AI agents explained: when AI goes beyond answers and gets things done

You are planning a weekend away. AI suggests places, explains the journey and produces a lovely packing list. Useful, certainly. Yet you still have several tabs open, checking opening hours and moving details into your calendar. Much of the actual work remains.

This is where AI agents become interesting. With suitable tools and permissions, they can carry out several steps and deliver something usable: a researched shortlist with sources, an edited spreadsheet or a prepared plan. That changes everyday work. It also changes responsibility. A system allowed to act can change more than one that only offers suggestions.

Our view is that the best starting point is a small task with a visible result. “Organise my entire life” sounds impressive. “Compare these three options and prepare a decision draft” can actually be evaluated. This article explains what matters. Its examples are hypothetical, rather than claims about customer experiences.

A chatbot initially responds to your message. An agent pursues a goal across several steps: it selects a tool, uses it, checks the feedback and decides what needs to happen next. The language model provides interpretation and decisions. Tools make information access or file editing possible.

Anthropic’s engineering explanation distinguishes predefined workflows from agents that choose the next step dynamically. In practice, the boundaries overlap. A chat product can include tools and agentic features; a product labelled “agent” can follow a narrow sequence. The label does not answer your most important question: which task can the system complete, under which conditions?

Imagine a research task. A simple workflow always opens the same three sources. An agent might notice conflicting information, look for an additional primary source and correct the comparison. That adds flexibility while making its behaviour less predictable. Extra freedom is useful when it produces a better result that you can check.

Anthropic: Building effective agents (19.12.2024)

For travel planning, a chatbot might explain how to compare hotels. A fixed workflow could sort three specified offers by price. An agent could research available information, apply your criteria and investigate unclear cancellation terms. Booking remains a separate step requiring supported functionality and your permission.

The same logic applies at work. AI can explain how to build a revenue overview. A tool can perform a clearly defined calculation. An agent could combine approved files, recognise different columns and prepare an overview with documented assumptions. Actual capability depends on formats, integrations and permissions.

These distinctions help you choose a suitable solution. For an identical recurring calculation, an ordinary formula is often preferable. An agent may help with a messy task involving changing information. Adding another AI loop does not automatically improve quality.

As of 9 October 2026, OpenAI describes ChatGPT Work as an agent for longer tasks and finished deliverables, including research, documents, spreadsheets and presentations. Its current help page states that the previous ChatGPT agent mode is no longer available. Product names change; the practical idea remains: AI works towards a result across multiple steps. Access and tools depend on the subscription, permissions and environment.

A finished spreadsheet is easier to assess than a promise that “everything is organised”. Useful results include their sources, visible assumptions and an explanation of unresolved gaps. A completed file without the origin of its numbers is not a dependable decision document.

Software development can also involve agents, as discussed in our existing Codex article. For this introduction, the more useful point is that agents are relevant beyond programming. Being able to describe a clear result and judge its quality gives you two essential starting skills. Technical integration becomes relevant when an agent needs access to other systems or permission to change them.

OpenAI: ChatGPT Work and Codex · OpenAI: ChatGPT agent · Codex and subagents

When choosing a bicycle, you could ask an agent to compare three named models by size, specification, total price and delivery terms. Require manufacturer information, visible gaps and no purchase. The result supports your decision. You still need a test ride to discover whether the bike feels comfortable.

At work, an agent could prepare a meeting: summarise approved documents, distinguish decisions from unresolved questions and produce an agenda. If two documents give different dates, it should show the conflict. It should not make the disagreement disappear through a plausible invention. The output is preparation, rather than an automatically approved business decision.

A third example is a small event. Provide the date, guest count, budget and requirements. The agent researches suitable venues and prepares a comparison. It can then draft enquiries. Sending them requires a separately authorised channel and verified recipients. Discovering an email address does not itself provide permission to contact someone.

All three examples share a useful property: you can inspect the result before it creates a commitment. That lets you learn where AI helps and where corrections are necessary. During an initial trial, this learning opportunity matters more than granting as much autonomy as possible.

An agent has no magical connection to your calendar, inbox or company storage. It needs a supported integration, a session or files you provide. Even then, reading, editing and sending are different capabilities. Check them individually instead of granting broad “Microsoft 365 access”.

A few selected files are often sufficient for a first task. This also reduces ambiguity: five current documents provide a clearer assignment than years of archives and conflicting drafts. Identify the authoritative version and explain which material is background only.

We recommend increasing write access gradually. First ask for a result in a separate working file. Then allow selected changes with a preview. Consider narrowly defined automatic actions once the process has proved dependable. An additional tool or inbox expands the scope and deserves a new decision.

A useful agent needs a clear task, suitable tools and a visible boundary.

Misreading a hotel price is frustrating. Making a non-refundable booking based on that mistake is a different kind of problem. Agree budgets, recipients, data scope and binding actions before work begins. Confirmation is particularly useful when money, external communication or changes that are hard to reverse are involved.

Human review works when the decision is understandable. “Continue?” provides little help. A useful preview shows what will be sent or changed, for whom and with what consequences. Actually inspect the content at that point. Approval without looking at the result is merely another click.

Prompt injection is another risk: a webpage or document may contain instructions intended to divert the agent from its task. Anthropic discusses this conflict in its research on trustworthy agents. Our practical recommendation is to treat external content as information, rather than permission to perform new tasks. Unexpected requests for additional data or rights should trigger a stop and review.

Anthropic: Trustworthy agents in practice

You do not need to be a prompting expert. Describe the required deliverable, the information it may use and how you will recognise a good result. Add the conditions under which the agent should stop or ask. These four elements are usually more useful than a lengthy set of role descriptions.

For example: “Create a comparison spreadsheet from the three attached offers. Separate one-off and recurring costs. Use supported facts only. Flag missing services and unclear contract periods. Produce a draft with source references; do not contact anyone or place an order.” This specifies the goal, material, quality criteria and boundaries.

For longer tasks, ask for a checkpoint after the initial review. Has the agent understood the assignment? Is an important source missing? An early correction prevents it from spending half an hour carefully solving the wrong problem. Refining an instruction during the task is perfectly reasonable.

Choose a first task that you perform regularly and can judge yourself. Use non-sensitive sample data and keep the scope small. Afterwards, compare your total effort, rather than just the AI’s processing time. Preparation, waiting, review and corrections all belong in the calculation.

Consider a hypothetical example: research normally takes 45 minutes. With an agent, you spend 10 minutes preparing the task and 15 reviewing the output. That would save 20 minutes of your own effort, provided the result is usable. It does not establish a general savings rate. Missing access or poor sources could remove that advantage in the next case.

Check a difficult case too. What happens when a file is missing, a figure conflicts or a website is blocked? A useful agent reports the gap. It does not declare success simply because it has produced attractive text. Include this distinction in your assessment.

In a team, ownership and storage matter: who reviews the output, where is the authoritative version and who removes access after the trial? Record the process briefly. This turns an interesting demonstration into a repeatable working step.

Our position is straightforward: let agents prepare, organise and make traceable edits. Expand responsibility where you understand and have checked the process. Complete autonomy is not a necessary destination. Often, the greatest benefit is reaching a sound human decision sooner.

For individuals, this can make research or planning less burdensome. For companies, it can reduce manual transfer work and improve preparation. Both depend on a well-defined task and a correct result. An agent can support responsibility; independently clicking through a task does not mean it assumes that responsibility.

Try one small, specific task this week. Ask for a working file or a sourced comparison. Then assess honestly what improved. Discovering that a simple template is sufficient is a useful result too.