IntentusNet – Deterministic Execution and Replay for AI Agent Systems

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Hi HN, I’ve been working on an open-source project called IntentusNet. It focuses on a narrow but persistent problem in AI systems: AI executions are observable, but not reproducible. When a production issue happens: the model may already be upgraded fallback logic may have changed retries may be implicit routing decisions are no longer recoverable Logs tell you something happened, but they don’t let you replay the execution itself. What IntentusNet does IntentusNet is not a planner, prompt framework, or model wrapper. It’s an execution runtime that enforces deterministic semantics around models: explicit intent routing deterministic fallback behavior ordered agent execution transport-agnostic agents (local, HTTP, ZeroMQ, WebSocket, MCP-style) In the latest release, I added execution recording and deterministic replay. Each intent execution can be: recorded as an immutable artifact replayed later without re-running models explained even after models or agents change The core invariant is simple: The model may change. The execution must not. Why I built this Most AI systems implicitly trust the model to drive control flow. That makes failures hard to reason about and almost impossible to reproduce. IntentusNet takes the opposite approach: models are treated as unreliable but useful routing and fallback are explicit and deterministic executions are facts, not logs This is closer to how distributed systems treat requests than how most LLM stacks work today. Demo (what it actually proves) There’s a small demo that shows: A live execution with “model v1” The same execution with “model v2” (different output) A deterministic replay of the original execution, even after the model changes Routing and execution order stay the same. Only the model behavior changes. No debugger UI, no dashboards — just execution semantics. What this is not Not a replacement for MCP Not a prompt-engineering framework Not a monitoring system Not trying to be “smart” It’s infrastructure for making AI systems operable. Repo GitHub: https://github.com/Balchandar/intentusnet I’m especially interested in feedback from people who’ve had to debug LLM-related production incidents or explain AI behavior after the fact. Happy to answer questions or criticism.

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