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Pain points in business: what people struggle with
Search the problems to solve, as people described them on Reddit and other platforms, in their own words.
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Hard-to-Debug AI Agents
Teams running AI agents in production lack readable traces and visibility, making silent failures, wrong outputs, and multi-agent cascades nearly impossible to debug or reproduce.
In their words
By the time we join timestamps across five systems, sampling gaps have removed the trace we really needed
production traces with labeled successes, failures, and parent-child relationships are difficult to find
half the time the trace looks fine until u realise retrieval pulled stale context or a tool retrned garbag. thats usually where the debugging hrs go..
You can see that a tool was called. You can't reconstruct whether the decision chain leading to it was coherent or had drifted somewhere upstream.
everytime it fails is usually for a new different reason i never thought of which takes 1 to 2 hours to investigate
Every service reports healthy, yet we cannot reconstruct the failed run.
You need approval gates, a log of every call it made so you can see what happened at 2am
how are you keeping track of what they're doing?
I feel like I'm playing detective every time a user says "the agent gave me a weird answer."
if you can't reconstruct the full execution path across every agent a request touched, you're not really operating in production, you're hoping.
For this one, it meant reconstructing what the agent saw and decided at each step.
I want to be able to mine this data in order to engineer out failure modes and plug gaps
How can we effectively troubleshoot what went wrong in an agentic loop when we cannot see the reasoning tokens generated from our inputs?
Debugging AI agent behavior has been painful for our team because we're always working backward from logs after something breaks.
AI agent developers and teams
Where they talk
- r/AI_Agents
- r/LangChain
- r/LLMDevs
- r/aiagents
- r/mlops
- r/sre
- r/mcp
- r/devops
Tools they use today
- Langfuse
- LangSmith
- Braintrust
- LangGraph
- OpenTelemetry
- Claude
- Claude Code
- LangChain
Why those fall short
- prompt hashes miss silent model weight updates and behavior drift from memory or context
- Traces show tool calls but not the agent's context or where the chain drifted.
- logging misses retrieved context, tool args, prompt version; traces not connected
- eval suites not dug into, testing not systematic
- abstractions hide prompts and state, hard to debug
Map of ideas
Drawing the map…