Cite as: Real Problem AI problem “Why do my AI agents fail silently in production with no usable trace?”. Opportunity score 8.8 out of 10 (severity 9, AI feasibility 9, market signal 9, competition gap 8). Category Others. Trend Agents. Source signal: r/MachineLearning, r/LocalLLaMA, LangChain Discord, X dev threads (May 2026).. Canonical URL: https://www.realproblem.ai/archive/why-do-my-ai-agents-fail-silently-in-production-with-no-trace.
Why do my AI agents fail silently in production with no usable trace?
Multi-step agents (Cursor, Claude Code, custom LangGraph) drift, loop, or quietly skip steps; standard APM tools report a successful status while the agent is producing garbage.
Who has it: AI engineers shipping agent workflows, SRE leads, founders running agent-first products.
Evidence
Developers describe a production agent running for a long time, spending real money on tokens and returning a refusal, while their monitoring showed every request as successful.
Our summary of a complaint that recurs in public posts, not a quote. Nobody submitted it to Real Problem AI.
Seen in: r/MachineLearning, r/LocalLLaMA, LangChain Discord, X dev threads (May 2026).Scoring breakdown
Existing players
- LangSmith · LangChain-only, trace-heavy not production-quality observability
- Datadog LLM Observability · Adds LLM spans to APM but no agent-level semantics
- Helicone · Strong for single LLM calls, weak for multi-step agents
- Braintrust · Eval-first; production monitoring still nascent
What they are missing
Agent-grade observability: per-step expected vs actual schema, drift detection, cost-per-task SLO, automatic regression vs last week. Not just spans, semantic correctness signals.
Stack hint
#AI1 · Canonical URL: https://www.realproblem.ai/archive/why-do-my-ai-agents-fail-silently-in-production-with-no-trace