Software systems that use large language models to perceive their environment, make decisions, and take actions toward a goal with varying degrees of autonomy.
An AI agent is a system that does things, not one that talks about doing things.
The architecture follows a loop: perceive the environment, reason about what to do next, take an action, observe the result, repeat. Agents use large language models for the reasoning layer and connect to external tools for the action layer. Those tools might be APIs, databases, file systems, web browsers, or other software. The agent decides which tool to use, when to use it, and what to do with the result.
What separates agents from automation
Traditional automation follows a script. Step 1, then step 2, then step 3. If something unexpected happens at step 2, the script fails or skips it. An AI agent can evaluate the unexpected result, adjust its approach, and try a different path. That adaptability is the core distinction.
What most people get wrong
The word “agent” has become a marketing label. Vendors rebrand chatbots, RPA bots, and workflow builders as “AI agents” to ride the hype cycle. A useful filter: if the system cannot change its own plan based on intermediate results, it is automation with a chat interface, not an agent. The ability to reason and adapt at runtime is what makes an agent an agent.
The enterprise reality
Most organizations in 2026 are running AI agents in some capacity. Few are governing them. The result is agents built by different teams, using different models, connecting to different data sources, with no central visibility into what they can access or what decisions they are making. The technology works. The organizational readiness usually does not.