As enterprise AI agents begin handling research, drafting proposals, and investigating incidents, the people responsible for those workflows need to understand how agents reach decisions and where human approval belongs. Moving AI agents from pilot to production requires decisions about the information they can access, the actions they can take, and the cost of keeping them running. Classifying agent types by risk gives teams a shared basis for setting those permissions, while verifiability and blast radius help determine how much autonomy each task can support. Those choices become part of an operating model that gives teams the evidence to assess business value and the oversight to intervene when an agent’s behavior changes.

Written for AI, IT, and security leaders, AHEAD’s Running Agents in Production connects agentic AI architecture and AI governance to the decisions involved in deploying and managing agents. The eBook explains six agent classes and follows familiar enterprise workflows to show how evaluation, security guardrails, and cost controls support the work each agent is permitted to perform. It also describes AgentOps, the lifecycle and runtime management practices through which teams assign ownership, monitor results, and adjust autonomy as production evidence accumulates. Reference architectures for AWS, Microsoft Azure, Google Cloud, and NVIDIA on-premises, together with guidance for the first 90 days, help readers apply these practices to their own environments.

Download the PDF to assess your AI agent use cases and plan the architecture and controls your teams will need in production.