Product @ Pre6 AI · I build production-grade AI agent systems.
Product by title, builder by craft — I design AI products and ship the engineering behind them: multi-agent orchestration, agent evaluation, and AI safety infrastructure.
I care about the unglamorous half of AI products — the part that decides whether they survive contact with real users. Most demos route a single LLM call. Production systems need orchestration, evaluation, safety gates, and observability. That gap is what I build into.
- Multi-agent orchestration — supervisor/specialist architectures with typed state, tool binding, and streaming traces.
- Agent reliability — measurable, auditable evaluation of agent runs across reliability, safety, latency, and cost.
- LLM safety — scanning retrieval context for prompt injection, secret leakage, PII, and exfiltration before it reaches a model.
- Developer tooling — sharp CLIs that turn fuzzy engineering signals into decisions teams can act on.
| Project | What it is | Stack | Links |
|---|---|---|---|
| gemma4-multi-agent | Production-ready multi-agent system — a Supervisor routes work across 4 specialist agents with live reasoning traces and sandboxed tool execution. | Python · LangGraph · Gemini · Streamlit | Code |
| rag-safety-gateway | AI security gateway that scans RAG context for prompt injection, secrets, PII, and exfiltration risk, producing deterministic allow/redact/quarantine decisions. | TypeScript · React · CI | Live Demo · Code |
| agent-evals-lab | Evaluation workbench for agent reliability — typed scoring engine, policy rules, regression detection, and a trace-inspection dashboard. | TypeScript · React · CI | Live Demo · Code |
| repo-pulse | CLI that turns any Git repo into an engineering-health report — churn × complexity hotspot scoring you can paste into a review. | Python | Code |
| contract-watch | CLI that diffs two OpenAPI contracts and flags breaking API changes before they reach clients. CI-friendly. | TypeScript | Code |
| ai-code-reviewer | Structured AI code review from the terminal — severity-rated, line-specific feedback in pretty / JSON / Markdown. | Python | Code |
Every project ships with tests, CI, and documentation — and the AI tooling runs without API keys so anyone can review it in under a minute.
repo-pulse in action — a real, unedited run, no keys or config.
Typed contracts first → domain models before logic, so behavior is auditable
Deterministic by default → scoring and decisions reproducible without a live model
Measurable, then pretty → evals and telemetry before dashboards
Reviewable in 60 seconds → clone, run, understand — no API keys to start
Python · TypeScript · LangGraph · LangChain · React · Streamlit · Google Gemini · OpenAI · pytest · Vitest · GitHub Actions · uv
Open to conversations on AI agent engineering, evals, and LLM safety.