Why Reflection Agent matters at work
Reflection asks an agent to critique or revise its prior work. It can improve some tasks, but repeated self-review can also reinforce a mistaken premise; external evidence and objective graders remain important.
A practical workplace example
Example
After drafting an analysis, the agent checks every factual claim against retrieved sources and removes unsupported claims before presenting the result.
What teams should evaluate
- 01
Give the system the minimum tools and permissions required, with explicit stop conditions and limits on retries, spend, and steps.
- 02
Place human approval before irreversible, sensitive, financial, legal, or customer-facing actions.
- 03
Trace plans, retrieved context, tool calls, outputs, errors, and approvals so failures can be reconstructed and corrected.
Frequently asked questions
What is Reflection Agent in simple terms?
A reflection agent reviews its own intermediate result against a goal or criteria before deciding what to do next.
Why does Reflection Agent matter for teams using AI?
Reflection asks an agent to critique or revise its prior work. It can improve some tasks, but repeated self-review can also reinforce a mistaken premise; external evidence and objective graders remain important.
What is a practical example of Reflection Agent?
After drafting an analysis, the agent checks every factual claim against retrieved sources and removes unsupported claims before presenting the result.
Sources and further reading
Luffy writes every definition in plain language and checks it against primary research or authoritative technical guidance. Source links open in a new tab.