What’s inside the whitepaper
Many enterprises now run AI agents in at least one business function, but few have changed how work, ownership, and control are structured around them. Agents are often inserted into processes built for people, leaving the same approvals, handoffs, and review queues in place and limiting the value they can deliver.
Drawing on Simform’s experience running agentic systems in production, this whitepaper introduces the Agentic Target Operating Model (ATOM), a six-layer framework for governed, repeatable execution.
Unlike frameworks that stop at enterprise capabilities, ATOM defines the decisions behind each layer, the sequence in which they must be made, and how they translate into practice through a real healthcare workflow and the Microsoft stack.
Designing the operating model for enterprise agentic AI
Once agents begin acting on behalf of the business, the value they create depends on how each layer of the enterprise around them is prepared, connected, and adjusted over time.
Prioritizing agentic AI use cases by value and readiness
Learn why choosing use cases for their visibility or technical appeal weakens agentic programs early, and how business value, context readiness, risk exposure, and change impact determine which workflows should move first.
Redesigning workflows at the decision level
See how breaking a workflow into individual decisions, assigning each its own level of autonomy, and planning escalation and recovery before go-live prevents agents from simply relocating manual effort to a new review queue.
Structuring ownership and the shared agent platform
Understand how an Agent Center of Excellence, domain pods, per-agent identity, and governed tool integration give accountability a form the platform can enforce as the number of agents grows.
Governing context and agents in production
Explore how grounding, memory, and access controls shape what agents are allowed to know, and how evaluation, cost monitoring, and predefined interventions keep them performing within agreed limits long after launch.
3 reasons to read this whitepaper
Written for CTOs, CIOs, and the leaders who own the P&L an agent will touch, this whitepaper is a working model for execution. It reflects a view shaped in production, that autonomy is an operating variable and the enterprises able to scale it are those that decided in advance how it would rise and fall.
See the model applied to a real workflow
Trace a healthcare prior-authorization process through all six layers and see how an early scoring decision shapes the autonomy tiers, agent boundaries, and controls that follow.
Confirm readiness before moving forward
Use the exit checklist that closes every layer to verify that key decisions are documented, required controls are in place, and completion criteria are met before funding or engineering advances to the next stage.
Build once and extend across workflows
Give every new workflow a starting point of proven controls and production evidence, so each deployment is faster to deliver and easier to govern than the last.
