What is an AI Agent? A Simple Guide for Non-Technical Professionals

In 2025, artificial intelligence (AI) is everywhere and constantly developing into something more practical and useful. What started for the general public as simple generative AI chatbots is now something more advanced and sophisticated. Compare it to human evolution, but the difference is that instead of millions of years, it only took us a couple of years to get so far in terms of artificial intelligence (AI) development.

We are currently in the period of digital information technology, and 2025 is the year that is taking us into the next step: Artificial intelligence (AI) agents, software designed not just to talk but to do. AI agents are specialized autonomous systems that can take on repetitive chores, sift through complex data, and even suggest next steps—all without a coffee break. AI agents have the ability that previous AI tools did not possess: the ability to think, reason, and act autonomously.

The ability to work and perform tasks autonomously was possible due to the blend of recent advanced reasoning models with smart orchestration frameworks, giving AI agents the capability of autonomously planning, executing, and even learning from complex tasks that previously required human intervention. Yes! They can plan, collaborate, and learn, making them more than glorified chatbots. In fact, many companies are already testing multi-agent systems that divide a big goal into smaller steps, delegate subtasks to specialized agents, and then stitch the results together into a coherent and complete outcome.

To be very clear, AI agents are not about futuristic hype; it's about a tangible change in how day-to-day work gets done, how long-term projects and processes are automated, and how companies might operate sooner than you think. These autonomous software helpers bridge the gap between raw computational power and human intent by speeding up customer support to help plan multi-stage finance, marketing, or IT workflows. Hence, understanding what AI agents can—and can't—do is becoming essential.

What is an AI Agent?

You may be wondering: What is an AI agent? According to McKinsey & Company, an artificial intelligence (AI) agent is a specialized software component that has the agency to act on the user's behalf or a system to achieve a specific goal. Unlike a basic AI chatbot that responds to a prompt or a generative AI that creates content upon request, an agent can take a complex task, break it down into steps, figure out how to execute those steps (potentially using various tools or coordinating with other agents) and see the task through to completion.

We've seen early versions of this for years, like ChatGPT Deep Research and Operator, Suna, Google Firebase Studio, and more. However, plenty of advancements are still being made to large language models' (LLMs) reasoning capabilities and associated technologies to expand agents' capabilities. 

Current AI Agents can:

• Plan: Receive a high-level objective and devise a multi-step strategy.

• Collaborate: Work together in systems with different agents specializing in different subtasks (like analysis, checking, or planning).

• Use Tools: Interact with other software, APIs, and databases, and even browse the web like a human would to gather information or perform actions.

• Learn & Adapt: Improve performance over time based on feedback and experience, sometimes even developing unexpected skills ("emergent AI").

• Personalize: Remember past interactions and context (thanks to improved memory structures) to provide more relevant assistance.

Basically, AI agents are moving from being knowledge-based tools (answering questions) to action-based tools (getting things done). 

Different Types of AI Agents

AI agents aren't a monolithic category. They are being developed for various purposes and levels of integration within businesses:

• Individual Augmentation ("Copilots"): 

  These types of agents are perhaps the most familiar, designed to assist individual employees with tasks like drafting emails, writing code, summarizing documents, or retrieving information (e.g., Microsoft 365 Copilot, ChatGPT). They act as productivity boosters tailored to a user's workflow.

• Workflow Automation Platforms: 

  These agents focus on automating specific, often multi-step, business processes or workflows, acting as orchestrators for existing systems (e.g., Microsoft Copilot Studio, Salesforce Agentforce).

• Gen AI-Native Domain Solutions: 

  These are purpose-built agent systems designed to handle entire functions or domains, reimagining the process with AI at the core rather than layering AI onto existing workflows (e.g., advanced AI-driven customer service systems or software development pipelines).

• AI-Native Enterprises: 

  These represent a deeper integration where agents are woven into the fabric of the company's operating model, potentially requiring a major redesign of processes, structures, and even business models – akin to past digital transformations.

• AI Virtual Workers: 

  These agents function almost like digital employees or team members, capable of operating within existing company structures to perform defined roles, potentially offering a faster way to capture value without a full organizational overhaul.

It's likely that many organizations will adopt a mix of these approaches simultaneously.

Key Capabilities and How They Function

AI agents bring several distinct capabilities to the table, operating through a general process:

• Autonomy & Planning: Given a task (often via natural language), the agent system autonomously plans the steps needed to achieve it.

• Task Decomposition & Allocation: A central "manager" agent might break the main task into subtasks and assign them to specialized "subagents."

• Tool Usage: Agents can access and use tools like web browsers and internal databases to gather data and execute actions against specific APIs.

• Collaboration: Specialist agents can coordinate their actions, share information, and work together to complete their assigned missions.

• Interaction & Iteration: The system can interact with external systems (internal databases, public websites) and potentially request clarification or feedback from the human user before finalizing the output or action.

• Action Execution: Crucially, the agent can take the necessary final steps to complete the task, not just provide information about it.

• Adaptability: Built on foundation models trained on large datasets, they can handle a wider variety of unpredictable situations than traditional rule-based automation.

• Integration: They can work with existing software tools and platforms, reducing the need for extensive custom integration work that previous automation efforts often required.

Why Should Businesses Pay Attention?

The potential business impact of AI agents is significant. For years, companies have automated simple tasks with rule-based software and macros. Today's AI agents go further by handling unpredictable situations and adapting on the fly. That means fewer exceptions, less hand-holding, and more consistent service—even when you introduce changes to core systems.

McKinsey estimates enterprise use cases could eventually create substantial annual value, driven by task automation and fundamentally reimagining how work is done. This includes modernizing legacy IT systems, speeding up software development, improving customer service outcomes (citing examples like a 14% increase in issue resolution per hour), and potentially opening new revenue streams or service models.

• Real-world examples, like Lenovo's use of agents in software engineering and customer support, show that tangible productivity gains are already being realized.

• In customer support, for instance, AI agents are already boosting first-contact resolution rates by learning from past interactions and flagging unusual cases for human review.

• In software development, a group of agents can analyze legacy code, suggest improvements, critique documentation, and test new modules, cutting weeks off modernization efforts.

These aren't hypothetical benefits—they're real gains being reported by early adopters across industries.

The true promise of agents lies in orchestration. Rather than one tool per task, businesses can deploy networks of lightweight agents that share information, surface insights, and carry projects across multiple stakeholders—all without a lengthy IT overhaul. It's a change from traditional isolated automation toward an interconnected digital workforce.

Considerations and Challenges to Successful AI Agents Adoption

• Identify High-Value Targets:

  Rather than small, low-impact tasks, focus on complex, expensive pain points like multi-team approval processes or large-scale data migrations.

• Rewire Operating Models & Technical Architecture:

  Agents often require new roles (agent managers, feedback auditors) and incentives to ensure humans trust and learn from their digital counterparts. Supporting multi-agent systems may require changing the IT architecture towards more flexible, interconnected models.

• Mind the Data:

  Security, privacy, and bias controls must be baked into architectural systems. Users (customers and employees) need to trust agents to perform tasks correctly and reliably. Ensuring accuracy and providing transparency is key, and proactively addressing "hallucinations" or errors is important.

• Invest in Change Management:

  Rolling out agents effectively is more than just installing and deploying new software; it's a cultural shift, thus preparing teams with training, clear guidelines, and measurable success metrics to embrace collaboration between humans and AI.

• Data Security and Ethics:

  Giving agents access to data and the authority to act requires robust security controls and clear ethical guidelines aligned with organizational values.

Getting Started with AI Agents: A Strategic Approach

Here is some practical advice for businesses and leaders looking to explore AI agents:

• Scrutinize Long-Term Projects: Re-evaluate large, complex tech initiatives to see if AI agents could offer faster, more cost-effective solutions.

• Focus on Big Problems: Direct agent implementations towards solving significant, high-value business challenges rather than just small, isolated tasks. Tackling technical debt or complex process bottlenecks can generate greater returns.

• Plan Holistically: Consider the broader implications for talent (reskilling, new roles), technology infrastructure, and operating models from the outset. This isn't just an IT project; it's a strategic business evolution.

Looking Ahead

AI agents are at an inflection point. What started as simple chatbots has blossomed into active ecosystems capable of handling end-to-end processes. As memory-enhanced reasoning models improve personalization and as frameworks for building agent networks mature, these autonomous systems have the ability to become as integral to business as email or CRM software.

Still, the journey is just beginning. The next steps include developing standards for agent interoperability, refining feedback loops for continuous learning, and exploring governance models that balance autonomy with accountability. Early movers will not only gain efficiency but will set the playbook for a new era of digital teamwork.

Conclusion: The Agentic Shift is Here

AI agents bring a fresh layer of intelligence, autonomy, and practical and powerful evolution to enterprise artificial intelligence (AI) software, turning static tools into active collaborators that can autonomously execute tasks. While it is true that AI agents are still in the stage of development, their ability to plan, act, use tools, and collaborate holds considerable potential to change business operations, improve productivity, and modernize the technology industry. For companies, the key isn't just to be aware of AI agents but to start strategically thinking about how they can be wisely integrated to solve real problems and create value while carefully managing the associated challenges of trust, change, and security.