AI agent

This is where most of the current effort and most of the current disappointment are concentrated. The promise is genuine: work that used to need a person shepherding it step by step can run unattended, which is a different order of saving from drafting help. The catch is that errors compound. A chatbot that gets one answer wrong wastes a minute, whereas a system taking twenty steps on your behalf can be wrong at step three and spend the remaining seventeen building on it. Judging these tools well means asking what happens when a step fails, not what happens when every step succeeds.

The loop is short and it is the whole idea. The system is given a goal, decides what to do next, does it, looks at the result, and decides again with that result now in front of it. Nothing about this is new as a pattern; what changed is that a model good enough at language turned out to be good enough to pick the next step from a description of the situation.

The steps are taken through tools. A tool is anything the system can call and get a result from: a web search, a calendar, a spreadsheet, a database query, another piece of software's interface. The set of tools available is what decides the boundary of what an agent can actually do, which is why two products built on the same model can feel so different when you try them.

Planning may be explicit or implicit. Some systems write a plan first and work through it, which is easier to inspect and slower to adapt. Others decide only the immediate next step each time, which is more flexible and harder to follow after the fact. Neither is a guarantee of good judgement, and a written plan can look sound while resting on a wrong assumption made in its first line.

Stopping is a design problem in its own right. Left alone the loop has no natural end, so systems are given limits: a maximum number of steps, a budget, a time cap, or a check that asks whether the goal is met. Runaway cost is a real failure mode, and so is stopping too early with a job half done and reported as finished.

Everything the agent has learned along the way has to fit in the same fixed budget as any other request. Twenty steps of results accumulate, and once the total exceeds what the model can hold, early findings drop out. This is why long agent runs tend to lose the thread rather than gently degrade, and why summarising as it goes is a common part of the design.

Approval gates are where most of the safety comes from. A system that reads and drafts can be allowed to run freely, because the worst case is a wasted run. A system that sends, pays, deletes or commits needs a person between the decision and the action for anything irreversible. Deciding which of your actions need that gate is the main design judgement in agent work, and it is yours to make rather than the vendor's.

Agentic AI and autonomous agent are the same idea under other names. Agentic is the adjective, describing software with any of this loop-and-tools character, and it is applied loosely enough that you will meet it on products making a single tool call. Autonomous emphasises running without supervision, which is a spectrum rather than a state: almost everything sold to you as autonomous still has limits, approval points and a human who set the goal.

• Set up a multi-step flow on an automation platform where each step's output decides the next, which is the loop with the decisions made in advance by you rather than at run time.

  Zapier

• Give a coding assistant a task in a repository and watch it read files, make a change, run the tests and react to the failures, which is the loop with a real feedback signal.

  Claude Code

• Build an assistant that handles a recurring task end to end on a self-hostable platform, where the tools it may call are ones you have connected deliberately.

  n8n

• Use a research assistant that runs several searches, reads what it finds and searches again based on the gaps, rather than answering from one pass.

  Perplexity

What is the difference between an AI agent and agentic AI?

Mostly grammar. Agent names the system; agentic is the adjective for software with that loop-and-tools character. The adjective is applied loosely in marketing, including to products that make a single tool call, so it is worth asking what the system actually decides for itself rather than reading the label.

Are AI agents reliable enough for real work?

For bounded tasks with cheap failure and a person reviewing the result, yes, and they are in production use. For long unattended runs that take consequential actions, the compounding-error problem is real and unsolved. The practical pattern is narrow scope, explicit stopping conditions, and approval gates on anything irreversible.

What tools can an agent use?

Whatever it has been connected to: search, calendars, email, spreadsheets, databases, other software's interfaces, and increasingly connectors built to a shared standard. The available set defines the boundary of what it can do, so comparing agent products is largely comparing what each one can reach.

Will an agent spend money without asking?

Only if it has been given a tool that can, and permission to use it unsupervised. Sensible deployments put an approval step in front of anything irreversible, spending included. Where a product offers unattended purchasing, treat the spending limit and the approval settings as the first thing to configure rather than a detail.

Do I need an agent, or would automation do?

If you can write down the steps, automation is cheaper, faster and more predictable, and it does not need a model at all. Agents earn their cost where the steps genuinely vary with what is found, such as research, triage or investigation. Many good systems are automation with one agentic step inside them.

• An agent is given a goal rather than a question, and decides its own steps towards it.
• It acts through tools, so the connected set defines the boundary of what it can do.
• Errors compound across steps, which is why demonstrations impress and long runs disappoint.
• Stopping conditions and approval gates on irreversible actions are the main safety design.
• Agentic AI and autonomous agent are the same idea, applied loosely as marketing adjectives.

• Zapier

  Multi-step flows where the sequence is fixed, which is the useful contrast case.

• Claude Code

  Reads, edits and runs tests, so the loop has a real feedback signal to react to.

• n8n

  Self-hostable, so the tools an agent may call are ones you connected deliberately.

• Lindy

  Assistants built around recurring tasks rather than one-off questions.