The Agentic Operating Model
Download the guide to learn how leading teams are building and scaling AI agents in production.
How Enterprise Teams Are Running Production AI at Scale
Shipping agents to production is the easy part. Keeping them reliable, governable, and improving over time is where most enterprise AI programs stall. This guide lays out the Agentic Operating Model — a framework for aligning the people, process, and technology needed to build, test, deploy, monitor, and continuously improve agent systems across an organization.
What You'll Learn
- Why the traditional software development lifecycle breaks down for agents, and what the Agent Development Lifecycle (ADLC) replaces it with — a shorter, evaluation-driven cycle that runs continuously rather than shipping to a gate
- How to structure the three rings of agent builders (platform engineers, domain engineers, and non-technical SMEs) so you avoid the most common failure modes: centralized bottleneck, decentralized sprawl, and engineer-only participation
- What a production-grade evaluation practice actually looks like — from dataset construction and evaluator selection to closing the loop from trace to fix in days, not quarters
- How governance, FinOps, and security get enforced in practice, not just in policy — with specific enforcement points mapped to each risk tier and cost lever
- How a global automotive manufacturer cut agent deployment time from three months to one week, and how a global telecom built governance-first across dozens of regulated entities without rebuilding their platform as requirements changed
Download the guide to see what production agent development looks like when people, process, and technology move together.
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