AI Agent Workflow
Exploring deterministic tool-calling workflows for autonomous agents.
Large Language Models often hallucinate tool parameters or drift when executing multi-step technical tasks. I wanted to test if strict state machines could make agentic loops reliably reproducible.
Instead of granting free-form tool access, wrap agent actions in deterministic state machines where every tool invocation must be verified against strict runtime schemas before side-effects are committed.
Implemented a local CLI harness that executes shell commands and file edits through sandboxed schema definitions, requiring explicit pre-condition checks.
Strict JSON schema validation drastically reduced malformed argument errors. Adding mandatory self-verification steps caught over 80% of syntax errors before the test phase.
Multi-agent debate architectures introduced unnecessary latency and token costs without significantly improving single-shot accuracy.
Single focused agents with strong deterministic tooling and explicit rollback mechanisms outperform complex multi-agent setups for practical development tasks.
Built a functional prototype runtime capable of executing multi-file refactoring runs reliably within controlled test suites.
Integrate AST-based diff validation to prevent accidental code stripping during large file replacements.