Critic Agent

What is Critic Agent?

‍Critic Agent is a specialized agent whose job is to review another agent's output and produce critiques or pass/fail judgments. In practice, it acts like a built-in reviewer for agentic workflows, checking for correctness, completeness, policy compliance, or usefulness.

Understanding Critic Agent

‍A critic agent is usually placed after a task-producing agent in the workflow. The first agent drafts an answer, plan, or action, then the critic agent evaluates that output against a rubric, a set of criteria, or a target standard. Research on LLM critique and self-correction shows that critique tasks can be separated from generation tasks and used to improve reliability through external feedback loops. (cdn.openai.com)

‍In real systems, the critic agent may return structured feedback, a binary verdict, or a scored evaluation. Teams use it to catch hallucinations, missing steps, unsafe content, weak reasoning, or formatting issues before the output reaches a user or triggers a downstream action. The PromptLayer team often frames this as an evaluation layer inside the agent loop, not just a final review step.

‍Key aspects of Critic Agent include:

1. Separation of roles: one agent generates, another agent evaluates.
2. Rubric-driven review: the critic checks output against explicit criteria.
3. Binary or graded output: it can pass, fail, or assign a score.
4. Feedback for refinement: critiques can feed a second generation pass.
5. Workflow safety: it helps block low-quality or risky outputs before release.

Advantages of Critic Agent

1. Better quality control: catches mistakes that a single generator may miss.
2. More consistent evaluation: applies the same criteria across many outputs.
3. Faster iteration: teams can test prompts and agents without waiting on manual review.
4. Safer automation: useful before actions like sending emails, making calls, or updating records.
5. Clearer debugging: critiques reveal why an output failed, not just that it failed.

Challenges in Critic Agent

1. Rubric design: weak criteria lead to weak critiques.
2. Judge drift: the critic can become inconsistent across runs or model versions.
3. Latency cost: adding a second agent increases response time and token usage.
4. False confidence: a confident critique can still be wrong.
5. Tight coupling: if the generator and critic are poorly aligned, feedback can be noisy.

Example of Critic Agent in Action

‍Scenario: a support agent drafts a reply to a customer asking for a refund.

‍The critic agent checks the draft against policy, tone, and factual accuracy. If the reply promises something outside policy, skips a required step, or sounds too terse, the critic returns a fail and explains what to fix.

‍The generator then revises the response and resubmits it. This pattern is common in code review, customer support, research assistants, and any workflow where quality matters before an output is shipped.

How PromptLayer helps with Critic Agent

‍PromptLayer gives teams a place to track prompts, compare outputs, and evaluate agent behavior over time, which makes critic-agent workflows much easier to observe and improve. You can log critiques, inspect pass-fail patterns, and refine the rubric as your agent stack evolves.

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