Reflection
An agent critiquing and revising its own output before returning.
/ quick answer
Reflection adds a second pass: the agent reads its draft, lists flaws, and rewrites. Costs 2x tokens; often worth it for writing, code, and analysis. An agent critiquing and revising its own output before returning.
/ frequently asked
What is Reflection?
Reflection adds a second pass: the agent reads its draft, lists flaws, and rewrites. Costs 2x tokens; often worth it for writing, code, and analysis.
What is an example of Reflection?
Writer agent drafts an article, then reflects: 'weak intro, thin evidence in section 3', then rewrites.
Why does Reflection matter?
An agent critiquing and revising its own output before returning.
What should I learn after Reflection?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →MCP (Model Context Protocol)
Open protocol that lets LLMs connect to tools, data sources and apps through a standard interface.
- →Agentic RAG
RAG where an agent decides what to retrieve, when, and from which source — instead of a single static query.
- →Agentic Workflow
A workflow where an LLM decides the next step instead of a hard-coded path.
- →Tool Use
An LLM invoking external functions to act beyond text generation.
Related workflows
Turn this into a repeatable process.
- →How to Create a Website with AI
Go from idea to a live, custom-domain website in one afternoon using AI builders.
- →How to Build an AI Content System
A repeatable pipeline that turns one input into publish-ready content across every channel.
- →How to Start a Niche Website with AI
Pick a niche, validate demand, build the site, and publish ranking content using AI end-to-end.
- →Personal Research Assistant Workflow
A repeatable system to research any topic deeply in under 30 minutes.
Related tool stacks
The tools that run it in production.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
Comparisons & alternatives
Pick between the options.
- →OpenAI vs Anthropic for Agents
Which provider builds better production agents in 2026.
- →CrewAI vs AutoGen vs LangGraph
Three approaches to multi-agent orchestration.
- →Cursor Agents vs Devin vs Lovable
Three ways AI writes code for you in 2026.
- →OpenAI vs Gemini for Agent Building
Both ship strong models and SDKs — differences are in tool calling, context and pricing.