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.
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Hands-on guides, comparisons, and tutorials that cover Agents.
FAQ
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 terms
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Agent Swarm
A multi-agent pattern where many lightweight agents work on sub-tasks simultaneously without a central orchestrator.
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Backpressure (Agent)
A flow-control mechanism that prevents an agent pipeline from overwhelming downstream systems.
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Tool Chaining
A pattern where an AI agent uses the output of one tool call as the input for the next, building a multi-step pipeline of actions.
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