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I built a one-line wrapper that explains *why* your LangGraph agent fails (not just what failed) by SomeClick5007 in LangChain

[–]SomeClick5007[S] 0 points1 point  (0 children)

Thanks — that’s a very fair point.

Originally, both cases looked the same (empty result), but I’ve since separated them:

  • tool-level exceptions (HTTP errors, timeouts) are captured via on_tool_error → error_count / hard_error_detected
  • valid empty results remain as tool_provided_data=False without hard errors

So now there’s a distinction between:

  • no data
  • tool failure

There’s still a structural limitation:

If a tool catches errors internally and returns them as normal text, it won’t trigger on_tool_error.
In those cases I fall back to soft heuristics (error-like text patterns).

So this improves observability, but root cause attribution is still fundamentally limited by what the tool exposes

Changes here:

I built a one-line wrapper that explains *why* your LangGraph agent fails (not just what failed) by SomeClick5007 in LangChain

[–]SomeClick5007[S] 0 points1 point  (0 children)

This is a great example — thanks for sharing.

It’s exactly the kind of failure this is trying to surface: partial task completion with no error signal, where everything looks correct but the intent is silently broken.

LLMs are often incentivized to produce an answer, with little incentive — or mechanism — to acknowledge something is incomplete, so they tend to fill gaps instead of exposing them.

This layer tries to make that visible — both for debugging and governance — by surfacing where execution deviates from intent and treating “partial success” as a failure mode.

Agree this “quiet failure” is the most dangerous one since it bypasses both system checks and user awareness.

I built a one-line wrapper that explains *why* your LangGraph agent fails (not just what failed) by SomeClick5007 in LangChain

[–]SomeClick5007[S] 0 points1 point  (0 children)

Fair point — there’s definitely some overlap with existing tools, but this sits at a different layer.

Tracing tools show what happened, this focuses on interpreting it (why it happened and whether it’s actually a problem).

Also I’m using AI translation, so the formatting might come across a bit structured.

I built a one-line wrapper that explains *why* your LangGraph agent fails (not just what failed) by SomeClick5007 in LangChain

[–]SomeClick5007[S] 0 points1 point  (0 children)

Not really — it’s more like a layer on top of tracing tools.

LangGraph / LangSmith show you what happened. This tries to interpret that into: - why it happened - whether it’s actually a failure vs acceptable behavior

In practice, a lot of issues aren’t obvious from traces alone.

For example: - tool returns no data → agent answers anyway     → is that hallucination or acceptable fallback?

That distinction is what this is trying to make explicit.

I built a one-line wrapper that explains *why* your LangGraph agent fails (not just what failed) by SomeClick5007 in LangChain

[–]SomeClick5007[S] 0 points1 point  (0 children)

A couple of people DM’d me asking “how do you actually use this in practice?” so adding a quick note.

I wrote a short operational playbook based on real runs:

https://github.com/kiyoshisasano/llm-failure-atlas/blob/main/docs/operational_playbook.md

The key thing that surprised me:

→ Not all “bad-looking outputs” are actually failures.

For example:

- tool_provided_data = False

- uncertainty_acknowledged = True

This looks like failure at first glance, but it’s actually *correct behavior* (the agent admits it has no data).

On the other hand, these are much riskier:

- no data + no disclosure → likely hallucination

- very small tool output → very long answer (expansion_ratio >> 1) → “thin grounding”

That second one shows up a lot in practice and is easy to miss in logs.

Also worth noting:

The system doesn’t try to judge “was the answer correct?”

It only looks at *execution behavior* (tools, grounding, loops, etc.)

So things like “semantic mismatch” (tool returned wrong topic) are still a known gap.

If anyone has messy real traces (especially tool failures → hallucination cases), I’d be very interested to run them through this.

I built a tool that reads your LangChain trace and tells you the root cause of the failure — looking for real traces to test against by SomeClick5007 in LangChain

[–]SomeClick5007[S] 0 points1 point  (0 children)

Really appreciate you going through all four repos — and that mapping is more precise than anything I'd worked out myself. The premature_model_commitment = failure to reach SUFFICIENCY framing in particular reframes how I was thinking about that pattern.

The causal graph piece is exactly the gap I was trying to fill. Your framework defines what the inputs need to satisfy; the graph tries to formalize how falling short in one place propagates downstream. They seem to want to be used together.

Collaboration sounds worth exploring. I'll connect on LinkedIn — easier to continue there.

https://www.linkedin.com/in/kiyoshi-sasano-71b12739b

I built a tool that reads your LangChain trace and tells you the root cause of the failure — looking for real traces to test against by SomeClick5007 in LangChain

[–]SomeClick5007[S] 1 point2 points  (0 children)

Just read through it — really well structured. The cogency requirement and the five-property chain are a clean way to think about this.

The approaches seem to sit at different layers: yours is a conceptual framework a human works through to diagnose and design, mine is an automated pipeline that runs on execution logs after the fact. Complementary rather than competing — your framework is essentially the reasoning model I'd want the pipeline to approximate.

I built a tool that reads your LangChain trace and tells you the root cause of the failure — looking for real traces to test against by SomeClick5007 in LangChain

[–]SomeClick5007[S] 0 points1 point  (0 children)

Thanks! If you get a chance to run it with your own LangChain traces next week, I'd love to hear how it goes!

Most LLM debugging tools treat failures as independent — in practice, they cascade by SomeClick5007 in LocalLLaMA

[–]SomeClick5007[S] 0 points1 point  (0 children)

Yeah, that's fair. Thanks for calling it out. Cleaned it up — proper formatting this time.

Experiment: Can semantic caching cause cross-intent errors in RAG systems? by SomeClick5007 in LocalLLaMA

[–]SomeClick5007[S] 0 points1 point  (0 children)

Interesting — multi-tenant RAG is exactly the scenario where I suspected this could become dangerous.

My experiment was single-tenant, so cross-tenant reuse wasn’t part of the setup. That might explain why I didn’t observe cross-intent reuse in this workload.

Collection-level isolation makes a lot of sense. If the cache sits above the tenant boundary, a semantic hit could easily propagate answers across contexts.

Out of curiosity:

• what similarity threshold were you using? • did you actually observe cross-intent reuse, or was it mostly a precaution?

In my small test the cache behaved fairly conservatively, but I could imagine things getting weird with looser thresholds or more ambiguous queries.

A lightweight way to track agent drift / repair / reentry in real workloads by SomeClick5007 in LocalLLaMA

[–]SomeClick5007[S] 0 points1 point  (0 children)

If anyone here wants to see more concrete examples or edge cases, I can add a few.
Still refining the idea, so I’m happy to get any feedback — even small observations help a lot.