Planner Executor on AIBussin — AI applications, systems and books

Planning and Execution

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

The critique loop works on one thing at a time. It assumes the task already exists as a candidate we can hold, inspect and improve.

Some tasks have no draft to hold.

Inspect a project, reproduce the failing test, find the cause, patch the code, rerun the relevant tests, and report what changed.

No single narrow action responsibly completes that goal, and the actions constrain each other. Patching before diagnosis is guesswork wearing the costume of work, and reporting success before observing a passing test is a claim about the world that nothing in the run supports. Worse, if execution reveals that an assumption was wrong, the rest of the route may be wrong too. A runtime choosing each action independently can react to the new observation, but without an explicit representation of the intended route it has nothing concrete to compare the changed world against.

Advanced Agents From First Principles 06: Does One Agent Plan, Execute and Judge Its Own Work? Build a Planner-Executor-Critic Architecture

Sat, 08 Aug 2026 23:49:00 +0100

Does One Agent Plan, Execute and Judge Its Own Work? Build a Planner-Executor-Critic Architecture

A single model can often do all of these things:

understand a task,
decide what to do,
execute a tool call,
inspect the result,
critique its own work,
decide whether it succeeded,
and produce the final answer.

That is convenient.

It is also a dangerous concentration of responsibilities.

If the same component creates the plan, executes it, explains why the result is good, and decides whether the job is complete, then failures become difficult to localize.

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: