How to Hire your First AI

A growing number of startups are marketing their AI agents as if they were employees you can hire, onboard and manage. This is a shift from how software was traditionally marketed or sold. Traditional software allows you to pay once to own and SaaS software has a recurring charge in return for an evolving product. Hiring software is new in the age of AI agents.

11x, Cody, and kay are all examples of companies that straddle the line between tech and teammate. 11x breaks out the two hireable personas into Alice and Julian. Cody and kay are the names respectively of those two agents. Across those three platforms, there are similarities in approach.

First, you’ll notice that the names given to these agents are more personable. This makes it more natural to hire and interact with these agents. Humans have a natural tendency to be skeptical of paradigm shifts in technology, so this allows for a more familiar way to utilize these teammates. Second, these agents have a focus on training. Across all three web pages the terms train and learn are prevalent. Instead of more technical terms like fine-tune, these agents are “onboarded”. This re-frames the problem as a more familiar one where resources are used on enablement instead of implementation. Finally, these new employees are integrated as always on team members. They are not seen as compute but rather as hard working, 24/7 co-workers. With interfaces such as Slack or email these agents become indistinguishable from other employees.

If we treat these agents as employees, we also want to interview them appropriately. Just because generative AI ROI is estimated at $3.7 dollars per each $1 spent, does not mean that will be the realized amount. The new framing of hiring these agents doesn’t change the fact that onboarding them will be the same rigorous process as any other software in an organization. There is even a higher chance it will be more rigorous as these agents require broad access and may send requests to a third party that contain highly sensitive information. Before continuing, ensure that the agent is the right way to solve the problem you are facing.

Assuming it is, the first thing you should do is a background check and get references from other teammates of these agents. These references should include not just the value of these agents, but also focus on how the user is interfacing with it. This should include what they found helpful when they first started working with the AI agent.

Identify weaknesses as well. Find the edge cases or limitations where the agent doesn’t do as well. Either confirm you can solve for those issues without the agent, or make a request for a feature. Think about what happens if the agent is unavailable or goes down.

In the same way you hire for the future aptitude you may need, the agent will need to evolve as the problem space does. Ask where they see themselves in the next few years. Confirm they can handle all of the shifting context and responsibilities given the role they have to play.

Assuming you do your due diligence, the agent can play an integral role in your organization. Ensure that they get the support they need when they need it. The problems they solve for are efficiency focused, and therefore without this support, they become bloat. Efficiency gains are also better when they are shared. Make sure the agent is easy for many users to make part of their existing workflows. All that is left to say is good luck and may this new agent be a successful addition to the team.

Thanks to Kwabena Ohemeng for review.