The term “AI agent” gets thrown around a lot. Vendors slap it on anything with a chat interface. Let’s cut through that and talk about what agents actually are from an engineering perspective.
A working definition
An AI agent is a system that takes a goal, decides how to accomplish it, and executes actions autonomously using external tools and data sources. It doesn’t just generate text. It does things.
The key word is autonomy. A chatbot waits for you to type something and responds. An agent receives an objective, plans a sequence of steps, executes them, observes the results, and adjusts its approach until the goal is met or it determines it needs human input.
The core loop: observe, reason, act
Every agent, regardless of framework or architecture, follows the same fundamental cycle:
- Observe — receive input from the environment. This could be a user request, an event from a system, or the result of a previous action.
- Reason — analyze the current state, determine what needs to happen next. This is where the LLM does its work: interpreting context, weighing options, forming a plan.
- Act — execute a concrete action: call an API, query a database, send an email, update a record, generate a document.
- Reflect — evaluate the outcome. Did the action succeed? Is the goal met? Should the plan change?
This loop repeats until the task is complete or the agent escalates to a human.
What agents are not
Not chatbots. Chatbots are stateless request-response systems. You ask, they answer. Agents maintain goals across multiple steps and take real actions in external systems.
Not RPA. Robotic process automation follows rigid, pre-defined scripts. If the UI changes or an unexpected condition appears, the bot breaks. Agents handle variability because they reason about what to do rather than following a fixed path.
Not traditional automation. A cron job that runs a script every hour is automation. An agent that monitors a data pipeline, identifies anomalies, diagnoses the root cause, and applies a fix is something qualitatively different. The distinction is judgment under ambiguity.
The capabilities that matter
Tool use. An agent without tools is just a chatbot with delusions. Production agents connect to APIs, databases, file systems, and third-party services. The quality of tool integration — clear schemas, robust error handling, proper authentication — determines how reliable the agent is in practice.
Memory. Agents need to remember what they’ve done, what’s worked before, and what context matters for the current task. Short-term memory covers the active task. Long-term memory — stored in vector databases or structured knowledge bases — lets agents learn from past interactions and accumulate domain knowledge.
Planning. For anything beyond a single-step task, agents need to break work into subtasks and sequence them. Good planning means the agent doesn’t waste tokens on dead ends and can recover when a step fails.
When agents make sense
Agents work best when three conditions are true:
- The task involves judgment that can’t be fully captured in rules
- The task requires multiple steps across different tools or data sources
- The task is repeatable enough to justify the setup cost but variable enough that hard-coded automation breaks
Common examples: processing complex documents, triaging support tickets, reconciling data across systems, monitoring and responding to operational events.
When they don’t
If a task is fully deterministic, a script is cheaper and more reliable. If a task requires deep creative judgment, you need a human. If the cost of errors is catastrophic and unrecoverable, you need a human in the loop — and you should be honest about that upfront.
Agents are a powerful tool. They are not magic, and they are not a replacement for thinking clearly about what your systems need to do.