What Are AI Agents? A Practical Guide for Business Leaders
AI agents plan, use tools, and complete multi-step work with limited supervision. Here's what that means in practice, and where they create real leverage.
An AI agent is a system that can plan a sequence of steps, call external tools or APIs, and complete a multi-step task with limited human input. That's different from a chatbot, which only responds to one message at a time and has no memory of what it's supposed to accomplish beyond the current turn.
If a chatbot is a very knowledgeable person answering questions over email, an agent is closer to an employee handed a task ticket: it figures out the steps, uses the tools available to it, checks its own work, and reports back — or asks for help when it hits something outside its scope.
What makes something an "agent"
Three properties separate an agent from a simpler AI feature:
- Planning. The system breaks a goal into steps rather than requiring each step to be specified by a human.
- Tool use. The system can call external functions — query a database, send an email, update a record — not just generate text.
- Bounded autonomy. The system completes several of these steps in sequence without a human approving each one individually.
None of that requires the system to be unsupervised end to end. In fact, the agents that work reliably in production are usually the ones with the narrowest scope, not the broadest.
Where agents create real leverage
The workflows where agents earn their complexity tend to share a pattern: multiple steps, multiple systems, and enough volume that a human doing it manually is either slow or expensive.
- Research and synthesis — gathering information across several sources and producing a structured brief
- Cross-system workflows — pulling data from one tool, transforming it, and pushing it into another
- Operational monitoring — watching for a condition and taking a predefined corrective action
- Triage and routing — reading an incoming request and directing it to the right process or person
Where agents are the wrong tool
Not every task benefits from agentic autonomy. A single, well-defined transformation — summarize this document, classify this ticket — is usually better served by a simpler, more predictable single-call AI feature. Agent architectures add latency, cost, and failure surface area. They're worth that cost only when the task genuinely requires multiple adaptive steps.
How we scope agents at Tercer Labs
Every agent we build starts with an explicit answer to three questions: what tools can it call, what's the maximum number of steps it can take without human review, and what happens when it's uncertain. That scoping work is most of the engineering effort — the model call itself is the easy part.
Read more about how we approach this in AI Agents, or see how it compares to simpler automation in AI Agents vs Traditional Automation.