AWS, GenAI, RAG, agents, and platform systems that work in a demo but do not yet survive real users, real load, and a real bill. Each area below links to the case studies and writing that show the work, not just the claim.
The model is rarely the whole problem. RAG fails silently when chunking, retrieval, and cache invalidation are wrong. I design retrieval quality, source attribution, evaluation, and cost control, not just prompts.
Bills climb a few percent a month until someone opens a finance ticket. The right order is visibility first, cleanup second, right-sizing third, commitments last. I will not promise blanket savings without billing and utilization data.
I will not suggest Kubernetes when Lambda, SQS, and Step Functions solve the problem with less operational load. Strangler-fig migrations, idempotent event flows, DLQs, and hot/cold path separation, chosen for the workload, not the trend.
Good platform work reduces decisions for product teams, not adds a system they are afraid to touch. Terraform/CDK with plan gates, one-click rollback, and drift control. Rollback is architecture: state is what breaks it.
Bounded workflows, not AGI demos. The best AI workflow still needs human gates, per-request cost ceilings with a kill-switch, tool-permission boundaries, and observability over every agent decision. The failure mode I will not allow: an agent that burns the budget overnight.
The system exists, the team inherited it, and nobody trusts it yet. I start with ownership and access, then how it deploys, what the tests cover, and what it costs, and I put the keep, fix or replace decision on evidence before more money is spent. The buyer here is the CTO with a deadline, not the engineer with a preference.
Send the architecture, the AWS bill, the deploy pain, or the GenAI reliability issue. I find the first real bottleneck and propose a small, reversible fix.
Request the reliability audit