Agentic AI · architecture · data · operations
I help teams move agentic AI from demo to production.
The hard part is rarely the model. It is the architecture around it: memory, state, tools, data, evaluation, recovery, and ownership. I design those seams so the system can be trusted when the happy path ends.
Current focus
Production-grade agentic AI reference systems, memory, evaluation, and governed change.
What I help teams do
Make the seams explicit.
Make the production decision
Reconstruct what an agent actually does, test consequential trajectories, and identify what must change before the next release.
Architecture review → 02Make execution recoverable
Design checkpoints, cancellation, idempotent effects, replay, and operational evidence for work that outlives a process.
See durable execution → 03Give agents something true to work with
Build typed memory and data foundations with provenance, scope, versioning, retention, and useful retrieval semantics.
See memory over a lakehouse →Selected proof
A reference build is an argument you can inspect.
Featured reference build
Verified-capability routing for multi-agent systems
Dynamic agent selection based on verified capabilities and trust boundaries, not marketing claims.
Read the case study →
More proof
Built around production seams.
Platform engineeringAgent sandbox sized to blast radiusIsolation, capability-granted mounts, egress controls, and reconstructable audit trails.
Agentic AIEvaluation and observability harnessA typed run ledger for traces, replay fixtures, eval gates, drift signals, and cost attribution.
Data platformSutra Feed GuardDeterministic drift classification and governed change before production data is touched.
Coming next
Exception queues before happy-path automation.
Human-in-the-loop exception handling with structured review workflows. The portfolio should show not just what runs, but how the system behaves when it cannot safely decide.
The garden
Start with a question, not an archive.
Have a production question?