A chatbot can answer; an agent can attempt a task
Ask a chatbot to draft a meeting agenda and it returns text. Ask an agent to prepare for the meeting and it may search your calendar, read permitted documents, draft the agenda, and check whether it included the right people. The difference is not that the second system thinks like a person. It is that software gives the model a task, tools it may call, and a loop for observing results before deciding what to do next.
That extra reach makes agents useful and risky. A mistaken paragraph in chat can be corrected before you use it. A mistaken calendar edit or file change may affect someone else. The design question is how to make the system useful while keeping its permissions, checks, and approvals matched to the task.
The plan, action, observation loop
An agent begins with a goal and some context. It may break the goal into steps, choose an available tool, call it, inspect the result, and decide whether to continue. Research such as ReAct explored interleaving reasoning with actions against external sources instead of forcing a model to answer entirely from its first guess.
Imagine the meeting request again. A first step could read the invitation. A second could search notes about the project. A third could draft a concise agenda. A final step should verify the date, participants, and links. If the notes are missing, the agent should not invent a project update to fill the gap.
Tools are the bridge to the outside world. They can read files, search the web, query a database, or change a record. Each tool has an input contract and a result. The model chooses how to use them, but the surrounding application decides which tools exist and which actions are allowed. That boundary matters more than whether a prompt calls the model “autonomous.”
Agents and fixed automations solve different problems
A fixed automation follows predefined rules. If every invoice arrives in a known format, a deterministic program may extract fields and route them more reliably than a language model. An agent can help where the next step depends on messy text, unexpected documents, or several possible paths. It trades predictability for flexibility.
That tradeoff should shape deployment. OWASP’s agent-security guidance recommends limiting tools and permissions to the task and checking consequential actions independently of the model. A system that summarizes support tickets can often operate with read-only access. A system that issues refunds needs transaction limits, identity checks, and a clear point where a person approves the action. Giving both systems the same broad permissions would be hard to justify.
Agents may also call other services with imperfect data. A search result can be stale, a website can contain malicious instructions, and a tool can report a partial failure. The agent should treat tool output as evidence to evaluate, not a new authority that can rewrite its instructions.
Verification is part of the work
A tool call succeeding does not prove the task succeeded. Sending a draft to a document editor might create the wrong file. A web search might find a similarly named company. An agent must compare the result with the original goal and, where possible, inspect the state after a change.
For a reversible action, a useful pattern is to prepare a concrete change, show it for review, and apply it only when the stakes warrant. For routine read-only work, the agent can proceed further. The approval boundary should reflect the consequences, not a blanket belief that more autonomy is always better.
Longer tasks create another problem: context can fill with tool outputs and earlier attempts. A robust system keeps track of what it has actually verified, where it remains uncertain, and what has already changed. Without that discipline, an agent may repeat an action or claim completion after only drafting a plan.
Set a boundary around the action
Consider an assistant asked to arrange a meeting. Reading calendar availability is one permission; creating an event is another; emailing outside participants is a third. A sensible design lets it draft a proposal, show the invitees and time zone, and obtain a check before sending. The assistant can still save work by finding common windows and composing a clear invitation. The human checkpoint belongs at the action that changes someone else’s calendar.
For that meeting task, keep three decisions separate: whether the proposed time works, whether the invitation contains the right information, and whether it may be sent. Reading a calendar can answer part of the first question; it cannot authorize the last. The agent-security walkthrough applies the same distinction to a support ticket, an outgoing message, and a record change.
What to ask before trusting an agent
The most useful questions are concrete. Which systems can it access? Can it only read, or can it write? What does it do when a tool fails? Can it show the source of a claim? How does it prove a change took effect? Who can stop or undo the action?
Return to the meeting example when assessing a demonstration. Open the resulting agenda or invitation and compare its date, participants, and links with the request. Our guide to giving an agent a task you can verify shows how to write those finish conditions before the agent starts. Adaptability is useful only if the workflow leaves you able to tell what it actually accomplished.
