AI Agents: More Than a Better Chatbot
When you ask ChatGPT something and get an answer, a classic language model is at work. When Claude independently analyzes your inbox, prioritizes appointments, writes a reply, sends it, and logs the result — that's an AI agent.
The difference is fundamental. And it's changing what's possible with AI in a business context right now.
What Is an AI Agent?
An AI agent is an AI model that:
- Plans independently: It breaks down a complex task into subtasks
- Uses tools: It can call APIs, query databases, execute code, read and write files
- Makes decisions: It chooses the next step based on intermediate results
- Iterates: It checks its output and corrects it as needed — all without human intervention
This sounds like science fiction. But it's already reality today — within limits.
Where Agents Actually Work Today
Research Agents
You give the agent a research task: "Create a competitive analysis for our three biggest competitors." The agent independently searches the web, extracts relevant data, compares it, and delivers a structured report — in minutes instead of hours.
Code Agents
Claude Code, Devin, and similar systems can independently find bugs, implement fixes, write tests, and create pull requests. Without a developer initiating every step.
Sales Automation
An agent monitors your CRM, identifies stalled deals, researches new information about the customer, drafts a personalized follow-up email, and presents it for approval.
Data Analysis
Instead of having an analyst battle spreadsheets for hours, an agent independently runs SQL queries, interprets the results, and creates a management summary.
The Honest Assessment: Where the Limits Are
AI agents are impressive — but not infallible:
- Chained hallucinations: If an early step contains a small error, it can propagate and amplify through the entire chain.
- Complex dependencies: With tasks involving many interlocking systems, error rates increase.
- Missing context sensitivity: What seems "obviously wrong" to a human isn't always recognized by an agent.
- Costs: Agentic AI workflows are significantly more expensive in API usage than simple chatbot requests.
Our advice: Start with agents for clearly defined, bounded tasks with measurable outputs. Build in human-in-the-loop mechanisms — not because you don't trust the agent, but because you want to maintain control.
What Should Businesses Do Now?
1. Identify processes that are rule-based, repetitive, and time-intensive — these are the best candidates for agents
2. Start small: One functioning agent for one task is more valuable than ten half-finished ones
3. Measure the output: Define upfront what "good" looks like — and check it systematically
4. Iterate: Agents improve through better instructions (prompts) and better tools
The question is no longer whether AI agents are relevant for your business — but when you start.
[Talk to us](/en/contact) — we'll help you find the right entry point.