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Module 7 · Lesson 7.3 · 11 min

The Reviewer Agent

Put a reviewer agent between your AI's work and your clients, so every output is checked by a fresh pair of eyes.

Module 7
Your AI Department

Subagents and teams that check each other's work

Bruno Ashford
Bruno Ashford · Module 701 / 13
The problem
You would never ask one employee to prospect, write, do the books and proofread in the same meeting. Stop asking your AI to.
Bruno Ashford
Bruno Ashford · Module 702 / 13
7.1 One agent per role

What a subagent is

A specialist with its own instructions, context and tools.

  • Starts with a clean context
  • Reads its own job description
  • Uses only the tools you allow
  • Returns a summary, keeps the noise
Bruno Ashford · Module 703 / 13
The three-question test

Does this task deserve a specialist?

  1. 1Repeats weekly?Recurring work earns a role
  2. 2Different mindset?Writing versus checking
  3. 3Lots of noise?Research and data crunching
Bruno Ashford · Module 704 / 13
7.2 The file

Anatomy of a subagent

FieldWhat it does
nameUnique id, lowercase with hyphens
descriptionWhen Claude should hand work to it
toolsAllowed tools, comma separated
modelsonnet, opus, haiku or inherit
bodyThe job description in plain English

Project agents: .claude/agents/. Personal agents: ~/.claude/agents/.

Bruno Ashford · Module 705 / 13
7.2 Least privilege

Only the tools the role needs

Leave tools out and the agent inherits everything.

  • Analyst: read, search, write reports
  • Researcher: read, web search, web fetch
  • Reviewer: read and search only
  • Nobody gets send by default
Bruno Ashford · Module 706 / 13
7.3 The reviewer
Writing and checking are different jobs. Give them to different agents.
Bruno Ashford
Bruno Ashford · Module 707 / 13
The loop

Write, review, fix, review

  1. 1WriteSpecialist drafts
  2. 2ReviewRead-only checklist
  3. 3FixEvery issue addressed
  4. 4Review againFixes create new mistakes
Bruno Ashford · Module 708 / 13
7.4 Parallel work

Independent views in minutes

Same question, several specialists, no groupthink.

  • Same inputs for every agent
  • Same output format
  • Read the disagreements first
  • Save it for decisions that matter
Bruno Ashford · Module 709 / 13
Accounting practice task map (illustrative case)

23 recurring tasks, sorted

6human only
14agent drafts, human approves
3agent alone
Bruno Ashford · Module 710 / 13
7.5 Org chart

Every agent has a human owner

AgentJobOwnerApproval
sdr-researcherLead researchSales leadNone, internal
copywriterOutreach draftsMarketingHuman sends
data-analystWeekly numbersOwnerNone, internal
reviewerChecks client workOperationsBlockers stop it
Bruno Ashford · Module 711 / 13
Project

Your first AI team

Three to five specialists, one real workflow.

  • Fill the org chart worksheet
  • Create agents with /agents
  • Restrict every tools line
  • Reviewer is always the last step
Bruno Ashford · Module 712 / 13
Recap
A department is not more AI. It is clear roles, clean context, limited tools and a human who owns every result.
Bruno Ashford
Bruno Ashford · Module 713 / 13
01 / 13

Use ← → to move. The marker highlights text and the pencil draws on the slide; navigation locks while you annotate. Undo removes the last mark, Esc exits.

1Keep the reviewer read-only

Give the reviewer read and search tools only. If it can edit, it will start rewriting instead of reviewing, and you lose the separation of duties. Its only output is a report: pass, or a numbered list of issues with the exact location of each.

2Give it a real checklist

The reviewer is only as good as its checklist. Include facts versus sources, client name and details, numbers and totals, promises you cannot legally or commercially make, tone versus your brand voice, and anything your CLAUDE.md marks as forbidden.

3Build the write, review, fix loop

Ask for the draft, then the review, then the fix, then a second review. Two rounds catch almost everything. Do not skip the second review, because fixes introduce new mistakes.

Prompt for Claude
Write the proposal for the client in notes/call-acme.md using my proposal skill. Then use the reviewer subagent to check it against the proposal checklist. Fix every issue the reviewer finds, then run the reviewer again. Show me the final version and both review reports.
4Grade issues by severity

Ask the reviewer to label issues as blocker (wrong client, wrong price, false claim), important (tone, missing section) or minor (wording). Anything with a blocker never leaves the building, no matter how tight the deadline.

5Encourage automatic use

Put the words 'use proactively' in the reviewer's description, for example 'Use proactively before any document is sent to a client.' That encourages Claude to call the reviewer on its own at the right moments. Still, for anything client-facing, ask for it explicitly until you trust the habit.

The most expensive AI mistake I have seen in a small business was not a hallucinated fact. It was a proposal sent with the wrong client's name in paragraph three. The writer was fine. Nobody checked.

A reviewer subagent is the cheapest insurance you can buy. It did not write the draft, so it has no attachment to it. It reads with a checklist, flags problems, and never rewrites the work itself. Writing and checking are different jobs, and they should be done by different agents.

Key points

  • Writing and checking are different jobs for different agents.
  • The reviewer is read-only and returns a report, never a rewrite.
  • Two review rounds: fixes create new mistakes.
  • Blockers never leave the building.

Avoid

  • Giving the reviewer edit tools so it silently rewrites the work.
  • Using a generic 'check this' instruction with no checklist.
  • Skipping the second review after fixes.
Real-world example: a law firm

A small employment law firm uses Claude to draft first versions of client letters. Their reviewer subagent checks four things: the client name and matter number match the file, every legal claim points to a source in the matter folder, no outcome is promised, and the tone matches the firm's letter style. In the first month it flagged 11 blockers across 140 letters, mostly wrong dates carried over from templates. Each of those would have reached a client.

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