Agent Control Patterns — Part 2: Reflection — A Simple Way to Improve Answer Quality

2026-03-03 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

The concept of "Reflection" in AI systems separates the generation of an answer from its evaluation, aiming to improve output quality without altering the underlying model. Unlike single-pass LLM systems that generate and immediately return a result, Reflection introduces a structured review process: Generate → Critique → Revise → Stop. This involves distinct "generator" and "critic" roles, even if using the same model, with the critic providing feedback based on clear criteria. The process continues until a stop condition is met, such as a fixed number of iterations. Implementing this often uses a `MessageGraph` with nodes for generation and reflection, where the critique is returned as a `HumanMessage` to guide subsequent revisions. Structured feedback, like a `Critique` object with `strengths`, `weaknesses`, and `improvement_suggestions`, further enhances consistency and precision in revisions.

Key takeaway

For AI Engineers building LLM-powered agents, implementing a Reflection loop can significantly enhance answer quality without complex model fine-tuning. You should design your agent workflows to explicitly separate content generation from critical review, using structured feedback mechanisms to guide iterative revisions. This approach ensures more robust and reliable outputs by systematically addressing weaknesses identified during the critique phase, rather than relying on single-pass generation.

Key insights

Separating generation from evaluation through reflection improves AI answer quality without model retraining.

Principles

• Execution does not guarantee answer quality.
• Separate generation and evaluation for consistent improvement.
• Structured feedback leads to more reliable revisions.

Method

Implement a Generate → Critique → Revise → Stop loop, using distinct generator and critic roles, with structured feedback and a clear stop condition.

In practice

• Use `MessageGraph` for reflection loops.
• Return critiques as `HumanMessage` for feedback.
• Define `Critique` objects for structured feedback.

Topics

• Reflection
• LLM Agents
• Self-Correction
• Reasoning Loops
• Answer Quality Improvement

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.