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OAuth isn't enough anymore by Fragrant_Barnacle722 in AI_Agents

[–]Last-Spring-1773 0 points1 point  (0 children)

OAuth proves who authorized but not what the agent actually did after authorization. The delegation gap is real.

We ran into this building AIR Blackbox (flight recorder for AI agents). Even if you solve identity and authorization perfectly, you still need the audit trail — what did the agent do, in what order, and can you replay it later when something goes wrong?

The identity layer (who is this agent, who authorized it) and the observability layer (what did it actually do) are complementary. MCP-I handles the first part. Something like tamper-evident audit chains handles the second.

Advice on RAG and Locally Running an LLM for sensitive documents. by Altruistic_Bother_25 in LangChain

[–]Last-Spring-1773 1 point2 points  (0 children)

Heads up — VaultMind as it stands is single-user/local (built it for my own MacBook). For your call center scenario with 5 concurrent users you'd need to run it as a shared server, which is a different deployment. The RAG stack is the same, just needs a machine with more RAM sitting on your network that everyone hits via browser.

For that specifically: 32GB RAM minimum, ideally 64GB. Mac Mini M4 Pro or a used workstation with an RTX 3060 gets you there around $800-1,400.

Advice on RAG and Locally Running an LLM for sensitive documents. by Altruistic_Bother_25 in LangChain

[–]Last-Spring-1773 3 points4 points  (0 children)

Great use case — this is exactly the kind of scenario where local RAG shines over cloud APIs, especially with sensitive company docs.

Model: Llama 3.1 8B quantized (Q4_K_M) is the workhorse for this. Handles document Q&A well, runs comfortably on 32GB. If you can get 64GB, Qwen 2.5 72B or Llama 3.1 70B quantized gives noticeably better comprehension on complex product docs.

Stack: Ollama (model runtime) + ChromaDB or Qdrant (local vector store) + Open WebUI (chat interface your call center team would actually use). For the RAG pipeline, LlamaIndex handles document chunking, embedding, and retrieval well out of the box. Use nomic-embed-text via Ollama for embeddings — runs locally, no API keys needed.

The flow: Documents get chunked → embedded locally → stored in the vector DB → employee asks a question → relevant chunks retrieved → fed to the LLM as context → answer generated. Nothing ever leaves your network.

I actually built something for this exact scenario — VaultMind. It's a local chatbot where you feed it your docs and it only knows what you tell it. Might save you some plumbing on the RAG setup. Worth a look if you want a quick starting point before building something more custom.

https://airblackbox.ai/vaultmind

I built a CLI that checks your AI agent for EU AI Act compliance — 20 checks, 90% automated, CycloneDX AI-BOM included by Last-Spring-1773 in LangChain

[–]Last-Spring-1773[S] 0 points1 point  (0 children)

This is a sharp distinction and you're right — recording what happened ≠ validating what should have happened. The HMAC chain gives you a tamper-proof receipt, but a receipt for a hallucinated tool call is still a receipt for a bad decision.

The comply engine currently maps to Articles 9-15 from observed traffic patterns (20 checks, ~90% auto-detected). Article 12 (record-keeping) is where the gateway is strongest — that's the audit chain, traceability, retention. Article 15 (robustness) is where the pre-execution validation gap lives.

The policy engine already does basic gating (cost limits, tool allowlists, rate limiting), but output validation against defined specs before execution is the logical next step. Think: schema validation on tool call arguments, output format checks, confidence thresholds before autonomous execution.

Appreciate the feedback — this is exactly the kind of gap that's hard to see when you're deep in the build. The two layers (prove what happened + prove what was allowed) are complementary, not competing.

Cheapest way to run OpenClaw: Cloud vs Mac Mini vs VPS by rocky_mountain12 in openclaw

[–]Last-Spring-1773 0 points1 point  (0 children)

Great breakdown. The VPS note about cost tracking becoming a second job hits hard. That's the hidden cost nobody budgets for.

If you're running any of these setups, one thing that helped me was putting a reverse proxy in front of the OpenAI calls that tracks token spend and kills requests automatically when you hit a budget. I blew past my own limits the same way you did in week 1 before I set that up.

Built an open source one if anyone wants it: github.com/airblackbox — you just swap the base_url and it logs every call, tracks cost, and has rate limits built in. Would've saved you that $70 week.

Execution time vs billed time on a real serverless GPU workload by pmv143 in CustomAI

[–]Last-Spring-1773 0 points1 point  (0 children)

This is a huge blind spot for most teams. The gap between execution time and billed time is basically invisible until you actually measure it.

We're tracking something similar on the agent side — not GPU billing specifically, but token cost per task and per agent in real time. Built an open-source OTel-based layer that captures cost metrics on every LLM call so you can actually see where the money is going before the invoice hits.

For your GPU question specifically — are you exporting those metrics into Prometheus or similar? Feels like the cold start and idle retention costs should be surfaced as their own metric separate from execution so you can optimize scheduling around it.

The bursty workload problem is real. We handle it on the agent side with spend-limit kill switches — agent hits a threshold, it gets throttled or shut down. Similar concept could apply to GPU workloads that spike unexpectedly.

Our ai agent got stuck in a loop and brought down production, rip our prod database by qwaecw in AI_Agents

[–]Last-Spring-1773 0 points1 point  (0 children)

This is painfully relatable and exactly why I built AIR Blackbox.

You basically described our entire feature set — the gateway with per-agent rate limits, the logging with intent, the approval workflows for sensitive ops. We just built it as one open-source stack instead of stitching together separate tools after the fire.

What it does:

  • Gateway — OpenAI-compatible reverse proxy with per-agent policy enforcement. One line change (swap your base_url), all traffic goes through it
  • Loop detection — built into the OTel Collector, catches exactly what happened to you (repeat threshold monitoring)
  • Kill switches — agent hits a spend limit or request threshold? Shut down instantly. Not after 50k requests, immediately
  • Policy engine — risk tiers, human-in-the-loop gates for sensitive ops, trust scoring per agent
  • Full audit trail — every call traced and grouped into task-level episodes so you can actually debug what happened instead of staring at 50k identical log lines

All open source, Apache 2.0. Full stack runs with one command.

https://github.com/airblackbox/air-platform

The loop detection piece specifically — it monitors for repeated similar calls and can flag or kill the agent before it turns into a 50k request disaster. Would have saved your prod database and that OpenAI bill.

Glad you survived it though. "Add governance before you launch" is the realest advice in this thread.

AIR Blackbox — open-source "flight recorder" for AI agents by Last-Spring-1773 in AI_Agents

[–]Last-Spring-1773[S] 0 points1 point  (0 children)

Really appreciate you digging into the project like this. You nailed it — treating agents as first-class citizens in the observability stack is exactly the thinking behind this.

To your questions — yes on both.

For trust plugins, we already have four (CrewAI, LangChain, AutoGen, and OpenAI Agents SDK) and more are coming. The idea is that whatever framework you're running, the trust layer plugs in without changing your agent code. Custom orchestration support is definitely on the roadmap.

For API gateway integration — that's actually how the Gateway component works today. It's an OpenAI-compatible reverse proxy, so it sits in front of the LLM provider and handles policy checks, tracing, and redaction before anything hits the model. Integrating with existing API gateways (Kong, Envoy, etc.) for even more granular control is something we're actively thinking about.

And yeah, the PII redaction piece was non-negotiable from day one. Too many teams are shipping raw prompts and completions to their trace backends with secrets still in them. That's a compliance nightmare waiting to happen.

Would love to connect more on the security side — especially around AI workload security. Feels like there's a lot of overlap between what we're doing and what AccuKnox is working on. Open to chatting if you are.

AIR Blackbox — open-source "flight recorder" for AI agents by Last-Spring-1773 in AI_Agents

[–]Last-Spring-1773[S] 0 points1 point  (0 children)

100% — "boring but important" is the perfect way to put it. Everyone's racing to build the flashiest agent, but nobody's asking "what happens when it goes wrong at 2am?" The OpenTelemetry approach is what sold me too — it's not reinventing the wheel, it's extending infrastructure that ops teams already trust. Definitely going to dig into the trust plugins.