AI systems need ordinary engineering discipline.

I connect models to approved data and tools, define the outputs they must return, and add the controls needed to deploy and support the workflow.

Orchestration

I define model, tool and workflow boundaries with explicit inputs, outputs and state transitions.

Model and tool selection

I choose local or cloud execution based on task quality, privacy, latency, cost and who will operate it.

Evaluation

I test representative tasks, regressions, grounding and known failure cases, then keep the results reviewable.

Observability

I track requests, tool calls, latency, cost, errors and approval decisions without logging sensitive payloads.

Access and approval

I apply least-privilege access, approval gates and audit trails around external actions and protected data.

Failure and cost controls

I use timeouts, retries, fallbacks, budgets and graceful degradation because model output can fail.

Local or managed models?

The workload decides. I compare task quality, data restrictions, latency, infrastructure cost and team ownership before choosing self-hosted or managed inference.

Discuss an AI system