Have you ever wondered why Large Language Model (LLM) agent stacks collapse under orchestration? It’s not always a model issue, but rather a reasoning and structure problem. From my experience, most breakdowns occur due to context handoff loss, orchestrator assumption cascades, cross-session memory drift, multimodal input poisoning, and recursive collapse, among other failure modes.
I’ve mapped out 16 such failure modes and developed small, testable patches to fix them. These patches don’t require fine-tuning or additional models, just reasoning scaffolds that stabilize boundaries, memory, and multi-step logic.
I’d love to hear from folks who have shipped agents at scale: which failure types cause you the most trouble? Are there any counterexamples where a generalized agent doesn’t degrade? What benchmarks or traces should I add?
By understanding these failure modes and implementing fixes, we can build more robust and reliable LLM agent stacks that can handle complex tasks.