5 Lessons on Running Agentic AI in Production - From the Fireside chat

Agentic AI governance is the set of controls that keep autonomous agents safe, traceable, and cost-bounded in production: cost visibility and limits, unified logging, access control over the tools and MCPs agents can call, and guardrails applied as policy. In practice enterprises enforce it through a central control plane, or AI gateway, that every agent request passes through.

An AI gateway sees all traffic, so it can show spend by user, team, and application, enforce budgets and rate limits before costs run up, and route each request to the right-sized model. That covers the three cost layers: visibility, control, and routing.

An MCP gateway is a central layer that registers your MCP servers and applies tool-level access control, so you can decide which users reach which tools with which authentication. It turns the N-by-M sprawl of clients, servers, and tools into something you can govern and audit from one place.

You route agents through infrastructure that logs and authenticates by default, rather than letting them deploy ad hoc. Design tracing in from the start, since non-deterministic agents cannot be reliably traced after the fact, and require service accounts instead of personal credentials for anything deployed.

Yes. TrueFoundry runs in your VPC, on-prem, air-gapped, hybrid, or across clouds, and no data leaves your domain. That is the main reason regulated enterprises pick it over SaaS-only gateways.

Yes. The gateway is OpenTelemetry-compliant and plugs into Grafana, Datadog, Prometheus, or your preferred stack. It traces every request from prompt to tool and model execution, so you get unified logging without ripping out what you already run.