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I’m calling it now—2025 is the year of the AI agent. These autonomous systems can actually make decisions and perform actions without human prompting, so it makes sense that savvy business leaders are looking to integrate them into their operations.
But like all things AI, implementing agents into your workflow won’t happen overnight. Having recently built and released our own AI agent to the world, I can say with confidence there are several steps you don’t want to skip.
Identify Your Business Needs
The last year was one of AI mania—and not all of it was good. (Perhaps you heard of the $3,500 AI-enabled toaster?) But for every poorly-rendered image and flat LLM-authored blog post, AI has truly changed the way we work. Its capacity for analyzing massive amounts of data and automating complex business processes has been revolutionary, and it’s tempting to adopt every tool in sight. As Phu Nguyen, the head of digital workplace at Pure Storage, told the Wall Street Journal: “Why should executives be the only people that have a ghost writer that writes their emails or does their slides? Imagine, now, all employees have that power?”
However, before you add agents to your toolkit, it’s crucial to understand exactly which pain points you’re trying to solve. Do you need to improve customer service response times? Resolve operational bottlenecks and optimize supply chains? Defining your problem first and foremost will help you seek out the appropriate solution.
Pick Your AI Agent
Not all AI agents are the same, nor do they perform the same function. You wouldn’t use a hammer to tighten a screw, right? Once you’ve clarified your pain points, it’s important to learn the differences between AI agent types and the jobs they’re built to do. Here are the three most common:
Collaborative AI agents actually involve multiple agents working together to execute a task. AirOps, for example, is a “content orchestration system” that employs several tools working in concert to research, strategize, and produce high-quality, SEO-friendly content, overseen by a real human to ensure quality control.
Automation AI agents are able to perform entire tasks and processes with little (or no) human intervention, using machine learning and predictive analysis to eliminate time-consuming and tedious busywork. For example, Otter.ai’s agent, Otter Pilot, automatically connects to virtual meetings, records and transcribes its contents, then generates a meeting summary with a list of action items, all of which can be automatically shared via Slack or email.
Lastly, social AI agents interact with people. They’re capable of handling customer support requests and scheduling appointments, but more than that, they eliminate the need to scour the web digging for answers. Say you want to plan a vacation, but you don’t know where you want to go. With social agents, you can list all of the criteria you want out of a trip: Kid-friendly, all-inclusive, within a certain distance of your home, and within a certain budget. A social agent will use your criteria to come up with suggestions, sparing you the pain of reading a hundred different reviews across several different websites.
Building And Releasing Your AI Agent
For non-technical founders, the process of building an AI agent might seem overwhelming. The good news is, there’s already an abundance of step-by-step no-code guidance online for those without an engineering background. A white paper recently released by Google also helpfully highlights two platforms: LangChain, an open-source framework, simplifies the AI-building process by allowing developers to connect LLMs to external data sources; there’s also Vertex AI, which allows users to train, deploy and customize AI models and applications, freeing up developers to focus on building and refining their agents.
Once you’ve built your agent, deploy it gradually so you can check for any bugs or other issues before releasing it to a wider audience. As with any launch, be sure to collect user feedback, and make the necessary changes iteratively. As Google’s white paper notes, “no two agents are created alike due to the generative nature of the foundational models that underpin their architecture.” Especially in these early days, experimentation and refinement are key for leaders to figure out what tools they need to address their specific challenges.
AI agents are quickly gaining traction, and their relevance is only going to continue to grow. Nevertheless, Mark Purdy points out in Harvard Business Review that, despite agents’ powerful ability to reason and execute, they still require human oversight. “Just as in traditional, human workforce settings, managers must still pay heed to issues of team composition and role selection, and they must set the right overall goals to ensure that agentic AI or hybrid teams can be successful,” he says.
Ultimately, AI agents are powerful tools that will transform businesses. Still, they require a clear sense of purpose, consistent oversight, and a commitment to responsible deployment. By carefully evaluating your pain points, choosing the right agent for the job, and continuously refining your approach, AI agents will unlock the potential of your organization in 2025 and well beyond.