Built-in Critic/QA Loops: How AI Agents Review Each Other's Work
One AI agent writes code. Another reviews it for bugs. A third checks security. AgentAGI's built-in Critic and QA loop means every output is automatically reviewed before it reaches you — no external tools required.
The Quality Challenge in Autonomous Systems
The promise of autonomous AI agents is that they work without supervision. You set a goal, the agents execute, and results come back. But here's the problem that every autonomous system faces: who checks the work?
In a traditional workflow, a human reviews every output before it ships. But the whole point of autonomous agents is to minimize human involvement. If you have to review every agent output, you haven't gained much autonomy.
The solution? Agents review each other. AgentAGI's built-in Critic/QA loop creates a closed feedback system where every agent output is automatically reviewed by a dedicated Critic agent — before it ever reaches you.
How the Critic/QA Loop Works
The Critic/QA loop operates as a continuous cycle embedded in every agent workflow:
Step 1: Agent Produces Output
A task agent — content writer, engineer, marketer — completes its assigned work. This could be a blog post, a piece of code, an ad creative, or any other deliverable.
Step 2: Critic Agent Reviews
The output is automatically routed to a dedicated Critic agent. The Critic evaluates the output against a set of quality criteria specific to the task type. For code, it checks for bugs, security vulnerabilities, and style consistency. For content, it checks for accuracy, tone, and alignment with the company mission.
Step 3: Pass or Revise
If the output passes the Critic's review, it moves forward — either to the next stage in the workflow or directly to you. If it fails, the Critic provides specific feedback and the task agent revises the output.
Step 4: Feedback Loop
The Critic's review feeds into the task agent's trust score. Agents whose outputs consistently pass review earn higher trust scores, which means they get priority for future tasks. Agents that require frequent revision see their trust scores decline.
What the Critic Checks
The Critic agent evaluates outputs across multiple dimensions depending on the task type:
- Correctness — Does the output accurately address the task requirements?
- Consistency — Is the output consistent with the company mission, brand voice, and existing work?
- Completeness — Does the output cover all aspects of the task, or are there gaps?
- Efficiency — Was the output produced within reasonable resource usage?
- Safety — Does the output contain any security vulnerabilities, problematic content, or policy violations?
Only AgentAGI has a built-in Critic/QA loop. We verified this against every major competitor: Paperclip.ing has approval workflows but no automated quality review. CrewAI and LangGraph can implement Critic agents if you build them. No platform offers it as a built-in, integrated feature that also feeds back into trust scoring.
Real-World Example: Content Production
Let's say your AI content marketing company needs to publish a blog post. Here's what happens automatically:
- 1. Content Writer agent drafts a 1,500-word blog post based on your topic brief
- 2. Critic agent reviews the draft for: factual accuracy, SEO keyword density, tone alignment, readability score, and plagiarism concerns
- 3. Feedback: The Critic finds that the SEO keyword density is too low and the tone is too formal for the target audience
- 4. Content Writer revises the draft with the feedback incorporated
- 5. Critic re-reviews and passes the output
- 6. Editor agent formats the post for your CMS and schedules publication
- 7. You receive a notification: “Blog post ready for final approval” — with a link to a draft that has already been through two rounds of quality control
The entire process happens autonomously. You only step in when you want to — not because you have to.
The Backend Integration
The Critic/QA loop is integrated across our service architecture, verified against the actual codebase:
- Scheduler (
server/scheduler.ts) — orchestrates the Critic review cycle as part of heartbeat execution - CEO Adapter (
server/adapters/ceo.ts) — routes completed tasks to the Critic for review - Prompt Adapter (
server/adapters/prompt.ts) — defines review criteria and evaluation prompts - Trust-Reputation Service (
server/services/trust-reputation.ts) — receives Critic feedback to update agent scores - Background Review (
server/services/background-review.ts) — handles asynchronous review for long-running tasks
Why This Matters
The Critic/QA loop is what makes true autonomy possible. Without it, autonomous agents produce outputs that require human review — which defeats the purpose of autonomy. With it, agents form a self-correcting ecosystem that produces high-quality work without requiring your constant attention.
It also creates a powerful data flywheel: every Critic review generates data that improves both the Critic's accuracy and the task agents' performance. The system gets better over time.