I help teams retrofit audit logs, access controls, tamper evidence, and retention rules into AI agent memory systems already in production—so security, compliance, and leadership get real answers instead of vibes.
A fixed-scope engagement that adds auditability, enforcement, and retention controls to agent memory—without a platform rebuild.
Identify where memory is stored, how recall happens, and what would fail an audit review (provenance, retention, access, integrity).
Append-only audit logs, tamper-evident signing, role-based enforcement, retention rules, and quarantine/kill-switch modes.
SQLite or Postgres. Local or VPC-contained. Includes operator walkthrough and an executive-friendly security posture summary.
Most production AI systems fail not on inference quality, but on memory architecture. Here’s how I fix that.
Stateless LLMs lose context between sessions. Users repeat themselves. Context windows overflow.
Persistent memory with cross-session continuity
Basic vector search misses exact matches. Keyword search misses conceptual similarity. Both are too slow.
4-strategy hybrid retrieval (hash + graph + semantic + hierarchy)
No traceability on AI decisions. Can't prove data integrity. Regulatory scrutiny reveals black boxes.
Tamper-evident signatures + hash-chained audit logs
JSON files break at 10k memories. No migration path. Backups are manual. Degradation is catastrophic.
SQLite/PostgreSQL + graceful degradation tiers
AI agents can't share insights. Each instance relearns. No collaborative memory or cross-platform learning.
Shared learning pools + cross-platform coordination
AI has no evolving identity. Can't reflect on its own growth. Values are hardcoded, not learned.
Narrative self-model + reflection engine
I don't just store memories—I architect cognitive infrastructure with enterprise-grade reliability, security, and introspection.
SQLite (embedded), PostgreSQL (enterprise), JSON (legacy) with automatic migration paths.
Hash (exact), Graph (relational), Semantic (conceptual), Hierarchical (filtered).
HMAC record signing, hash-chained audit logs, RBAC policy engine, optional PII scrubbing.
Narrative identity persistence, reflection engines, epoch-based timekeeping, confidence scoring.
Deep expertise in the intersection of cognitive architecture, distributed systems, and AI safety.
Bridging neural embeddings with discrete symbolic representations. Jaccard similarity for exact matches, cosine for conceptual, graph for relational.
Cryptographic hash chains for audit logs, HMAC record signing, and bounded reasoning traces for regulatory compliance without exposing internal CoT.
5-tier degradation system (Normal → Optimization → Selective → Critical → Emergency) with automatic archiving and importance-weighted retention.
Narrative identity persistence, reflection engines for pattern extraction, epoch-based developmental timekeeping, and confidence-calibrated memory decay.
I keep bandwidth intentionally limited. If you're running agent memory in production and need auditability, enforcement, or retention controls, email me with a short scope note.
Based in Ocean Springs, Mississippi • Available for remote & on-site consulting