What Is an AI Agent and How Does It Work?

For most people, using AI still means opening a chat window, describing a problem, and reading the answer. The interaction can be useful, yet most of the surrounding work remains manual. Someone gathers the relevant context, applies the answer, checks the result, and remembers to repeat the process next week.

AI agents extend the model into that surrounding work. An agent receives an outcome, inspects the current state, chooses from available tools, and continues until the work is complete or a decision needs human judgment. A weekly competitor brief, for example, could involve reading the previous report, checking approved sources, recording changes, verifying links, and preparing an updated draft for review.

Every step could be done manually, and delegation becomes worthwhile when the process repeats often enough that gathering, checking, and organizing the material consumes more time than the final decision. The agent prepares the evidence, while the founder decides whether any of the changes deserve a response.

Allowing a model to continue acting also creates new failure modes. Stale sources can produce the same misleading report every week, weak limits can keep research running long after it stops being useful, and unclear permissions can let uncertain findings travel further than intended. Understanding agents therefore requires looking at the entire operating loop: how they act, how their progress is checked, where they stop, and who remains responsible.

How an agent moves through work

An AI agent is a software process that uses a model to choose actions in pursuit of an outcome. The outcome gives the work direction, and the surrounding system supplies the context, tools, permissions, and stopping conditions that shape how the agent gets there.

The competitor brief shows how those parts connect. At the beginning of the run, the agent reads the approved company list, the previous report, and the period it should investigate. It then decides which sources to inspect, records what it finds, and compares the new evidence with the existing brief. Each result changes what the agent knows and therefore influences the next action.

That cycle can be described in six stages:

Stage	What happens	Competitor-research example
Observe	Read the state needed for the next decision	Open the previous brief and approved source list
Decide	Choose an allowed action that advances the work	Inspect recent pricing and product announcements
Act	Use a tool or create an artifact	Search a source and add a dated finding
Verify	Check the result against explicit criteria	Confirm the finding has a current working link
Route	Continue or ask the appropriate person to decide	Send an uncertain pricing change to the founder
Stop	Finish in a known state	Store the reviewed brief for the next run

The model plays its largest role in deciding what to do with variable information. Ordinary software still handles the rules that need to remain consistent, including permission checks, validation, attempt limits, and persistence. A source requirement enforced by the workflow remains in force even when the model feels confident enough to skip it.

This division is what makes the loop manageable. The agent can interpret a page whose layout changed overnight, while code can insist that every material claim carries a source and every run stops after a fixed amount of work.

Where agents fit alongside chat and automation

Chat remains useful when the work is exploratory and a person wants to shape each turn. A founder comparing several positioning ideas may prefer that back-and-forth because the direction is still forming and every answer changes the next question.

Traditional automation works well at the other end of the spectrum, where the path is predictable. Exporting a database, resizing an image, or sending a webhook already has a known sequence, so adding a model would introduce cost and uncertainty without contributing useful judgment.

Agents occupy the space where the desired method stays recognizable while the inputs vary. Support tickets use different language, research sources publish in different formats, and bug reports rarely arrive with the same clues. The agent can interpret those differences while working toward the same kind of outcome each time.

Real workflows often combine all three approaches. A weekly report may use ordinary code to retrieve metrics, an agent to investigate unusual changes, and a conversation with a person to decide how those changes affect the plan. Choosing the right approach for each step keeps stable work deterministic and reserves model judgment for the parts that genuinely need interpretation.

What gives the loop reliable edges

A model with a long prompt and a large tool list may produce an impressive demonstration, but recurring work quickly exposes everything around the model. The agent needs a finish line, current context, durable state, narrow tools, enforced limits, and verification that matches the artifact it produces.

The finish line starts with a concrete outcome. “Research our competitors” leaves the agent with an endless supply of possible reading. A bounded brief names the companies, observation period, required evidence, and questions that should reach the founder. It also explains when the run should stop because a source is unavailable, its attempt limit has been reached, or the next action requires additional permission.

Keeping the context current grounds each decision in the approved list, last week’s brief, and the sources the research agent may use. Giving it every document the company has ever written consumes attention and increases the chance that old material shapes the result. A smaller set of relevant context usually produces clearer work and makes mistakes easier to trace.

Durable state carries the work across more than one model call. Completed searches, source results, draft changes, open questions, and review decisions should survive a retry or pause. When that state lives only in a transcript, the agent may repeat completed work or lose track of which external actions already happened.

Tools then determine what the agent can actually do. A useful tool has one recognizable purpose, validates its inputs, checks permission, and returns a clear result. Separate tools for reading an account, preparing a draft, requesting approval, and applying an approved change create boundaries that both the system and a reviewer can understand.

Those boundaries become especially important during retries because a timeout may occur after an external service has already accepted an action. The agent then needs a way to reconcile what happened before trying again. Otherwise a temporary network problem can create duplicate messages, records, or other side effects.

Verification should follow the artifact

Agent output often sounds more certain than the available evidence deserves. The surrounding workflow needs a way to judge the actual artifact, using checks suited to the kind of work being produced.

Artifact	Useful verification
Code change	Tests, compilation, static analysis, and diff review
Research brief	Current links, claim-to-source checks, and coverage rules
Customer reply	Account context, policy check, and named approval
Financial draft	Reconciled totals, required fields, and approval before payment
Published page	Link check, claim review, and visual review

Mechanical facts belong in deterministic checks whenever possible. A command can establish whether code compiles, and a link checker can establish whether a source is reachable. Questions involving tone, strategy, financial authority, or professional accountability need a named person who can judge the evidence in context.

That person should receive a decision rather than an alert. A useful request explains what happened, shows the relevant evidence, presents the available choices, and describes what follows from each one. The agent can then pause cleanly while the person decides, without requiring anyone to watch the entire run unfold.

Following the competitor brief from Monday to Monday

When the weekly workflow begins, it reads the approved competitor list and the brief produced by the previous run. The agent searches defined public sources for changes within the observation window, records each finding with its date, and separates clear updates from signals that still need interpretation.

A link checker catches missing evidence, while a required-section check keeps the report consistent from week to week. If a company removes its pricing page, the workflow preserves the gap instead of filling it with an assumption. The founder receives the source history and can choose whether to use an archived page, mark the price as unavailable, or wait for clearer evidence.

Once the material changes have been reviewed, the new brief is stored with its sources and decisions. The next Monday begins from that durable record, which means the agent can focus on what changed during the week instead of rebuilding the entire comparison.

The model contributes interpretation throughout the process, especially when pages vary in structure or an announcement affects several parts of the existing report. The recurring value comes from the whole loop working together: a stable outcome, constrained sources, recorded progress, independent checks, and a clear route to human judgment.

Choosing a useful first job

A strong first agent job returns often, produces a recognizable artifact, and remains easy to review. It should also matter enough that reducing its preparation work creates visible relief.

Question	Promising answer	Warning sign
Does the work repeat?	The same outcome returns each week	Every instance has a different purpose
Is the result concrete?	A draft, report, diff, or updated record	A broad request to “help the business”
Are the inputs reachable?	Sources and permissions are known	Important context lives only in someone’s head
Can quality be checked?	Commands, evidence rules, or a reviewer exist	Nobody can describe acceptable output
Are mistakes reversible?	Work stays in draft or can be rolled back	One action creates irreversible harm
Can exceptions be routed?	A named person owns ambiguous cases	The workflow has nowhere to send uncertainty

Start with one narrow process and review the early runs closely. Repeated corrections reveal where the surrounding system needs work. Missing information may belong in the context, recurring mistakes may need a verifier, and sensitive actions may need a stronger permission boundary.

Expansion becomes safer once the original loop handles ordinary variation without creating a larger review burden. At that point, the agent has demonstrated that it can carry a defined process, and the team has evidence for deciding which responsibility comes next.

Turning the loop into recurring work

Task Machine provides an operating layer for recurring work shared by humans and agents. Chat holds strategic discussion, the Inbox receives approvals and exceptions with their decision context, and Tasks preserve the detailed history of each outcome.

Agents execute through connected workers where the required files, CLIs, browsers, and tools are available. Workflows can combine agent steps with questions, approvals, verifiers, retries, and recorded outcomes, giving each part of the loop a durable place.

Setting up that structure takes more work than opening a chat, so the job needs to repeat often enough to justify it. Deterministic processes still belong in scripts, exploratory thinking still belongs in conversation, and decisions carrying personal accountability still belong with people.

Choose one process that returns every week and write down its outcome, inputs, allowed actions, verifier, decision owner, and stopping condition. Those details are enough to reveal whether an agent can take on a useful part of the work and what the surrounding workflow must provide.