier to understand, start with a non-technical example.

Suppose you raise crawfish and want to decide:

Should I expand my crawfish ponds from 30 mu to 60 mu this year?

If you ask one AI, it may give you a long answer covering market demand, costs, weather, disease, sales, and cash flow. It looks comprehensive, but it is hard to tell what matters most or whether any key risk was missed.

This is a good use case for an Agent Team.

Main Task

Help me decide whether I should expand my crawfish ponds from 30 mu to 60 mu this year.

Please organize three sub-agents to analyze the decision from market, cost, and risk perspectives.

Each sub-agent should provide:
1. Key conclusion
2. Biggest risk
3. Data I need to add
4. Whether expansion is recommended
5. Reasoning

Finally, the lead Agent should synthesize a decision: expand, expand cautiously, or wait.

After synthesis, use a testing and acceptance perspective to check:
1. Whether the answer addresses "should I expand to 60 mu"
2. Whether key risks are explained
3. Whether executable next steps are provided

Agent A: Market Agent

The market Agent only examines demand and sales questions, such as:

• What is the local crawfish price trend this year?
• Is demand stable across restaurants, wholesalers, and community group-buying channels?
• If production increases, are there enough sales channels to absorb it?
• If the harvest is concentrated, will the price be pushed down?

Its task is not cost calculation or disease analysis. It answers:

Can the additional crawfish be sold at a reasonable price?

Agent B: Cost Agent

The cost Agent only examines investment and cash flow, such as:

• Additional pond rent or renovation cost.
• Seedling, feed, labor, electricity, and water-quality maintenance costs.
• Whether adding 30 mu will create obvious cash-flow pressure.
• Whether the business can still break even if prices fall.

Its task is to answer:

Does the math still work after expanding?

Agent C: Risk Agent

The risk Agent only examines uncertainty, such as:

• Abnormal weather.
• Disease risk.
• Water-quality management capability.
• Whether labor is sufficient.
• Whether sales channels are too concentrated.
• How much loss can be tolerated if things go wrong.

Its task is to answer:

What is the worst case, and can you withstand it?

Lead Agent Synthesis

After the three sub-agents return, the lead Agent should not simply copy three answers. It must make the final judgment.

For example, it may conclude:

I do not recommend expanding directly from 30 mu to 60 mu. A safer plan is to expand to 45 mu first and lock in at least two stable sales channels in advance. The market Agent sees demand opportunity, but the cost Agent points out significant cash-flow pressure, and the risk Agent warns that labor and water-quality management have not yet been proven at a 60-mu scale.

This conclusion is more useful than "the crawfish market looks promising this year, so expansion can be considered." It is not generic advice. It integrates market, cost, and risk into an executable decision.

But the work is not finished yet. The lead Agent still needs to validate the result:

• Does this conclusion answer "should I expand to 60 mu"?
• Does it explain why not 60 mu directly, but 45 mu first?
• Does it state that sales channels must be secured before expansion?
• Does it warn about cash flow, labor, and water-quality management risks?

Only when these questions pass can the Agent Team result be considered aligned with the objective.

This is the value of Agent Teams for ordinary users: not making AI talk more, but making AI split a complex problem apart, cover it more completely, and then bring it back into one decision.

A Real Example: Creating a 3-Agent Team for a Video Script

In one DeepCarry content proposal review, we used three sub-agents to review the same video script from distribution, user, and execution perspectives:

How much mental effort can AI actually save when writing emails or improving messages?

The main task was:

Review whether this video script is worth recording and provide revision priorities.

Agent A: Distribution Review

You are a content distribution review sub-agent.

Only review this video script and recording execution package.

Goal: Review it from the distribution perspective of Bilibili, YouTube, Douyin, and X.

Output at most 8 items, and must include:
1. Current strongest distribution point
2. Current biggest distribution risk
3. Three improvement suggestions for title / opening
4. Best platform for first release
5. Distribution potential score from 0-10

Requirement: Cite specific script content or storyboard details as evidence.

Agent B: User Review

You are a normal-user perspective review sub-agent.

Only review this video script and recording execution package.

Goal: Review whether ordinary viewers would click, watch to the end, and try it themselves.

Output at most 8 items, and must include:
1. What users are most likely to resonate with
2. What users may find boring or too abstract
3. Which demo feels strongest, and which demo is weakest
4. Three suggestions to make it easier for viewers to follow
5. Normal-user value score from 0-10

Requirement: Cite specific script content or demos as evidence.

Agent C: Execution Review

You are an execution and production review sub-agent.

Only review this video script and recording execution package.

Goal: Review from actual recording, editing, and publishing execution perspectives.

Output at most 8 items, and must include:
1. Whether it is truly ready to record
2. What materials or decisions are still missing before recording
3. What is most likely to go wrong in editing
4. Minimum viable recording sequence
5. Execution maturity score from 0-10

Requirement: Cite specific storyboard items, checklists, or script content as evidence.

How the Lead Agent Synthesizes

If all three Agents point to the same issue, the lead Agent should upgrade it into the main decision.

In this case, all three perspectives supported one change:

Move the "polite rejection" demo to the front as the main hook, then first edit a 45-60 second short video to test feedback.

The reasons:

• Distribution perspective: this demo has the strongest emotional resonance.
• User perspective: this demo feels the most real.
• Execution perspective: this demo is best suited for a short-video feedback test.

This is the value of an Agent Team: multiple independent perspectives supporting a more stable action judgment.

A Minimum Viable Agent Team for Beginners

For first-time use, start with this structure:

1. Critic Agent: focuses on finding problems.
2. User Agent: focuses on real user value.
3. Execution Agent: focuses on whether it can be implemented at low cost.
4. Testing and acceptance Agent: checks whether the final result meets the goal and expectation.

This setup works for most proposal reviews, content reviews, and product reviews.

Minimum viable prompt:

I have a proposal. Please organize three sub-agents to review it:

1. Critic Agent: find the biggest weaknesses.
2. User Agent: judge whether users truly need it.
3. Execution Agent: judge whether it can be implemented at low cost.

Each Agent should output at most 5 items, and must provide a score and evidence.

Finally, the lead Agent should synthesize one decision: do it, revise and then do it, or wait.

After synthesis, use a testing and acceptance Agent to check whether the decision answers the original question, meets the goal, and misses any key risks.

If you are not a technical user, you can make it more business-oriented:

I have a business decision. Please review it from three angles:

1. Profit opportunity: Is there real upside?
2. Cost pressure: How much money, labor, and time are required?
3. Biggest risk: What is the worst case, and can I withstand it?

Each angle should output at most 5 items and must give reasons.

Finally, synthesize a decision: do it, try it at smaller scale, or wait.

After synthesis, run one testing and acceptance pass: check whether the decision fits my goal, whether the risks are clear, and whether the next step can be executed.

Common Risks of Agent Teams

Risk 1: The Task Is Split Too Thin

Too many Agents increase integration cost. Start with 2-3 Agents, then expand only if needed.

Risk 2: Sub-Agent Output Is Generic

The root cause is usually that the prompt did not specify input, output, and evidence requirements. Require source citations and concrete suggestions.

Risk 3: The Lead Agent Becomes a Narrator

The value of an Agent Team is synthesis, not presenting several answers side by side. The lead Agent must output the final decision.

Risk 4: No Acceptance Criteria

Without pass / fail standards, there is no way to know whether collaboration worked. Every test should define pass conditions in advance.

Risk 5: Division of Labor Without Testing and Acceptance

An Agent Team may split the task well, but no one checks whether the final result meets the goal. The fix is to include a testing and acceptance Agent by default, and require the lead Agent to personally run one final acceptance judgment before delivery.

Risk 6: Duplicate Perspectives

If multiple sub-agents perform similar analysis, they create noise. Roles must complement each other.

Upgrade Path

To move Agent Teams from stable usability to a higher level, four areas need to be strengthened:

1. Define acceptance thresholds before every Agent Team task.
2. Accumulate samples across task types: research, code, data, competitor analysis, and execution.
3. Make the testing and acceptance Agent a default role for high-risk tasks.
4. Track metrics: time saved, omissions found, conflicting views, adopted suggestions, and rework reduced.
5. Turn frequent task patterns into skills, templates, or fixed playbooks.

The next batch of useful test directions includes:

• Research Agent Team: for example, competitor content research for AI lifestyle tools, split into Bilibili / YouTube / Xiaohongshu / X / official websites.
• Product Agent Team: for example, DeepCarry product direction priorities, split into user pain points / technical feasibility / monetization / competitive landscape.
• Code Agent Team: for example, scanning architectural risks in a project, split into frontend / backend / testing / security.
• Investment Research Agent Team: for example, analyzing a crypto project, split into fundamentals / token economics / on-chain data / community sentiment / risk.

Final Principle

The core of an Agent Team is not "more Agents." It is clearer division of labor, stronger evidence, and better final judgment.

Testing and acceptance is the final gate. Without acceptance, an Agent Team is merely divided work. With acceptance, it becomes closer to a team that is accountable for outcomes.

The safest default configuration is:

• Simple task: 0 Agents.
• Independent review: 1 Agent.
• Complex proposal: 2-3 Agents.
• Multi-source research: 3-5 Agents.
• Large task: run a pilot with no more than 3 Agents first, then expand based on information quality.

DeepCarry will continue turning these high-frequency collaboration patterns into reusable OpenClaw workflows, so complex tasks can move from "one-off conversations" to organized, verifiable, and reviewable work.

Whether you are writing code, creating content, researching a project, or deciding whether to expand a business, the essence of an Agent Team is the same: split the problem across different perspectives, then bring the results back into one clear decision.