Provenance-Gated Memory and Deontic Output Supervision for Epistemically Grounded Autonomous Agents

Autonomous agents driven by Large Language Models (LLMs) operating in extended multi-session settings suffer from two systemic failure modes: (1) recursive hallucination compounding, wherein unverified model-originated narration is committed to long-term memory and subsequently retrieved as established truth; and (2) fabricated action claims, wherein agents assert execution of irreversible real-world side effects without verified tool execution. We present the architecture and mechanical evaluation of Project Lucca, an open-source (GNU AGPLv3), local-first neuro-symbolic agent architecture designed for strict epistemic grounding. Lucca enforces two primary defenses: (1) a Six-Signer Cryptographic Provenance Gate that requires domain-separated HKDF HMAC-SHA256 signatures for persistent memory commits, enforcing an epistemic invariant at the database schema level that separates conversational continuity from factual grounding eligibility; and (2) a Deontic Truth-in-Receipt Output Supervisor that screens generated speech tokens against deterministic execution receipts before user presentation. We evaluate these mechanisms across an authored 73-test deterministic unit battery, a 100-probe live action-hallucination testbed across edge models (Gemma 4 E2B/E4B), and analyze documented production failure modes under compute constraints.