For decades, we’ve been dreaming of a future made better by AI. Our collective vision of intelligent, autonomous, AI-powered assistants has been popularized by films like Her and series like Star Trek: The Next Generation – and now, as we enter the dawn of the AI agent, we’re one step closer to that reality.
Across industries, AI agents will help us raise the bar of productivity and efficiency, proving that tech-human partnerships are not only feasible but fruitful. That is, if everything goes according to plan.
Because that’s the thing – agentic AI is still relatively new. While we’re clear on our aspirations for the new technology, we’re still waiting for observable outcomes at scale. We don’t yet have industry standards or best practices.
While we await this crowdsourced guidance, technology leaders still need a “true north” for their AI compasses; something to aim for as they navigate the path forward with agents. In this regard, leaders should go back to basics.
AI agents’ value is in their ability to independently perform assigned tasks. They are meant to be an extension of a human team.
To ensure AI agents blend well with existing teams and add organizational value, technology leaders should provide clarity on roles and responsibilities, conduct regular performance reviews, and iterate processes based on “lived” agent experience. In other words, they should treat their AI agents like human employees.
Give your agents role clarity
Like human workers, AI agents need guidance on leadership’s expectations of them. Clear parameters must be in place to dictate the kinds of tasks agents perform, the specific systems they’ll use, and the access they’re granted. Whether an agent will be working alone or as part of a team of “chained” operatives, they need to have a clear directive of the specific tasks under their purview.
For example, a retail organization might charge an AI agent with escalating customer service inquiries for a specific line of soft goods.
The agent’s manager will need to specify the product line being covered, the language or action that triggers their intervention, the managers to whom the agent should escalate these cases, the messaging they should use with customers, and more. Every step of the process, every variable, must be accounted for.
Of course, providing agentic role clarity is not a discrete process; it will evolve as organizations observe their agents in action and learn more about their capabilities. To the extent possible, organizations should collect and analyze agent performance data to determine whether the agent is operating as intended. This data will prove invaluable when it comes time to issue the agent’s first performance review.
Conduct regular performance reviews
Building AI agents is challenging, and doing it successfully is an achievement worth celebrating. But after the initial feelings of triumph must come a frank assessment of the technology. First and foremost, leaders need to determine whether the agent is performing as expected.
Even if the agent’s performance isn’t good, it might be good enough for the task at hand. Good enough is a fine starting place, especially for responsibilities that don’t directly impact an organization’s brand reputation or product (e.g., scheduling internal meetings).
Assuming the agent’s performance isn’t perfect, teams should turn to reinforcement learning to help steer agents in the right direction. Reinforcement learning helps not only agents but human teams; it’s only by falling short that we learn how to hit the mark next time.
Providing feedback and conducting agent performance reviews will help everyone – from associates to team leads to CTOs – better understand how to set agents up for success. It can also serve as a bridge of trust for human workers, helping them gain appreciation for the agent and work to support its continuous improvement.
With clean, forensic performance assessments, tech teams can help agents fit into the broader organization and add value. These assessments don’t need to be time-sensitive (especially since organizations likely don’t have very many agents), but they should happen at frequent intervals. Proactive course-correction is the name of the game.
Learn from the agentic experience
Effective leaders seek to not only understand their employees’ experiences but learn from them and adjust processes as necessary – and they should do the same with their agents’ experiences.
The performance reviews mentioned above will do more than give supervisors a chance to help agents improve. They’ll illuminate potential avenues for improvement for internal workflows and tools.
By evaluating an agent’s ability to perform its assigned tasks, tech teams can begin to distinguish the “stated truth” (how they imagine processes will be completed) from the “observed truth” (how they are actually completed).
If leaders discover a surprising gap between the stated and observed truths, they can work to understand the reasons why. It may be that they don’t have the right people or agents in the right roles, that people or agents don’t have what they need to succeed, or that the stated path is simply incorrect.
The discovery process will look different for every organization. What’s important is that leaders take their agents’ experience seriously and treat it like a valuable learning opportunity – just like they would a human worker’s experience.
Agents are here and ready for hire
Agents aren’t a “someday” concept. They’re already on the job. A few agentic roles you might recognize:
- Supplier onboarding agents who run compliance checks, screen tax IDs, and vet new vendors.
- Support agents who process tickets, resolve quick fixes, and escalate more complex cases to human reps.
- Scheduling agents who rid humans of endless email chains, find good meeting times, and put calls on the books.
- Observation agents who monitor workflow bottlenecks and show leaders how training or automation can help.
These aren’t prototypes or sci-fi experiments; they’re already out in the wild, reshaping how teams get work done. The more clearly we define agents’ roles, give them feedback, and hold them accountable, the faster they’ll move from tools we use to teammates we rely on.
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