Agents
Reflection (Agent)
A technique where an AI agent evaluates its own output before returning it to the user.
In depth
A technique where an AI agent evaluates its own output before returning it to the user. After generating a response, the agent prompts itself to critique the result for accuracy, completeness, and quality, then revises based on its own feedback. Reflection adds an extra inference step but significantly improves output quality. Some agents run multiple reflection cycles, progressively refining their work.
Example
In practice, developers reach for Reflection (Agent) when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Agents.
FAQ
Common questions
What is Reflection (Agent)?
A technique where an AI agent evaluates its own output before returning it to the user.
Why does Reflection (Agent) matter for AI developers?
Reflection (Agent) sits in the Agents part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Reflection (Agent)?
Developers Digest publishes tutorials and videos that cover Agents topics including Reflection (Agent). Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Agents
Mixture of Agents
An ensemble technique where several LLMs answer the same prompt in layers.
Agents
Agent Handoffs
A multi-agent mechanism where one agent passes control of the conversation to a more specialized agent, rather than calling it as a subroutine.
Agents
Refusal Fallback
A safety architecture where a model routes blocked queries to a more constrained model instead of refusing outright.
Put this concept to work
Get Smarter About AI Dev
New tutorials, open-source projects, and deep dives on coding agents - delivered weekly.
One email per weekReal code, not theoryFree forever