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What an AI agent can and cannot do for you yet

"Agent" now describes everything from a scripted macro to a model with access to your inbox. The difference matters most when something goes wrong.

By THRYV Tech Desk·Published July 30, 2026·Updated July 31, 2026·7 min read
What an AI agent can and cannot do for you yet
AI · Illustration commissioned for THRYV. Photography is replaced with original imagery as each story is produced.

The takeaway

An AI agent is a language model given tools, permissions and a loop so it can take actions rather than only produce text. That works well for bounded, reversible tasks with a clear success signal. It works poorly where errors are expensive, context is missing, or no human checks the result before it takes effect.

The word agent has drifted far enough that it now needs defining every time it is used. In its useful sense, an agent is a model that has been given three things a chatbot does not have: tools it can call, permissions to use them, and a loop that lets it keep going until a goal is met or a limit is hit.

Where the loop helps

Agents perform best on tasks that are bounded, repeatable and reversible: reformatting data between systems, drafting a first version of routine correspondence, monitoring a feed and flagging exceptions, or filling a form from a document. In each case there is a clear finishing condition and a cheap way to undo a mistake.

The test before you delegate

Ask what happens if the agent is confidently wrong on its worst day. If the answer is an awkward email, delegate it. If the answer is a payment, a public statement or a deleted record, keep a human approval step in the loop.

Where it still fails

  • Tasks with missing context — the agent cannot know what was decided in a meeting it never saw.
  • Judgement calls with no verifiable success signal, where the model has no way to check its own answer.
  • Long chains of actions, because a small error early is compounded rather than corrected.
  • Anything exposed to untrusted text, which can carry instructions the model may follow.

Setting one up responsibly

  1. Give the narrowest permissions the task needs, and separate read access from write access.
  2. Require explicit approval for actions that spend money, contact customers or delete data.
  3. Log every tool call so a failure can be reconstructed afterwards.
  4. Set a hard step limit and a timeout so a stuck loop stops on its own.
  5. Review a sample of completed runs weekly rather than trusting an aggregate success rate.

THRYV has not benchmarked individual agent products for this article and names none. The framework above is what we apply when evaluating any tool that asks for permission to act on your behalf.

Sources

This article is original writing by THRYV. We link to primary reporting and official documents rather than reproducing them.

  1. AI Risk Management FrameworkUS National Institute of Standards and Technology
  2. OWASP Top 10 for Large Language Model ApplicationsOWASP Foundation
  3. Regulatory framework for AI (AI Act overview)European Commission

Why you can trust this article

Written and edited in-house by the THRYV Tech Desk. We do not republish or reword agency copy, and we do not invent quotes, statistics, testimonials or ratings. Where figures move frequently, we point you to the primary release rather than printing a number that will be out of date. Advertising and affiliate partnerships have no influence on our reporting — see our editorial standards, fact-checking policy and affiliate disclosure. Spotted an error? Write to newsroom@thryv-affiliate.com.

General information only. Not personalised financial, medical or legal advice.

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