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.
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 for AI and automation?
An agent critiquing and revising its own output before returning. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
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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.