Show HN: Provability Fabric – From Guardrails to Guarantees for AI Agents
Most AI “safety” today is just vibes. Prompts get hardened, heuristics stacked, dashboards blink green. But when agents can call tools, stream outputs, or mutate state, vibes don’t scale. You need math wired into the runtime.
Provability Fabric (PF) is a framework for proof-carrying behavior:
Spec-to-Proof layer → ActionDSL policies compile to Lean 4 obligations + runtime monitors
Provable Agent Bundles (PABs) → signed packages binding spec, proofs, SBOM, provenance
Rust sidecar → complete mediation of all events (call, ret, log, declassify, emit_chunk, emit_end)
Egress certificates → every output gets a signed verdict (pass/fail/inapplicable)
TRUST-FIRE test suite → stress tests for chaos, rollback, privacy burn-down, adversarial adapters
The core bet:
“Safety isn’t a setting. It’s a correspondence proof between your semantics and your runtime.”
Would love HN’s take:
Is this the right way to move beyond vibes toward falsifiable guarantees?
Should proof-carrying bundles (like PAB-1.0) become the standard for deploying agents?
What would it take for you to trust an agent in prod — proofs, monitors, or something else?
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