The governance layer
The controls that make your AI intent enforceable.
Six pillars, one production pipeline. Each is built around your regulatory, residency, cloud, and cost constraints, and each is the place a decision from the approach becomes something a machine enforces.
AI Gateway architecture & build
A governed control plane for LLM traffic: central authentication, rate limiting, per-team and per-app cost attribution, semantic caching, load balancing, and multi-cloud backend federation, routing to Azure OpenAI, AWS Bedrock, and GCP Vertex via workload-identity / OIDC federation. Whether that plane is one gateway or several feeding a shared one is a function of your constraints, not a default.
Guardrails, content safety & PII
PII anonymization versus hard-block per entity type, custom regex for domain- and region-specific PII, semantic denied topics, prompt-injection and jailbreak detection, separate input versus output screening, and contextual grounding for RAG. Build-vs-buy advisory across native cloud tools and third-party engines.
Agent observability
Framework-agnostic OpenTelemetry piped to Langfuse, Logfire, or Application Insights, with thin wrappers so any team, Pydantic AI, LangGraph, Microsoft Agent Framework, gets tracing, cost, and latency for free.
Agent evaluation
Capability and regression suites, code / model / human graders, outcome-versus-transcript grading, and annotation queues that let non-technical domain experts score traces, all wired into CI.
This is where the judgment layer becomes literal: human judgment, from the people who actually own the domain, wired into the production loop, not a one-off sign-off.
Secure agent CI/CD & enablement
Enforce gateway use over direct API calls, required cost-tracking headers, and mandatory OpenTelemetry logging, with GitOps deployment of agents on Kubernetes and the developer enablement to make the governed path the easy path.
Securing coding assistants
Securing coding assistants such as Claude Code and Copilot in regulated organizations: managed policies, layered context hierarchies, DLP, and evaluating whether that context actually steers the agents in practice.