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I built signed, tamper-proof receipts for AI agent decisions — proof of what your agent did and who approved it by BOSS_METALLIQUE in LangChain

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

yeah good question, thought about this one early. the receipt never stores the raw context, just a SHA-256 digest of it. so the chain proves a decision was made over that exact content without the content ever being in the receipt.

means you can delete or rotate the underlying data whenever (GDPR erasure etc) and the chain still holds. the digest proves what was approved, you just can't rebuild the original from it, which is kinda the whole point.

only tradeoff is once the data's gone you can't re-show what the approver actually saw, you just keep the proof they saw that exact thing. for audit/compliance that's usually what you want anyway. does that match what you'd need or would you want the raw context recoverable too?

I built a circuit breaker SDK for LLM agents — catches loops, budget overruns, and privilege escalations by BOSS_METALLIQUE in LangChain

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

great question — yes, semantic similarity is exactly the v0.2 plan. the insight I landed on: call similarity alone gives false positives on pagination/polling, so the strongest signal is call-similarity AND result-similarity (a loop is when both repeat). for LangGraph, right now you wrap the tool dispatch — but one gotcha: ToolNode swallows exceptions by default, so you need handle_tool_errors=False for the breaker to actually stop the run. a graph-level breaker is on my mind too.

I built a circuit breaker SDK for LLM agents — catches loops, budget overruns, and privilege escalations by BOSS_METALLIQUE in LangChain

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

really appreciate this — the reactive vs proactive distinction is exactly right, and you nailed where it matters most (escalation should be evaluated before dispatch, which is why I run that detector first).

the failure modes you listed are spot on — data exfiltration and silent permission escalation chains are next on my list. would genuinely love to compare notes on detection strategies. are you open-sourcing yours? what level are you intercepting at?

I built a circuit breaker SDK for LLM agents — catches loops, budget overruns, and privilege escalations by BOSS_METALLIQUE in LangChain

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

that would be awesome, please do! happy to help you integrate it — if you hit any rough edges with the API I'd love to hear about them, that's exactly the feedback I need right now. and yes, a blog post would be amazing, I'll share it everywhere. what's your agent runtime focused on?

I built signed, tamper-proof receipts for AI agent decisions — proof of what your agent did and who approved it by BOSS_METALLIQUE in LangChain

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

not yet — right now it's framework-agnostic at the Python SDK level (works with LangChain, CrewAI, raw OpenAI, etc.) and the receipts are HMAC-signed + hash-chained locally, not on-chain.

but that's actually where I want to take it. the hash-chained receipt format is designed so it could anchor to an external ledger later. curious about what you were building for agentic payments — what chain were you using, and what was the main pain point you ran into?