Agentic AI: Five Best Practices for Moving from Pilot to Production

An AI agent retrieves information, analyzes a request, and proposes an action in a business application. The demonstration succeeds. The business sponsor wants to accelerate deployment.

Yet several questions remain unanswered. Who is accountable when the agent makes a mistake? Which actions can it take independently? Who provides support? How will the team verify that a model update preserves service quality?

These questions determine whether an experiment can become a service people rely on every day. They belong at the start of the project. For CIOs, the challenge is to establish a repeatable path from exploration through industrialization to ongoing operations. These five practices provide a starting point.

1. Establish accountability before expanding scope

An agentic service brings together business teams, Data, AI specialists, and IT. Effective collaboration requires explicit decision ownership.

The business defines the expected outcome and process rules. Data teams own data quality and meaning. AI specialists evaluate agent behavior. IT manages integration, reliability, and operations.

Best practice: Appoint one end-to-end product owner, then identify who has authority over each critical decision: approving business rules, authorizing production release, changing autonomy levels, or retiring the service.

Shared delivery should still give everyone a clear answer to one question: who decides?

2. Define what the agent is allowed to do

Autonomy needs to be defined action by action. Reading information, preparing a response, and modifying a customer record carry different consequences.

Consider an agent handling purchase requests. It might gather information and prepare a recommendation. Spending limits, access permissions, and mandatory approvals should be enforced by the surrounding system, independently of the model’s reasoning.

Best practice: Create an operating contract covering permitted actions, required human approvals, conditions for abstaining, and recovery procedures when something goes wrong.

This makes autonomy a deliberate business decision with enforceable boundaries.

3. Plan for operations during experimentation

Pilots need room for rapid learning. However, decisions deferred for too long can make production unnecessarily difficult: unclear access rights, missing activity logs, fragile dependencies, or undefined quality standards.

Involving operations and security teams early helps surface these issues while changes are still manageable. Requirements should remain proportionate to the use case’s risk and business importance.

Best practice: Define the evidence required at each stage. Exploration establishes value and feasibility. Industrialization establishes readiness for a bounded service commitment. Operations tracks reliability, adoption, and value over time.

Stopping a weak use case should remain a legitimate outcome.

4. Build shared foundations that accelerate future delivery

Identity, tool access, evaluation, monitoring, and cost controls recur across agentic services. Rebuilding them for every project duplicates effort and complicates support.

An AI Delivery Factory provides an organizational framework for addressing these recurring needs. It combines shared foundations and delivery practices with product teams that remain accountable for adoption and business outcomes.

Best practice: Start with one or two real use cases, extend existing enterprise platforms, and progressively share components whose usefulness has been demonstrated.

The approach should grow through delivery experience, with each product improving the foundations available to the next.

5. Measure the service delivered

Pilot counts show experimentation activity. Investment decisions need evidence of actual performance: successful tasks, necessary human intervention, incidents, sustained adoption, and cost per successful business outcome.

These measures help reveal whether a service improves work and remains worth operating.

Best practice: Agree on a small set of business and operational measures during scoping. Reassess them after significant changes to the model, tools, or business process.

Production is the beginning of continuous evaluation.

Make the next pilot an investment in future delivery

Before funding another use case, ask: what will this project contribute that makes the next service easier to deploy and operate?

That question encourages teams to build reusable controls, evaluation methods, components, and skills. It also gives the CIO a practical way to connect innovation with operational discipline.

Explore the full framework: In Agentic AI Has Outgrown the Data Lab: Why Enterprises Need an AI Delivery Factory, François Bossière examines how Business, Data, AI, and IT can organize delivery across exploration, industrialization, and operations.