Unveiling IntentusNet: Enhancing AI Pipeline Reliability
Hello, LinkedIn community! 🚀 I’ve been developing IntentusNet, a groundbreaking execution runtime designed to tackle a common challenge in AI: ensuring that our pipelines are not only observable but reproducible.
Key Features of IntentusNet v1.3.0:
- Deterministic Replay: Implement a write-ahead log (append-only JSONL) for true replayability.
- Crash-Safe Recovery: Experience robust recovery mechanisms with clear failure notifications.
- Execution Contracts: Set timeouts, retries, and cost ceilings for increased reliability.
- Side-Effect Classification: Avoid unsafe retries or fallback practices.
- CLI-First Inspection: Easily list, show, trace, replay, and diff execution states.
This framework is not a planner or agent, but a focused solution for execution semantics in AI tools, including MCP-style tools.
🔗 Explore Further: IntentusNet GitHub
I invite feedback from those in the field: What guarantees do you prioritize in deterministic replay? Let’s elevate our AI systems together! 💡
Share your thoughts and join the conversation!
