I build production-grade agentic AI systems — evidence-backed, measurable, and trusted in enterprise workflows.

Let your AI agents talk about each other.

Local-first CLI for evidence-backed cross-agent coordination across Codex, Claude, Gemini, and Cursor. One agent reads another's session with citations and structured evidence — no orchestrator required.

• Dual implementation: Node.js + Rust with identical conformance tests
• Session reads, diffing, comparisons, agent-to-agent messaging, secret redaction
• Context Pack: 5-doc agent-first repo briefing that eliminates cold-start re-reads
• Zero npm prod dependencies, works offline, nothing leaves your machine

Repo: cote-star/agent-chorus

Choose the trust posture before a local tool gets credential-backed access.

macOS-first local trust broker for agent-mediated tool execution. Secrets stay local, wrappers and binaries are trust-pinned, and you choose the trust mode per task.

• Three shipped modes: handoff (env injection), oneshot (bounded run), brokered (per-operation request)
• Swift 6.0, macOS Keychain-backed, JSONL audit trail with enforced preflight
• Fail-closed on drift, hijack, or bypass — defense in depth, not "secure agents solved"

Repo: cote-star/latchkeyd

• Production multi-agent systems and coordination reliability
• LLM/VLM engineering with research-to-production translation
• Fine-tuning, structured generation, and inference optimization
• LLMOps, evaluation, and governance for enterprise deployment
• Local-first agent security and trust infrastructure

Practical field notes on enterprise AI reliability and agent systems:

• The Silo Tax — why multi-agent workflows break
• Why Agents Don't Fail Fast — they fail slow

More at Confessions of a Thoughtful Engineer

• Evidence over assumptions
• Reliability over demo polish
• Measurable outcomes over vague AI claims
• Simplicity first; orchestration only when needed

• LinkedIn
• GitHub
• Substack