Agent Debugging on Programmer.ie: Modern AI programming

Agents From First Principles 08: AI Agent Picks the First Solution? Add Search Instead of One-Shot Generation

Sat, 08 Aug 2026 17:26:00 +0100

An AI agent often fails for a surprisingly ordinary reason:

it commits too early.

It finds one plausible next action, follows it, and then spends the rest of the run trying to make that first choice work.

That can look intelligent because the agent keeps reasoning, calling tools, revising plans, and explaining itself.

But underneath, the trajectory may be almost completely determined by an early mistake.

A coding agent chooses the wrong implementation strategy and spends twenty tool calls repairing it.

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 04: AI Agent Fails on Multi-Step Tasks? Separate Planning From Execution

Sat, 08 Aug 2026 16:35:00 +0100

A surprising number of agent failures are not really model failures.

The model may be perfectly capable of writing each individual step. The failure happens because the system tries to decide what to do and do it at the same time.

That works for simple tasks:

question
  ↓
model
  ↓
answer

It becomes fragile when success depends on several ordered actions:

goal
  ↓
step 1
  ↓
step 2
  ↓
step 3
  ↓
verification

A useful next step in agent design is therefore to separate two jobs:

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:

Agents From First Principles 02: AI Agent Gives Inconsistent Answers? Generate Multiple Candidates and Rank Them

Sat, 08 Aug 2026 15:54:00 +0100

One of the first things you notice when you build anything around a large language model is that the same prompt does not always produce the same quality of answer.

Sometimes the first response is excellent.

Sometimes it is merely acceptable.

Sometimes it misses the point entirely.

That creates a very common agent-engineering question:

If the model is inconsistent, should the agent trust the first answer it gets?

Often, no.

AI Agent Returning Invalid Tool Calls? How to Validate LLM Actions

Sat, 08 Aug 2026 15:46:00 +0100

If your agent sometimes invents a tool name, omits a required argument, returns malformed JSON, or produces an action that looks plausible but cannot actually be executed, the problem is usually not “the agent is dumb.”

The problem is that raw language-model output has been allowed to cross directly into execution.

That boundary is too weak.

The simplest useful agent architecture is not:

prompt
  ↓
model
  ↓
execute whatever came back

It is: