Start Small, Then Scale
A practical, repeatable approach to adopting agentic AI involves understanding your current agent landscape, proving value with a measurable use case, and applying what you learn as you scale.
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1. Assess the current agent footprint
Inventory the agents that are reaching your systems today. You might find unexpected agents inside existing software that hasn't gone through an AI evaluation.
As part of this discovery process, establish the authority that each agent acts under.
Completing this step first is important. You need to understand the current landscape before optimizing it.
2. Choose a process with outcomes you can evaluate
In this step, your goal is to start with a use case for which you can clearly judge the outcome.
To get started, identify a business process that you understand well. Implement this use case using agentic AI tools, including exposing its systems as governed tools and deciding the approvals and limits.
Often, the biggest hurdle is organizational: Understanding where approvals belong, which steps must not vary, and how much autonomy the organization is comfortable granting. Future use cases will benefit from the rules that you establish.
Even if this specific project stalls, the time you invest in creating the tools and governance will be applicable for future projects.
3. Learn, then iterate
Identify the next process, and review the first use case. Carefully consider the components you can reuse, such as connections, tools, policies for the MCP gateway. Think about what you learned about where approvals belong and which steps must run exactly as defined. The governance work from your first use case now pays dividends.
Next steps
You've reached the end of the story. Ready to get started? See Get Started with Agentic AI.