Critic Agent Pattern¶

A second model reviews the primary agent's plan before execution begins, catching structural errors early when recovery is cheap.

Structure¶

The critic agent pattern inserts a dedicated review agent between the planning phase and execution:

Planner — the primary agent reads the task, explores context, and produces a structured plan
Critic — a complementary model reviews the plan for risks, gaps, and incorrect assumptions
Gating decision — execution proceeds only if the critic approves; otherwise the planner receives structured feedback and revises

graph TD
    A[Task] --> B[Planner]
    B --> C[Critic]
    C -->|Approved| D[Execute]
    C -->|Rejected + feedback| B

The critic is a distinct agent role, not self-review. Using a different model creates genuine disagreement — the critic is not subject to the same blind spots as the planner. Research on the "self-correction blind spot" measured an average 64.5% blind-spot rate across 14 tested LLMs, where models failed to correct errors in their own outputs even while successfully correcting identical errors from external sources — evidence that same-model review inherits the producer's failure modes.

Why Plan-Gating Matters¶

The pattern's value is timing. The evaluator-optimizer pattern applies a reviewer inside a generation loop — useful for iterative refinement. The critic agent applies review at the plan stage, before any tool calls or code changes execute.

Plan-stage errors are cheap. A structurally flawed plan caught before execution costs one extra model call. The same error caught mid-execution requires rollback, re-planning, and re-execution — each of which re-incurs the token cost of the already-consumed steps.

Multi-step agentic plans amplify single errors. If step 3 of a 10-step plan assumes the wrong environment state, every subsequent step inherits that assumption. A critic that reviews the full plan detects cross-step inconsistencies that per-step review misses.

When to Apply¶

The pattern pays off when:

The task involves multiple sequential steps with compounding dependencies
Mistakes are expensive to reverse (destructive operations, external API calls, database writes)
The primary model has a documented tendency to miss a specific class of error (e.g., environment assumptions, API contract mismatches)

Skip it when:

The task is short enough that re-running from scratch is cheaper than critic overhead
The plan has no branching — a single-step task has nothing for a critic to evaluate
Evaluation criteria are vague; a critic without clear scoring criteria produces inconsistent verdicts

Copilot CLI Implementation¶

Copilot CLI v1.0.18 (April 4, 2026) introduced an experimental critic agent that automatically reviews plans and complex implementations using a complementary model to catch errors early. The feature is available in experimental mode for Claude models.

The release does not describe how the complementary model is selected — whether it is a different vendor model, a different reasoning configuration, or a separate context. Operators should treat the specific routing as an implementation detail subject to change.

Trade-offs¶

Factor	Impact
Latency	Each plan review adds one model round-trip before execution begins
Token cost	One extra model call per task; most valuable when execution cost is high relative to review cost
Coverage	A complementary model surfaces errors the planner's reasoning style systematically misses
Diminishing returns	For simple one-step tasks, critic overhead exceeds the value of catching errors

When a Critic Backfires¶

A high-accuracy critic is not automatically a net-positive intervention. Vasudev et al. (2026) report that a critic with strong offline accuracy (AUROC 0.94) can still induce a 26-percentage-point collapse on one model while leaving another near-unchanged, because interventions face a disruption–recovery tradeoff — recovering failing trajectories while also disrupting trajectories that would have succeeded. Validate the critic on a sample of representative tasks before deploying it on a high-success-rate workload; if the critic disrupts more than it recovers, gate or disable it.

A separate theoretical result bounds the upside. Ao, Gao, and Simchi-Levi (2026) show that any delegated planner-plus-critic network is decision-theoretically dominated by a centralized Bayes decision-maker with the same information access — language-based handoff between agents is a lossy channel. A critic adds value only when it brings information the planner lacked, not when it merely re-reads the same context with the same model family. Pair the critic with a different model and a structured rubric the planner did not see at plan time.

Example¶

A developer runs: copilot -p "Migrate the users table to add a new required column with no default"

Without a critic: The planner generates a migration script and executes it. If the script omits a backfill step for existing rows, production fails at runtime.

With a critic: The critic reviews the plan and flags: "Required column with no default will fail on existing rows — backfill step missing between ALTER TABLE and constraint enforcement." Execution is blocked. The planner revises the plan to include the backfill step before the constraint is applied.

The error is caught before a single query runs.

Key Takeaways¶

The critic agent reviews the plan before execution, not output after generation — catching errors when they are cheapest to fix
A complementary model creates genuine disagreement; self-review by the same model reproduces the same blind spots
The pattern is most cost-effective for multi-step plans where errors compound across steps
Copilot CLI v1.0.18 ships an experimental critic for Claude models that fires automatically
For iterative output refinement, use the evaluator-optimizer; for pre-execution plan validation, use the critic agent