Software & AI · 6 min read
AI agents in production: a governance-first rollout plan
Most agentic AI pilots stall before they reach production. A rollout plan that starts with ownership, permissions and measurement, not with the model.
· Bhargava Group
AI agents are software that can plan, call tools and act on a user’s behalf: open a ticket, update a CRM record, draft and send a reply. That ability to act is the difference between an agent and a chatbot, and it is also why so many agent projects never leave the pilot stage.
In June 2025 Gartner predicted that more than 40% of agentic AI projects will be cancelled by the end of 2027, citing rising costs, unclear business value and inadequate risk controls. None of those reasons is about model quality. They are about how the project was set up.
1. Start with one workflow and one owner
Pick a process that is frequent, well understood and measurable, such as service-desk triage, invoice matching or first-draft proposal writing. Name a business owner who will decide whether the agent is working. An agent without an owner becomes an experiment nobody can switch off.
Write down the baseline before you build: how long the task takes today, how often it goes wrong and what it costs.
2. Give the agent a job description
Treat the agent like a new hire with a narrow role:
- What it may read. Only the data sources the task needs.
- What it may do. An explicit list of tools and actions, each with a limit (for example, may draft an email but not send it; may create a ticket but not close one).
- When it must ask. Actions that move money, delete data or contact customers go to a human for approval.
This is least privilege applied to software that makes decisions. It is also the single most effective control against prompt injection and runaway actions.
3. Build the audit trail in from day one
Log every prompt, every tool call, every approval and every output, tied to the user the agent acted for. When something goes wrong, and eventually something will, you need to answer “what did it do, and why?” in minutes, not days. Logs are also how you prove value.
4. Evaluate before you scale
Create a test set of real cases with known good answers and run it on every change: new model version, new prompt, new tool. Track accuracy, cost per task and the rate of human escalation. If a change makes any of them worse, it does not ship.
5. Expand by evidence
Once the first workflow meets its targets for a full month, add the next one. Reuse the same identity, logging and approval patterns so that every new agent is cheaper to govern than the last.
What this looks like in practice
A well-run first agent typically moves from idea to production in weeks, not quarters, because the hard questions — who owns it, what it may touch, how we know it works — were answered before the first line of code.
If you are planning your first agent, or rescuing one that stalled, our Software & AI Engineering practice runs this exact sequence with your team.