What an AI agent actually is, and where the hype ends

The word "agent" has been attached to so many products in the past two years that it has nearly lost meaning. Salesforce calls its Agentforce platform a suite of autonomous agents. Microsoft embeds "Copilot agents" into Teams and SharePoint. OpenAI markets operator-style agents through its APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.View full definition →. Strip away the branding and a real technical concept sits underneath, one that is genuinely different from a standard chatbot and genuinely more complicated to run well.

The concept worth understanding is this: an AI agent is a software system that uses a language model to decide, in a loop, what action to take next, executes that action against real tools or data, observes the result, and then decides again. The loop continues until the task is done or the agent gives up. That is the whole thing. Everything else is marketing on top.

Why it matters for managers and decision-makers

Most interactions with AI at work are single-turn: you type a prompt, you get an answer, you copy it into a document. That model works well for summarisation, drafting, and quick analysis. It breaks down when the task requires multiple steps, conditional logic, or access to live systems.

Imagine asking an AI to analyse your company's customer churncustomer churnChurn rate is the percentage of customers or revenue lost over a period. It measures how fast a business loses its existing customer base.View full definition → for Q3, pull the relevant Salesforce records, cross-reference them against support ticket volume in Zendesk, and draft a one-page memo with recommendations. A chatbot cannot do this. It has no access to those systems and no ability to decide what to do when the Salesforce API returns an error. An agent, properly built, can handle each of those steps sequentially, adapting as it goes.

This matters because the productivity ceiling of single-turn AI is real. Companies that have deployed agents for structured workflows, including JP Morgan for document review and Klarna for customer service routing, report that the value comes specifically from automation across multiple connected steps, not from any single AI response being better. The distinction between "AI that answers questions" and "AI that completes processes" is the line between tool and agent.

How it actually works: the mechanics

At the center of any agent is a reasoning loop. The language model receives a goal and a description of available tools. It produces a plan, often just one immediate step. The surrounding software executes that step, whether that is a web search, a database query, or an API call. The result comes back to the model. The model decides what to do next.

A concrete example: you ask an agent to find the three most recent analyst reports on semiconductor demand and summarise the key disagreements. The agent calls a web search tool, reads the returned URLs, decides which look credible, calls a document-reading tool on each, synthesises the content, and produces a structured output. If one URL times out, it retries or skips. You see only the final memo.

The tools available to an agent can be almost anything with an API: calendars, code execution environments, internal databases, email, browser control. The model does not need to be specially trained on these tools; it needs a clear written description of what each tool does and what inputs it expects. This is why the quality of tool descriptions matters enormously in practice and is frequently where real deployments fail.

There is also a memory question. By default, language models have no memory between sessions. Agents can be given short-term memory (the ongoing conversation), longer-term memory stored in a vector databasevector databaseA vector database stores data as high-dimensional numeric vectors (embeddings) and retrieves items by similarity rather than exact matches, powering semantic search and AI applications.View full definition →, and access to structured records. Managing what the agent remembers and forgets is a design decision with real consequences for both accuracy and data privacy.

The role of reasoning models specifically

Standard language models produce a response in one forward pass. Reasoning models, like OpenAI's o-series or Google DeepMind's Gemini 2.0 Flash Thinking, generate an internal chain of thought before producing output. For agent tasks that require multi-step planning or error recovery, this matters. A reasoning model is more likely to catch its own mistakes mid-loop and adjust. For simple tasks, the additional compute cost is not justified. Matching model type to task complexity is a real engineering decision, not a default.

When to use agents, and when not to

Agents are worth considering when the task is multi-step, when it requires real-time data the model cannot have memorised, and when the cost of a wrong action is recoverable. Document research, competitive monitoring, structured data gathering, and code generation pipelines are good candidates. The economics can also work: an agent running a four-hour research task overnight at API cost may replace several hours of analyst time.

Agents are a poor fit when the task is genuinely simple, when errors are hard to reverse, or when the system needs to be auditable at every decision point. An agent deciding to send emails to customers, delete database records, or execute financial transactions is an agent where a single reasoning error has consequences that compound. In those domains, the right architecture is a human reviewing proposed actions before execution, which is sometimes called a "human in the loop" design and is not a limitation so much as a sensible constraint.

The failure mode to watch for is what practitioners call "agent drift": the model pursues a plausible-looking path that diverges from the actual goal, spending compute and API calls on actions that do not contribute to the outcome. This happens more often with vague goals and with long task horizons. The fix is to decompose goals more tightly and to set explicit checkpoints where a human or a secondary model reviews progress.

One more honest point on the current state: most vendor demos show agents completing tasks cleanly. Production deployments are messier. Tool calls fail, APIs change, models hallucinate tool syntax. Teams building agents seriously at firms like Palantir and Accenture's AI practices report that the engineering work around reliability, retry logic, and error handling often exceeds the work on the model itself.

Agents are a real and useful category of AI system. They are also demanding to deploy well, and the gap between a polished demo and a reliable production workflow is larger than most vendor materials suggest. The practical question is not whether agents are impressive but whether a specific workflow has the structure, the tooling, and the error tolerance to make one worth building.