LLM agent architectures fail silently as they grow I've been working with LLM-based agent systems (LangGraph-style, multi-node, long-running)
and noticed a recurring failure mode that doesn't show up in early prototypes. As agent graphs grow: - state becomes implicitly shared - routing decisions become opaque - responsibilities blur across nodes The system still "works", but no one can explain why a certain path was taken or what invariant is supposed to hold. In practice, this becomes a serious problem when: - multiple engineers touch the same agent - the agent runs for weeks/months - auditability or reproducibility is required What surprised me is that most agent frameworks optimize for flexibility and velocity, but offer very little guidance on what should be constrained to avoid silent failure. I've been exploring a contract-driven approach: explicit node I/O, declared dependencies, supervisor-level routing constraints, and observability as a first-class concern. I'm curious: - Have others run into similar "it works, but we don't know why" situations? - How do you reason about correctness or debuggability in agent systems? |