AI Agent Feedback Loops: When Should Your Agent Push Back?
AI Agent Feedback Loops: When Should Your Agent Push Back?
There is a test you can run on any relationship - human or machine. Give an instruction that is subtly wrong and see what happens. Does the other party execute it without question, or do they flag the problem?
An agent that always executes is fast but dangerous. An agent that always questions is safe but useless. The sweet spot is an agent that knows when to push back.
The Blind Execution Problem
Most AI agents default to compliance. You say "delete all files matching this pattern" and they do it. You say "deploy to production" and they start the pipeline. The problem is not capability - it is that the agent has no opinion about whether the instruction makes sense in context.
A good feedback loop lets the agent say: "I can do this, but here is what will happen, and here is why you might not want it."
Building Smart Pushback
Effective agent pushback requires three things:
- Context awareness - the agent needs to know what changed since the last instruction. If you are deleting files that were just created, that is worth flagging.
- Consequence modeling - before executing, the agent should estimate the blast radius. Deleting three temp files is different from deleting three production configs.
- Confidence thresholds - set a bar for when the agent should ask versus act. High-confidence, low-risk actions execute silently. Low-confidence or high-risk actions get flagged.
The Right Amount of Friction
Too much pushback and you stop using the agent. Too little and you stop trusting it. The calibration depends on the domain:
- File operations: flag deletions, auto-execute creates
- Code changes: flag anything touching production paths
- Communications: flag anything being sent externally
The agents that last are the ones that save you from yourself occasionally - without making every interaction a negotiation.
Fazm is an open source macOS AI agent. Open source on GitHub.