Assistants that can touch production
Tool calling, write safety, evals and cost control on .NET and Azure
The model decides which tool to call. What that tool may change, what the call costs, and whether it still behaves after a prompt edit all stay in deterministic server-side code.
LCP < 1.5s
Performance First
>99.95% SLA
Reliability
Multi-region
High Availability
What I work with
What I reach for, and what I have run in production.
Agents in Production
The gap between a working demo and a system you can leave running
A demo ends when the model calls a tool and something happens. In production the same agent works on real data, spends money on every call, and has to be stoppable before it does damage. The .NET ecosystem still has thin coverage for that.
Tool Calling
Tools defined over the domain services the product already uses, so the agent inherits their validation and permissions instead of reaching around them.
Write Safety
Reads stay open, writes stay bounded. Each tool declares what it can mutate; anything outside that boundary needs an explicit approval step before it runs.
Evals
A fixed set of cases replayed on every prompt, model or tool change, so a regression shows up in CI rather than in a customer's account.
Cost Control
Token accounting per run and per tenant, with the caps and fallbacks that keep an agent loop from turning into an unbounded bill.
The Work Underneath
The platform and delivery experience the AI work sits on
FinTech Platform Delivery
Built and evolved multi-tenant services processing high-frequency financial data, with delivery guarantees that hold through broker and consumer restarts.
CI/CD Delivery Modernization
Moved legacy delivery pipelines to modern GitOps workflows with safer, repeatable deployments.