Most conversations about Agentic AI in business are about use cases—still! But those organizations and leaders who are further along on their journey are figuring out the next challenge after value: governance.
How do you manage all those agents in your business? Who builds them? How do you know who has built them (and what they’re supposed to do)? Questions, questions, questions. But they’re much easier to answer before you scale your agentic deployment than in the middle of it or even after the fact. Reason enough to establish a clear system for tracking agents, match control to risk, and measure real adoption to prove value. But where should you start?
That’s why I invited Samantha McConnell, Director of AI Strategy and Product Management at Cox Communications, to join me on “What’s the BUZZ?” and share how she and her team have approached it. Here’s what we’ve talked about…
Create an Agent Registry and a Governance Process
If you’re running AI projects inside a business, you need a place that records what’s been built and who owns it. Some teams create an AI hub and a registry: a central index where every agent, its purpose, status, and contacts are logged. That registry is not just inventory, but the first step to avoid five teams building the same thing.
Not all agents should be treated the same. Split them into two buckets: individual-productivity tools (things someone builds for themselves or a small team) and enterprise-grade agents that serve departments or customers. For personal tools, allow more freedom but encourage using a list of approved platforms and models. For enterprise agents, a formal review by a cross-functional council that includes security, data governance, procurement, and the AI team is required.
Make the registry easy to use. Aim to auto-register tools where possible, and require manual entries for production systems. Capture: owner, scope, data sources, third-party models used, compliance checks, and retirement plan. This reduces duplication, accelerates discovery, and provides a single view of audits and lifecycle decisions without slowing builders unnecessarily.
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Agent projects are different from the multi-year systems IT used to ship. You can get a proof of concept in days or weeks, and a viable first version far quicker than traditional software. That’s good because time-to-value decreases. It’s also risky: many agents won’t survive their first year unchanged.
Accept that speed means some solutions will be short-lived. Build in modularity and clear handoffs so you can replace or rewire an agent when a better off‑the‑shelf product appears. Use a “land and expand” approach: start with a narrowly scoped, high-impact feature—personalized content generation, a seller briefing tool, or a brief creation assistant—and expand outward once you validate value and adoption.
Make pragmatic bets about when to build vs buy. Sometimes building a quick POC is worth it to learn and to serve users now; sometimes waiting for a third-party tool makes sense. Either way, plan your architecture so components can be retired or swapped without a full rewrite. Treat agent releases like experiments: short cycles, clear hypotheses, and scheduled reviews to decide evolve, replace, or retire.
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Measure Adoption and Tie Agents to Business Outcomes
A great agent that nobody uses doesn’t move the needle. So measure adoption from day one. Start with meaningful user metrics: active users meeting a usage threshold (not one-off trials), frequency of use, and retention. Compare usage of the agent versus legacy workflows to see if the tool truly changes behavior.
Beyond usage, tie agents to business metrics that matter to stakeholders. For a B2B marketing agent, track content throughput, personalization rate, and impact on engagement or pipeline. For seller-facing tools, measure seller preparedness, time saved in briefs, and close-rate changes. For general productivity tools, quantify time reclaimed and what people do with that time—more creative work, more customer time, fewer manual tasks.
Adoption often needs nudges: clear onboarding, templates, and change support. If adoption stalls around 50%, dig in and fix UX issues, improve prompts, or refine integration points. Use the registry and governance council data to see where overlapping tools exist and consolidate to boost adoption and reduce spend. Good upfront measurement turns anecdote into numbers you can take to leaders.
Summary
You can build agent-based tools quickly, but speed without structure leads to duplication, shadow projects, and unclear value. Start with a clear registry and a governance process that treats personal tools differently from enterprise systems. Move fast with small, testable bets, and plan for short lifecycles and component swaps. Finally, measure adoption with meaningful user thresholds and connect agent outcomes to business metrics so you can prove impact.
What you can do now:
Set up a simple registry or inventory for agent projects and require entries for any tool used by multiple people.
Define two paths: lightweight approval for personal productivity tools and a formal review for department-level or customer-facing agents.
Launch a one-week pilot that targets a measurable outcome, decide success criteria up front, and schedule a six-month review to decide extend, replace, or retire.
Take these steps and you’ll reduce duplicate work, get usable tools into hands fast, and be able to show business leaders where agents deliver real value.
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Andreas, this is a timely and essential guide. The transition from "use case exploration" to "governance and scalability" is exactly where many enterprises are struggling right now. Your point about the Agent Registry resonates deeply with my background in Kaizen—without a clear "standard" or "visual management" of what exists, we cannot hope to improve or eliminate waste (Muda). I particularly liked the land-and-expand approach; it mirrors the Kaizen philosophy of starting small, validating value, and then scaling the standard. I'm currently exploring how to embed these Kaizen principles directly into Agentic workflows to ensure that governance doesn't become a bottleneck, but a catalyst for continuous improvement. Great insights!
This article comes at the perfect time! Spot on about governance being the next big hurdle after use cases. I realy loved the idea of splitting agents. Could you elaborate a bit more on how the risk profiles differ between individual-productivity tools and enterprise-grade agents, especially regarding data privacy?
Andreas, this is a timely and essential guide. The transition from "use case exploration" to "governance and scalability" is exactly where many enterprises are struggling right now. Your point about the Agent Registry resonates deeply with my background in Kaizen—without a clear "standard" or "visual management" of what exists, we cannot hope to improve or eliminate waste (Muda). I particularly liked the land-and-expand approach; it mirrors the Kaizen philosophy of starting small, validating value, and then scaling the standard. I'm currently exploring how to embed these Kaizen principles directly into Agentic workflows to ensure that governance doesn't become a bottleneck, but a catalyst for continuous improvement. Great insights!
This article comes at the perfect time! Spot on about governance being the next big hurdle after use cases. I realy loved the idea of splitting agents. Could you elaborate a bit more on how the risk profiles differ between individual-productivity tools and enterprise-grade agents, especially regarding data privacy?