ai · 1 min read
Self-Critique and Reflection: Teaching Agents to Catch Their Own Mistakes
A small reflection step often beats a bigger model. Here is how Bhogar AI implements self-critique loops that improve quality without exploding cost.
BABhogar AI TeamProduct & Engineering
The cheapest accuracy improvement most teams never try is asking the model to grade its own work before returning it. A well-designed critique step routinely lifts task accuracy 8-15 points without changing the model.
Why it matters
Naive "are you sure?" prompts barely move the needle. The trick is to give the critic a different role, a different context window, and an explicit scoring rubric. Treat the critic as a separate agent with its own contract.
How Bhogar AI approaches it
Bhogar AI ships a critic-agent template that takes the worker's draft, the original task, and a JSON scoring rubric. Scores below threshold trigger a single revise pass. The whole loop is gated by a hard budget so quality wins do not become latency disasters.
- Critic-agent template with rubric-based scoring
- Configurable threshold and max-revisions budget
- LLM-as-judge optional second-opinion path
- Per-task aggregated quality metrics in the eval dashboard
- Tracing showing draft, critique and revision side-by-side
What you get
Across customer support, code review and document drafting use cases, self-critique lifts first-pass acceptance from 71% to 86% on average for less than 30% additional cost.