Over the past two years, one of the most talked-about ideas in AI has been AI agents. A chat assistant answers a question; an agent takes steps on its own to reach a goal: it searches for information, connects to systems, prepares documents, drafts emails and checks the result.
So what exactly are agents, where are companies using them and what should you watch out for?
What is an agent?
An AI agent is essentially made of four parts:
- A language model (LLM): the "brain" that reasons, plans and decides
- Tools: the capabilities the agent can use, such as searching, querying a database, reading files, sending emails or calling an API
- A goal: the task defined by the user
- A loop: the agent takes a step, evaluates the result, plans the next step if needed and continues until the goal is reached
Chat assistant vs. agent
| Chat assistant | AI agent | |
|---|---|---|
| How it works | Question and answer | Goal-driven, multi-step |
| Access to systems | Usually none | Through tools |
| Who drives it? | The user manages each step | The agent plans the steps |
| Output | Text | Text and actions (records, emails, reports) |
| Risk level | Low to medium | Medium to high (because it can act) |
Where are companies using agents?
- Customer service: classifying incoming requests, retrieving customer history, drafting replies for agents to review
- Document processing: extracting information from invoices, contracts or applications and entering it into the right system
- Reporting: collecting data from different sources and preparing recurring reports
- IT support: suggesting or applying fixes for common issues
- Sales and research: compiling public information about a customer or market into a summary
What they share: repetitive work involving multiple steps and systems.
Connecting agents to systems: MCP
An agent is only as useful as the tools it can reach. The Model Context Protocol (MCP) has quickly become a widely used open protocol for connecting AI applications to tools and data sources in a standard way. A connector built once for a corporate system can then be reused across different AI applications.
Risks: the power to act brings responsibility
Because agents can take actions, their risks are higher than those of chat assistants:
- Permissions: an agent should not access more systems or data than it needs. Apply the principle of least privilege.
- Prompt injection: instructions hidden in an email or web page the agent processes can mislead it.
- Error cascades: a small mistake in one step can grow in later steps.
- Cost: multi-step agents can make more model calls than expected.
- Accountability: what the agent did, and why, must be traceable.
That's why human approval is essential for critical actions such as payments, messages to customers or deleting records.
How to get started
- Pick the right process: start with something repetitive, rule-based and low-risk.
- Start small: let the agent prepare drafts first, with a human taking the final step.
- Limit permissions: define exactly which systems and data the agent can access.
- Measure: track time saved, error rates and cost from day one.
- Prepare your teams: process owners need to understand what agents can and can't do.
Conclusion
Used in the right process with the right controls, AI agents deliver serious productivity gains. But a "let the agent do everything" approach can lead to uncontrolled permissions and errors.
Our AI Agents & Process Automation programme teaches business teams to build agent and automation flows without code, and to use them safely. If you want agents connected to your enterprise systems, we can work together through our Generative AI & LLM Solutions service.
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