I've spend so much time obsessing over which model is smarter or tweaking our system prompts, but if a simple None value causes a backend TypeError that crashes the whole runtime, none of that reasoning truly matters. The agent is basically dead in the water.
By fuzzing the execution environment deterministically first, we can guarantee the "hands" of the agent work perfectly before we even bother testing the "brain". Plus, it runs in milliseconds during CI/CD instead of waiting for expensive API calls.
I got tired of my AI agents crashing because the LLM hallucinated a JSON key or passed a string instead of an int. So I built ToolGuard — it fuzzes your Python tool functions with edge-cases (nulls, missing fields, type mismatches, 10MB payloads) and gives you a reliability score out of 100%.No LLM needed to run tests. It reads your type hints, generates a Pydantic schema, and deterministically...
I got tired of my AI agents crashing because the LLM hallucinated a JSON key or passed a string instead of an int. So I built ToolGuard — it fuzzes your Python tool functions with edge-cases (nulls, missing fields, type mismatches, 10MB payloads) and gives you a reliability score out of 100%.No LLM needed to run tests. It reads your type hints, generates a Pydantic schema, and deterministically...