Why Every AI Agent Needs an Owner

Everyone is talking about AI agents. Far fewer people are talking about what happens after you deploy one. Over the last two years at Knak, we've learned something that surprised me:

AI agents behave a lot more like new employees than software.

You don't hire someone on Monday, give them access to a knowledge base, and expect them to perform at a senior level by Friday.

Yet that's exactly how many companies approach AI.

They turn an agent on, ask a few questions, get mediocre results, and conclude that the technology isn't performing. Our experience has been the opposite.

The biggest unlock wasn't finding the perfect AI model. It was realizing that successful AI agents need onboarding, training, coaching, feedback, and performance management.

In other words, they need owners.

If you take away one thing from this blog… avoid the organizational mistakes companies can make by treating AI as a tool deployment rather than an operating model change.

As Intercom/Fin notes in its work on AI-powered customer service organizations, AI creates demand for entirely new functions. From AI program leadership and knowledge operations to agent training, governance, and performance optimization. The companies that realize the greatest value won't simply automate existing work; they'll redesign their org charts around the reality that AI has become a new participant in the workforce.

Let’s dig in further with three lessons that stand out for us:

1. AI agents are powerful, but they need care and feeding.

An agent is only as good as the information, workflows, and context you give it.

The companies getting real value from AI aren't treating agents like tools. They're treating them like team members who need continuous development.

2. AI success is not a side quest.

One of the biggest mistakes I see is assigning AI ownership as an "extra project" on someone's already-full plate.

It doesn't work.

The people who succeed are the ones who approach AI with a beginner's mindset. They're curious. They experiment. They aren't attached to how things have always been done.

AI optimization is a real job, not a hobby.

3. Choosing the owner is a bigger decision than choosing the technology.

Everyone obsesses over the platform. I think the bigger bet is the person.

The right owner can make an average agent exceptional. The wrong owner can make great technology look ineffective.

For anyone wondering whether the effort is worth it, our results speak for themselves.

Over the last two years, our AI support agent has:

• Resolved 3,927 customer conversations
• Handled the equivalent workload of 1.44 full-time support team members
• Improved from a 55% resolution rate in its early days to 71.4% today
• Allowed our team to spend more time on complex customer issues instead of repetitive support requests
• Our customers are pleased with their experience!!!

None of those gains came from simply turning AI on. They came from improving the technology, investing in the right people, and treating agent management as a discipline instead of an experiment. In fact, I believe we're about to see an entirely new role emerge: AI Agent Manager.

Someone whose primary responsibility is training, monitoring, optimizing, and scaling AI agents across the business.

We're starting to pursue this more formally at Knak, and I'm convinced it will become a standard role in many organizations over the next few years.

The companies that win with AI won't necessarily be the ones with the best agents.

They'll be the ones with the best agent managers. And if you're wondering whether that investment pays off, our experience says yes: nearly 4,000 customer conversations resolved, a 71.4% resolution rate, and counting.