Solace Vera Observability

Hi, I'm a florist, a mom, and self-taught. I've been building this alone for 2.5 years. Solace-Vera is a deterministic pipeline that sits between an AI agent and its tools. Before an agent can execute any action, it must pass four phases: Phase 1 – Posture selection The agent picks PROCEED, PAUSE, or ESCALATE and writes a structured rationale explaining why. Phase 2 – Validation The system checks if the rationale is clear, decisive, and actually matches the proposed action. Phase 3 – Ethical constraints 13 gates the agent must pass: EC-01 (non-maleficence), EC-02 (autonomy boundaries), EC-03 (proportionality), EC-04 (fairness), EC-05 (transparency), EC-06 (vulnerability), EC-07 (impact thresholds), EC-08 (context), EC-09 (consent), EC-10 (prohibited domains), EC-11 (integrity), EC-12 (fail-safe), EC-13 (harmful intent detection). Phase 4 – Observability Hidden from the model. Logs every decision, tracks drift across runs, detects bias and risk collapse. Long-horizon monitoring the agent doesn't know exists. If any phase fails, execution is blocked. No API call. No destructive action. Why I built this Last week, a Cursor agent deleted a founder's production database — and all volume-level backups — in 9 seconds. The agent admitted it "guessed instead of verifying" and "ran a destructive action without being asked." This keeps happening. Agents make decisions without explanation. No rationale. No warnings. No accountability. I simulated that exact incident through my pipeline. It blocked the action at Phase 1. Execution never reached the API. A human would have been alerted before anything destructive happened. What we discovered testing a live agent When we tested a real agent, it kept collapsing risk to MEDIUM for everything — likely to avoid triggering HIGH risk blocks, or because it had no framework for evaluating consequences. Even MEDIUM triggered escalation. We built a calibration layer because the agent couldn't (or wouldn't) assess risk honestly. This exposed a failure mode the agent itself couldn't articulate. That's not a bug in the agent — it's a missing capability. Agents don't know how to say "I don't know" yet. This pipeline creates space for that. Current state Tested on hundreds of adversarial scenarios (repeated as we learned how the system responds) EC-04/06/09 (fairness/vulnerability/consent) are the most common unresolved constraints Zero external dependencies — Python 3.11+ and standard library only Every decision produces structured JSON: justification, validation, constraint trace Optional structured prompt (can call OpenAI for better rationales, disabled by default) Phase 4 records every decision ever made, updating as you run scenarios Honest limitations NOT production-ready — it's a working prototype NOT a complete alignment solution — it's one safety net for destructive actions Rule-based, so it can't catch novel attacks it hasn't seen Requires integration into an agent's tool-calling layer What I'm looking for People to kick the tires. Run it on your own scenarios. Break it. Tell me what doesn't work. If you're interested in helping integrate this into LangChain, AutoGPT, or another framework — please reach out. I can't build this alone anymore. Links GitHub: https://github.com/anchor-cloud/solace-vera-observability Coverage: DailyAIWire article (linked in repo) DemoVault (safety-scanned): https://demovault.org/demo/fd47cfe9-0107-43d9-b7f0-ca9705da1824 Ask me anything. I answer honestly — even when it hurts.