The hype is real, but the explanation is usually terrible

"AI agents" has become one of those phrases that gets dropped into every boardroom presentation and vendor pitch without anyone actually explaining what it means. If you've nodded along while quietly wondering what's actually being described, this is for you.

The short version

An AI agent is software that can perceive its environment, make decisions, take actions, and pursue goals — with varying degrees of autonomy. The key word is act. An AI agent doesn't just answer a question; it does things in the world based on the answer.

That might mean searching the internet, running a calculation, updating a record in your CRM, sending an email, or triggering a workflow. The agent uses an AI model (usually a large language model) as its reasoning engine, and tools as its hands.

What makes something an agent (and not just a chatbot)?

A chatbot responds to what you say. An AI agent pursues a goal.

The distinction comes down to three capabilities:

Tool use. An agent can call external services. It can search the web, query a database, read a file, run code, or call an API. A chatbot that can only produce text is not an agent.

Multi-step reasoning. An agent can plan. Given a goal like "summarise this week's sales performance and flag any deals at risk", an agent will break that into steps: find the data, analyse it, identify the relevant deals, format the output. A basic chatbot handles one turn at a time.

Autonomy. An agent can operate without a human at every decision point. The degree of autonomy varies — some agents require approval before taking certain actions, others run fully automated — but the defining feature is that the agent is driving the process, not just assisting it.

A day-in-the-life example

Here's what an AI agent looks like in practice for a business team:

A sales manager asks an agent: "Prepare my briefing for tomorrow's pipeline review."

The agent:

• Pulls the latest CRM data for all open deals
• Identifies deals that haven't had activity in more than 14 days
• Checks the calendar for tomorrow's attendees
• Pulls recent emails relevant to the at-risk deals
• Drafts a two-page briefing document with a summary, deal status, and suggested talking points
• Drops it into the shared Teams channel

The sales manager comes in the next morning and the briefing is waiting. They didn't write a single line of code. They didn't manually pull a report. The agent handled the whole thing.

That's an agent. Not just a better search bar.

Where AI agents are being used right now

Across the organisations we work with, the most common early deployments are:

• IT helpdesk automation — handling password resets, software requests, and FAQ responses without a human ticket handler
• HR policy assistants — answering leave, benefits, and payroll questions from a single source of truth
• Document intelligence — extracting, summarising, and routing information from contracts, invoices, and reports
• Sales assistance — enriching leads, drafting outreach, and updating pipeline records automatically
• Onboarding — guiding new hires through their first week with personalised checklists and automated provisioning

What AI agents can't do (yet)

It's worth being clear about the limits, because a lot of vendor claims are ahead of the reality:

• Agents are not infallible. They make mistakes, particularly on tasks requiring precise factual accuracy or complex judgement.
• They depend on the quality of the data and systems they're connected to. A great agent connected to a messy CRM will produce messy outputs.
• Fully autonomous agents operating across multiple systems without human oversight carry real governance and security risks. Most production deployments maintain human checkpoints for high-stakes actions.

How to start thinking about agents for your organisation

The most useful question isn't "what can AI agents do?" It's: "where does work in our organisation get stuck waiting for someone to do something routine?"

Those friction points — the manual handoffs, the repeated lookups, the reports that take two hours because the data lives in three different places — are your agent candidates.

Start narrow. Build something that handles one of those scenarios well. Then expand from there.

If you want to identify the right starting points for your team, get in touch with TrimJourney. We help Microsoft 365 organisations move from chatbots to agents that genuinely change how work gets done.