What is a feedback loop?

A feedback loop is a cycle in which the results of an action are fed back to shape the next one. With AI agents, the loop runs from a reviewer's corrections back into the agent's brief, sources and bounds, and from the agent's held and refused items back into what reviewers are trained on.

Why do feedback loops matter with agents?

An agent with no feedback loop repeats its mistakes, and a reviewer with no feedback loop repeats theirs. Corrections made in the queue fix one item each. Only when someone collects them, finds the pattern and changes something upstream does the next item come out better.

There is a second reason. Governed agents do not learn by quietly updating their model on your data. They improve through qualification: a person changes the brief, adds or replaces a source, adjusts a bound, and the agent is re-certified. That makes the feedback loop a human job, and a trainable one.

How does a feedback loop work in practice?

A working loop has four steps, each with an owner:

1. Capture. The reviewer approves, edits or declines a held draft and records why in one line.
2. Collect. The agent owner reviews corrections weekly and groups them by cause: wrong source, missing fact, bound too tight, bound too loose.
3. Change. The owner changes the brief, sources or the customer's own limits, never the platform floor.
4. Confirm. The change goes through certification again, and reviewers are told what changed and what to watch for.

Skipping step four is the most common failure. The owner fixes a source, nobody retests, and reviewers keep correcting an error that is already gone, or miss a new one.

What does a feedback loop look like in a real team?

Take a customer service team where an agent drafts replies at depth 2. Reviewers keep editing the same paragraph about delivery times. The owner finds the agent is using an old service page. She replaces the source, the agent is re-certified, and she tells the team in a two-line note. Edits on that paragraph stop within a week.

The same loop applies to training. A training feedback form for agent courses captures what learners found unclear, and the course changes before the next group. Collecting feedback on an agent's work covers the first half of the loop in more detail.

How is a feedback loop different from human-in-the-loop?

Human-in-the-loop describes a person approving each action before it takes effect: the held verdict. Human-on-the-loop describes a person watching actions the agent completes and stepping in when needed. Both are about who decides each action.

A feedback loop is about improvement over time. A team can have a person in the loop on every action and still have no feedback loop, if nobody collects the corrections. Whether every agent needs a human in the loop is a separate question from whether its errors get fixed.

Which related terms should you know?

• Correction: a single reviewer edit, the raw material of the loop.
• Drift: when an agent's answers move away from its certified behaviour, which Attest checks as one of its seven gates.
• Lessons learned log: a team record of what changed and why.

For how feedback loops fit into a training programme, see training teams for AI agents.

Where the Academy fits

Pryme Intelligence Academy's Operator track includes modules on teaching the agent and keeping knowledge current, which are the change and confirm steps of the loop. Builders learn to carry a change through to a new certification in Attest.