AI that doesn't just answer questions — it takes action. Understanding what AI agents are, how they work, and what they mean for your business.
You're probably already familiar with AI assistants like ChatGPT, Claude, or Gemini. You ask them a question, they give you an answer. You ask them to write something, they write it. It's a conversation — you direct, they respond.
AI agents are different. Instead of just responding to each instruction, an AI agent can work towards a goal more independently — planning steps, using tools, making decisions, and taking actions without needing you to guide every move.
Think of the difference this way:
You: "Write me an email to follow up with a client."
AI: Writes the email. Stops. Waits for your next instruction.
You: "Follow up with all clients who haven't responded to proposals in the last week."
Agent: Checks your CRM for clients with outstanding proposals. Identifies which ones are overdue. Looks up the proposal details. Drafts personalised follow-up emails for each. Sends them (or queues them for your approval). Logs the activity. Reports back what it did.
The key difference: the agent breaks down a goal into steps and executes them, rather than waiting for you to tell it what to do next.
Not all AI agents are equally independent. There's a spectrum:
| Level | What it does | Example |
|---|---|---|
| Assistants | Responds to each instruction individually | ChatGPT answering questions |
| Copilots | Suggests actions, helps complete tasks, but you stay in control | GitHub Copilot suggesting code as you type |
| Semi-autonomous agents | Plans and executes multi-step tasks, but asks for approval at key points | An AI that drafts and schedules social posts, but waits for you to approve before publishing |
| Autonomous agents | Works independently towards goals with minimal oversight | An AI that monitors your inventory, predicts shortages, and automatically reorders stock |
Most business applications today sit in the "copilot" and "semi-autonomous" categories. Fully autonomous agents are still emerging — and for good reason, as we'll cover in the risks section.
AI agents represent a shift from "AI as a tool you use" to "AI as a worker you delegate to." This is a significant change in how businesses can leverage AI — but it also requires more careful thinking about oversight and control.
Understanding how AI agents work helps you evaluate them more critically and use them more effectively. Here's what's happening under the hood:
Every agent starts with something it's trying to achieve. This could be specific ("Book a meeting with John for next Tuesday") or broader ("Keep our social media accounts active with relevant content").
The agent breaks the goal into smaller steps. For a task like "research competitors and create a comparison report," it might plan to:
Agents can use tools — things like web browsers, code execution, file access, APIs to other software, email, calendars, and databases. This is what lets them take action in the real world rather than just generating text.
More sophisticated agents maintain memory across sessions — remembering previous interactions, your preferences, and ongoing projects. This lets them pick up where they left off and learn what works for your specific situation.
Good agents check their work, handle errors, and adjust when things don't go as planned. If a website is down, they try an alternative. If the output doesn't match what you wanted, they revise.
Let's trace how an AI agent might handle a real business task:
Here's what the agent might do:
What would take you 20-30 minutes of research happens in a couple of minutes — and it's ready before you even thought to prepare.
You might be thinking: "This sounds like automation — we've had that for years." There's an important difference:
Traditional automation follows fixed rules. "If email contains 'invoice', move to Finance folder." It does exactly what you programmed, every time, regardless of context.
AI agents can handle ambiguity and variation. They understand intent, adapt to unexpected situations, and make judgement calls. When something doesn't fit the expected pattern, they can figure out what to do rather than just failing or doing the wrong thing.
This makes agents useful for tasks that were too unpredictable for traditional automation — but it also means their behaviour is less predictable, which is why oversight matters.
Agent pricing varies depending on how you access them. Here's a straightforward breakdown of the main models:
Many agent features come bundled into existing software subscriptions. Microsoft 365 Copilot, for example, adds AI agent capabilities for a monthly per-user fee on top of your existing Microsoft subscription. This is predictable and easy to budget for, but costs scale with your team size.
Customer service agents often charge based on what they actually do. You might pay per ticket resolved, per conversation handled, or per successful action taken. This can be cost-effective if volume is low, but watch for costs to climb as usage grows.
If you're building custom agents or using tools that call AI APIs directly, you'll pay based on usage — typically measured in tokens (roughly, chunks of text processed). Costs vary by model and can add up quickly if agents are doing heavy research or processing large documents.
Most platforms let you set monthly spending caps or usage limits. Use them. An agent that runs more than expected — or gets stuck in a loop — can rack up charges quickly. Start with conservative limits and adjust as you understand your actual usage patterns.
An agent is only as useful as the tools it can access. Without connections to your business systems, it's limited to what you can copy and paste into it.
Most software today offers an API (Application Programming Interface) — think of it as a set of rules that lets different software programs talk to each other. When you connect two apps together (like linking your calendar to a scheduling tool), they're communicating through APIs. Agents use these same APIs to read data from your CRM, send emails, update records, and take actions across your tools. The more APIs an agent can access, the more it can do.
The Model Context Protocol (MCP) is an emerging standard that makes it easier to connect AI tools to your business systems. Instead of building custom integrations for each tool, MCP provides a standardised way for agents to discover and use the tools available to them. MCP has gained broad adoption across all major AI providers and is becoming an industry standard. It's worth checking whether the agent tools you're evaluating support it, as it can simplify how your systems connect.
For a deeper look at how these connections work and what they mean for your business, see our Getting More From AI guide, which covers integrations, APIs, and working with your own business data.
Before choosing an agent solution, check what integrations it offers out of the box. The best agent in the world won't help if it can't connect to the tools you actually use.
There are several routes to getting AI agents working in your business. The right choice depends on your technical resources, budget, and how specific your needs are.
You may already have access to agent capabilities without realising it. Microsoft 365 Copilot can act as an agent across your Office apps. ChatGPT's custom GPTs let you create specialised assistants with access to your documents. Claude's Projects feature lets you build context-aware assistants for specific use cases. Check what your existing subscriptions offer before buying something new.
Platforms like Lindy let you build multi-step agents without writing code. You define the workflow visually — "when this happens, do this, then this" — and connect the tools the agent needs. Good for straightforward automation and internal processes. The trade-off is less flexibility than custom development.
Other options include Relevance AI (pre-built templates for sales, support, and research tasks) and CrewAI (focused on multi-agent systems where several agents work together). These platforms offer a middle ground between off-the-shelf features and full custom builds.
A growing number of companies specialise in building custom AI agents for businesses. They'll design an agent tailored to your processes, train it on your data, and integrate it with your tools. If you go this route, consider:
If you have technical resources, you can build agents directly using AI provider APIs. OpenAI's developer tools, Anthropic's Agent SDK, and frameworks like LangChain give you full control over how agents behave. This is the most flexible option but requires development expertise and ongoing maintenance.
Most businesses should start with the first option — exploring agent features in tools they already pay for. Only move to more complex solutions once you've identified a specific need that simpler approaches can't handle.
AI agents are already being used in business today, though often in more controlled forms than the fully autonomous vision. Here are practical applications that are working now:
AI agents that handle customer enquiries — not just answering FAQs, but actually resolving issues: processing refunds, updating orders, booking appointments, checking account status. They escalate to humans when needed, but handle the straightforward cases independently.
Examples: Intercom's Fin, Zendesk AI, Ada
Agents that research prospects, personalise outreach, schedule follow-ups, and keep your CRM updated. They can identify when a lead goes cold and re-engage them, or spot when someone's ready to buy based on their behaviour.
Examples: Clay, Apollo.io, Outreach
Agents that gather information from multiple sources, synthesise it, and produce reports. Competitive intelligence, market research, due diligence — tasks that used to take days can happen in hours.
Examples: Perplexity Pro, custom GPTs, various vertical-specific tools
Handling scheduling, email triage, travel booking, expense processing, and other administrative tasks. These work best when integrated with your existing tools.
Examples: Microsoft Copilot, Reclaim.ai, Motion
Agents that can write code, debug issues, deploy updates, and monitor systems. They're particularly useful for routine maintenance, security patches, and responding to alerts.
Examples: GitHub Copilot Workspace, Cursor, Devin
A newer category of agent that runs on your own computer or server rather than in the cloud. You interact with them through messaging apps like WhatsApp, Slack, or Telegram. Because they run locally, they can have system-level access — they can open a browser, read and write files, run commands, and chain tasks across different programs. They also have internet access, can make their own decisions, and act on your behalf — including spending money if granted access to payment or financial accounts. They maintain persistent memory, remembering your preferences and past conversations over time — and they learn continuously, adapting their behaviour based on your feedback and usage patterns. They can handle a very wide range of tasks, and use other AI tools themselves to enhance their capabilities.
A word of caution: these agents can behave unpredictably, and because they have deep access to your system, a misconfigured agent is a security risk. Setting up and securing them requires hands-on technical knowledge and is not recommended for non-technical users. If you're considering running one, there is plenty of guidance available online about how to set them up safely.
Examples: OpenClaw (open-source, formerly Clawdbot/Moltbot), Nvidia NemoClaw (open-source stack that adds privacy and security controls to OpenClaw)
If you're interested in using AI agents in your business, here's a practical approach:
Start with copilots, not autonomous agents. Tools that assist rather than act independently are lower risk and help you understand what's possible. Microsoft Copilot, ChatGPT with custom GPTs, Claude with Projects — these let you experience agent-like capabilities with you still in control.
Pick a specific, bounded use case. "An agent that handles all customer service" is too broad. "An agent that answers product questions from our knowledge base and escalates everything else" is specific and manageable.
Use existing tools before building custom. Many SaaS products now include AI agent features. Your CRM, help desk, or project management tool may already have capabilities you're not using. Check what's available before building something new.
Measure the impact. Track time saved, tasks completed, errors caught. This helps you understand what's working and build the case for further investment.
If you're already using ChatGPT or Claude, try the "Projects" or "Custom GPT" features. Upload your key business documents and create an agent that can answer questions about your specific business. This gives you a feel for agent capabilities in a controlled environment.
AI agents are powerful, but they come with risks that are different from traditional AI tools. Understanding these helps you use agents responsibly:
When an AI assistant makes a mistake, you see it immediately and can correct it. When an AI agent makes a mistake early in a multi-step process, that error can compound — each subsequent step builds on the flawed foundation.
Imagine an agent that's supposed to identify your most valuable customers and send them a special offer. If it misidentifies who's valuable — perhaps because it misunderstood your criteria — it might send offers to the wrong people or miss your actual best customers entirely. And you might not know until it's too late.
Build in checkpoints where the agent reports what it's about to do before doing it, especially for actions that are hard to reverse. "I'm about to send emails to these 47 customers — approve?"
AI agents can behave in unexpected ways, especially in situations they weren't designed for. They might find creative solutions to problems — but those solutions might not be ones you'd approve of. This isn't malice; it's an AI optimising for its goal in ways you didn't anticipate.
A classic example: an agent tasked with "book me a flight" might book a more expensive flight because it found a slightly better option, not realising cost was your priority. Or it might book a flight at 5am because technically that met your criteria.
Be explicit about constraints and priorities, not just goals. "Book me a flight under £300, departing after 8am, preferring direct routes" is better than "book me a cheap flight."
For an agent to be useful, it needs access to your systems — your CRM, your email, your calendar, your files. This creates a larger attack surface, meaning a compromised agent could expose sensitive business data.
This risk is higher with self-hosted local agents. When you give an AI permission to read your files, manage your emails, and communicate externally from your own machine, you are giving it broad access to your system. If it misinterprets an instruction, or processes a hidden malicious command from a website (known as a prompt injection), it could delete files, share private data, or send unauthorised messages. Some large organisations and government bodies have already restricted staff from installing these agents on work devices.
There are also questions about what the AI provider can see. If an agent is reading your emails and CRM data to do its job, that data is being processed by the AI provider's systems.
Only give agents access to what they actually need. Use read-only permissions where possible. And keep humans in the loop for any access to financial systems or sensitive customer data.
When an agent makes a decision or takes an action, who's responsible? If your AI agent sends an inappropriate email to a customer, is that the AI's fault? The vendor's? Yours? This isn't just philosophical — it has real implications for liability, compliance, and customer relationships.
Current legal and regulatory frameworks weren't designed for AI agents, and the rules are still being worked out. In the meantime, the safest assumption is that you are responsible for what your AI agents do.
There's a temptation to hand over as much as possible to AI agents — "let it handle everything." But some tasks shouldn't be fully delegated, either because they require human judgement, because they're core to your business, or because customers expect a human touch.
The goal isn't to replace human judgement; it's to free up humans to focus on work that actually needs them.
As you consider using AI agents, think about oversight at three levels:
Before: What is the agent allowed to do? What are its boundaries? What permissions does it have? What should it never do?
During: How do you monitor what the agent is doing? Where are the checkpoints? What triggers human review?
After: How do you audit what the agent did? How do you catch and correct errors? How do you improve the system over time?
The right level of oversight depends on the stakes. An agent that schedules social media posts needs less oversight than one that handles customer complaints or processes payments.
AI agents are powerful but need careful planning. Let's talk about what would work for your specific situation.