Agent Loops on AIBussin — AI applications, systems and books

Critique, Revision, and Acceptance

Sat, 08 Aug 2026 16:23:00 +0100

Selection can only choose among the candidates it is given. A strong evaluator may recognize that every available candidate is poor, but it cannot select a correct answer that the generator never produced.

That limitation becomes practical because the candidates usually come from one model answering one prompt, and such samples correlate. When four candidates share the same misreading of the evidence, ranking them can still produce a confident winner while leaving the shared defect untouched.

Runtime State, Progress, and Termination

Sat, 08 Aug 2026 16:56:00 +0100

A plan is a claim about the future. It says that reproducing the failure, then diagnosing it, then patching, then testing, is a route from here to a working system. Making that claim explicit was worth the machinery. But it is written before any of the work happens, and execution may invalidate one of its assumptions almost immediately.

Execution produces something else entirely: a trajectory. Actions went out, observations came back, and some of what came back may contradict the intended route. The patch step failed because a dependency is missing. The test step is not merely late; it is unreachable.

Agents From First Principles 05: AI Agent Gets Stuck in a Loop? Add State, Feedback and Stopping Conditions

Sat, 08 Aug 2026 16:56:00 +0100

An AI agent that keeps calling the same tool, revisiting the same page, rewriting the same file, or repeatedly saying “I’ll try again” is not displaying persistence.

It is displaying a control-flow bug.

This is one of the most common failure modes in agent software because the basic loop is deceptively simple:

observe
 ↓
decide
 ↓
act
 ↓
observe
 ↓
repeat

The problem is hidden inside the final word.

Agents From First Principles 03: AI Agent Keeps Making the Same Mistake? Add a Critique-and-Revision Loop

Sat, 08 Aug 2026 16:23:00 +0100

An AI agent can fail in a particularly frustrating way: it produces an answer that is almost right, you ask it to improve the answer, and it produces another answer with the same underlying defect.

Sometimes the wording changes. Sometimes it adds more explanation. Sometimes it becomes longer and more confident. But the important mistake survives.

That usually means the system is doing this:

prompt
 ↓
model
 ↓
answer

or this: