AI systems as infrastructure
Agents, tools, memory, retrieval, and evals inside observable, durable runtimes.
I design production-grade architectures where autonomy, state, and side effects stay explicit, testable, and recoverable.
Reusable architecture components for AI systems that need state, orchestration, retrieval, tools, evals, and recovery.
Delegation, coordination, state transitions, and typed execution boundaries for autonomous workflows.
Episodic memory, semantic retrieval, compression, persistence, and replayable context for stateful agents.
Durable execution, retries, branching, checkpoints, and human-in-the-loop gates for production AI flows.
The operating model behind the work: explicit execution, measurable behavior, and infrastructure-grade reliability.
Prefer bounded execution, failure recovery, and measurable behavior over impressive but fragile autonomy.
Production agents need durable state, replayable context, and lifecycle control, not only prompt chains.
Every workflow should expose traces, decisions, tool calls, cost, and recovery paths.
Autonomy belongs inside explicit gates, escalation paths, and reviewable execution models.
Projects, research notes, and writing are connected around the same architecture primitives.
Concrete systems translated into case studies: problem, architecture, runtime model, tooling, reliability, tradeoffs.
Notes and experiments around memory, orchestration, runtime design, MCP, and agent infrastructure.
Technical articles on production patterns for agents, durable workflows, evaluation, RAG, and stateful systems.