I’ve been running into a pattern with LangChain agents:
The final output often looks reasonable, but something about the run feels off — especially when the reasoning chain is weak or the model hedges.
So I put together a small demo to make this visible.
It shows a simple case where:
- the output looks valid
- but the “trust” in that output drops based on signals like hedging / weak evidence
Example output:
LOW_CONFIDENCE: I think the answer might be 42, but I am not sure.
[RECON] Reflex Score: 1.00 → 0.45 (DEGRADED)
[RECON] Reason: weak evidence chain
WARNING: Output still looks valid — but trust has dropped
Repo (5-minute quickstart):
You can run:
python examples/03_drift_detection/app.py
Curious how others are thinking about this:
- Are you explicitly modeling “trust” separate from correctness?
- Using evals / heuristics / guardrails for this?
- Or relying on downstream validation?
Would love to compare approaches.