Daily Junction.mobi portalTechnology · May 20, 2026 · Amelia Hart · 3 min
AI agents go beyond chatbots: they can plan, use tools and take actions to complete multi-step tasks. Here is what that means, where it works today, and where it does not.
If 2023 was the year the world met the chatbot, the years since have been about a more ambitious idea: software that does not just answer, but acts. These systems are usually called AI agents, and "agentic AI" has become one of the most-used — and most-stretched — phrases in technology.
Strip away the marketing and the concept is straightforward.
An AI agent is a system built around a large language model (LLM) that can plan and take actions to achieve a goal, rather than producing a single block of text and stopping.
In practice, an agent combines three things:
That loop is the heart of the idea. A chatbot is a single turn. An agent is many turns, mostly with itself, checking its own progress.
Suppose you ask an agent to "find three suppliers for recycled packaging, compare their minimum order sizes, and put it in a table."
A plain chatbot would guess from memory. An agent would instead: search the web, open several supplier pages, extract the relevant numbers, notice if one page failed to load and try another, assemble the comparison, and format it — pausing to ask you if something is ambiguous. The difference is the ability to gather new information and respond to it.
Agents are most reliable when a task is well-defined and verifiable — when there is a clear way to check whether the work is correct.
The same qualities that make agents powerful make them risky in the wrong context.
An agent is only as trustworthy as your ability to check its work. If you cannot tell whether the output is right, autonomy is a liability, not a feature.
Three weaknesses stand out:
Organisations getting value from agents tend to follow a few rules:
The honest summary is that agents are genuinely useful and genuinely immature. They shine on bounded, checkable tasks and stumble on open-ended, high-stakes ones. The most productive way to think about them is not as autonomous employees but as fast, tireless assistants that still need a manager.
For now, the winning pattern is collaboration: let the agent do the legwork, and keep a human on the decisions that matter.
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