AI platform design
Agent orchestration, tool boundaries, RAG/context flows, validation, observability, and governance that survive beyond the prototype.
I design and ship auditable agent systems, governed tool execution, deterministic workflows, and delivery infrastructure. ExNulla is the working proof: architecture, code, deployment, and runnable artifacts in one place.
AI features become useful when they are connected to real workflows and bounded by clear contracts. I build the layer around the model: schemas, tools, state, validation, security, observability, and deployment.
My work sits between applied AI engineering, platform architecture, and operational delivery: turning uncertain model output into inspectable systems that can be tested, operated, and improved.
Agent orchestration, tool boundaries, RAG/context flows, validation, observability, and governance that survive beyond the prototype.
TypeScript, Node, Python, PostgreSQL, CI/CD, cloud deployment, release discipline, and recovery-minded operations.
Schema gates, threat modeling, prompt-injection resistance, audit trails, deterministic fallbacks, and human accountability around model output.
A TypeScript/Node authorization gateway for AI agents: identity and vault-backed credentials, an MCP (Model Context Protocol) adapter, and a 10-scenario policy test suite covering approval workflows, delegation limits, replay-attack detection, and default-deny access control -- schemas, authority boundaries, deterministic gates, audit records, and CI enforcement throughout.
This is the kind of work I want to be known for: making AI useful without making it unaccountable.
ExNulla is a working reference implementation of how I operate: define the problem, lock the constraints, build the smallest useful system, verify it, deploy it, and document the trade-offs.
The goal is simple: make the work legible to the people hiring for applied AI, platform, and systems delivery.