Agentic AI, like generative AI at the end of 2022, opens up an exciting new frontier for business leaders to explore, experiment with, and put to work. Its potential and reach, however, can make it tricky to fully grasp agentic AI’s immediate applications or long-term impact. But with the right perspective, leaders and their teams can start using AI agents to tackle meaningful tasks right away and draw insights from these efforts to guide larger business transformations.
How to think about agentic AI
As the name would suggest, agentic AI is all about “agents.” These agents will become increasingly more autonomous and will be able to complete complex, multistep tasks without the back-and-forth prompting familiar to gen AI. That doesn’t sound very different from the general promise of AI today until you dig into what autonomy really means.
AI agents will be able to autonomously sense and act within their environments. Instead of feeding AI information and instructions, professionals will provide it with an objective. Much like a capable and curious professional, an AI agent will identify the tasks and information needed to achieve the objective. It can even course-correct dynamically based on real-time insights.
This unlocks an entirely new orchestration layer of work that will significantly augment workflows across every aspect of the workplace.
For example, your recruitment team might deploy an AI agent to automate job postings––from end to end. Say you are hiring for a senior software engineer. The AI agent might:
- Search your HR systems to collect standard hiring and company requirements.
- Conduct market research across job sites to analyze the skills, qualifications, and salary expectations of similar positions.
- Draft the job description based on your company’s desired format.
- Optimize the job description so it is more discoverable on search engines and uses inclusive language.
- Route the draft to a recruitment manager for review and approval.
- Use automation tools to publish the role across platforms like LinkedIn, Indeed, or Glassdoor.
As the team grows confident that the agent is accurately and responsibly delivering on that workflow, they might ask the agent to do more. That could include proactively searching for candidates across channels that fit the job description, contacting candidates, and setting up exploratory meetings with recruitment managers. This is a very real agentic workflow companies will start deploying in 2025.
I’ll pause here and point out two things in the example. First, this is just one of the thousands of repeatable workflows that comprise the modern workplace. By handling these time-consuming tasks, the AI agent frees up employees to focus on more strategic and personalized parts of their work. Second, the agent is interacting with sensitive company information, preparing content that might impact a person’s future career, and posting on behalf of the company in public forums. Each one of these steps creates an opportunity for risk and will require teams to test and monitor agents to fully trust them before deploying widely. That brings us to context.
Agentic AI runs on context
Think of using agentic AI as you would hiring a new contractor rather than implementing another piece of software. Similar to the ways you would onboard a new contractor, you would need to connect the AI agent to the tools and systems it needs to do its job, give it a clear picture of what success looks like, and ensure it internalizes the cultural and compliance expectations of the role. This context is the secret sauce for agentic AI’s impact. Whereas people grow more effective in their roles through experience over time, AI agents become more effective through the context available to them.
This context is also essential to establishing the critical guardrails agents operate within. Agents need to understand the ethical, compliance, and security boundaries that are important to your company and its customers. By defining these early on, you create an environment in which the AI works as a trusted partner rather than a risk.
AI agents need context but will also be context creators. Agentic AI will help turn libraries of unstructured organizational information into relevant, timely context for professionals. Take Grammarly as an example here. Many have used it for years as a communication assistant that follows you around, providing helpful recommendations wherever you write. Within that broad context, Grammarly and other AI agents can learn an employee’s preferences, company insights, and workflow expectations to become an intelligence agent that can truly share the mental load with professionals in the modern workplace.
What to do today
I believe agentic AI will significantly transform the way we work for the better. That transformation won’t be immediate, but business leaders can take meaningful steps today to capture early wins while developing their strategies and infrastructure for long-term shifts.
Here are several steps that leaders can focus on today.
Start with the employee experience
Success with agentic AI hinges on employee adoption, experimentation, and innovation. The most impactful early agentic use cases should enhance both the quality of professionals’ work and their overall work experience. As with gen AI, this will require businesses to invest in AI literacy training for employees, create a culture around safe experimentation, and allow employees time to explore these tools. Similarly, the tools you adopt should be intuitive to employees and fit into existing workflows. I recommend identifying and nurturing the early adopters and power users, and then encouraging them to build agents and teach their coworkers.
Identify agentic-ready workflows
Find workflows that are routine, repeatable, and audit-friendly, similar to the previous job posting example. Routine workflows that require multistep tasks with minimal variations are prime candidates for early agentic AI pilots. As a general rule of thumb, the right workflows should save employees significant time without compromising the quality of work. In addition, there should be some “human in the loop,” especially at first, to ensure quality and build trust.
Prioritize security and infrastructure
Safe and responsible use of AI is just as important as the efficiencies it enables. Security measures should build on current AI privacy and compliance standards and extend to managing AI agents much like a member of the team. This involves implementing secure access controls, monitoring agent behavior, and safeguarding sensitive data.
Agentic AI is ushering in a very exciting time for businesses and employees. The long-term potential is significant, as are the early wins AI agents can create for employees and teams. Now is the time to pair aspiration with real-world application to unlock one of the most transformative tools for the workplace.
