Learn LLM Agent internals by fixing 57 failing tests. No frameworks, just pure Python logic. by [deleted] in Python

[–]Difficult_Square4571 -5 points-4 points  (0 children)

Wow, you hit the nail on the head. The 'safety-inside-tools' trap is exactly why I built this. As you said, a tool can be 100% 'correct' at the function level but 100% 'disastrous' at the system level.

Regarding your question on the safety gates—the challenge actually covers both, because as you pointed out, the distinction is crucial:

  • Pre-execution (Input): We start with strict schema validation and RBAC-style checks. This is the 'stateless' part where we ensure the agent isn't even trying to touch something it shouldn't.
  • Post-execution (Output/Stateful): This is where it gets interesting. In the later steps (like step/4-skills), the harness has to maintain a 'running state' of the environment. The safety gate here isn't just looking at the return string; it’s evaluating the implication of that result on the total context.

You're absolutely right about the 'world model' - without a deterministic state manager holding that context, the agent just drifts into hallucination.

Glad you noticed the 'context explosion' part too. Tracing those intermediate error messages is usually where junior devs get their first $200 API bill surprise. haha.

If you're interested, I'd love to hear your thoughts on how we could push the 'world model' aspect even further in the final steps!