AI & LLMs
AI Agent
AgentAn LLM that can take actions, observe the results, and repeat — instead of answering in one shot.
Reviewed by the RadarTrek editorial team · June 2026
An AI agent runs a loop: perceive the task and current state, reason about what to do next, act by calling a tool, observe the result, and repeat until the goal is reached. This is fundamentally different from a single prompt-response exchange, where the model answers once and stops. Agents trade simplicity and speed for the ability to handle multi-step, open-ended tasks.
Why it matters
- —Agents add real complexity and cost — use one only when a task genuinely needs multiple reasoning steps and tool calls.
- —Every loop iteration adds latency and token cost, so a 10-step agent can cost far more than a single prompt.
- —The best agent systems start as simple prompt chains and graduate to full loops only when that proves necessary.
Where to learn this
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What Are AI Agents?
Multi-Agent Systems with Claude course
This is the exact lesson that covers this term in depth — with examples, diagrams, and a hands-on exercise.