What is reflective AI?
Reflective AI is AI that examines its own work, judges whether it meets the goal, and uses that judgment to improve. In agent systems it usually takes the form of a self-critique loop. The agent produces a draft or takes an action, a review step checks the result against criteria, and the agent revises. The reviewer can be the same model prompted to act as a critic, a separate judge model (the LLM-as-a-judge pattern), or an external check such as a test suite or a database lookup.
Reflection works at two time scales. Within a task, the loop repeats until the output passes or a retry limit is reached. Across tasks, an agent can write a short note about what went wrong and store it in agent memory, so the next attempt starts from that lesson. Reasoning models are trained to check and correct intermediate steps before they answer.
Reflective AI in practice
A commercial lender uses an agent to draft credit memos. Before an analyst sees a draft, a review step compares every figure with the borrower’s financial spreadsheet and every covenant with the term sheet. Mismatches go back to the agent for correction, and anything unresolved after two passes is flagged for the analyst. A model grading its own prose tends to approve it, while a comparison against source data catches real errors. The analyst still signs the memo, and no agent books a loan or changes a borrower record without human approval.