AI agents are systems that receive a goal, interpret current state, choose an action and use tools until they reach an outcome or a stopping condition. A language model may guide decisions, but the complete product also needs instructions, data, permissions, execution, observability and evaluation.
Autonomy is not binary. A system may suggest a reply, execute after approval or operate through several bounded steps. Start with risk and the desired outcome rather than the agent label.
How an AI agent works
A typical loop has five parts: a goal with completion criteria; permitted context; a decision to answer, ask or use a tool; an action in another system; and an evaluation that determines whether to continue. The loop needs limits for steps, time, cost and permissions.
Essential components
Model and instructions
The model interprets inputs and selects actions. Instructions define scope and escalation. They do not replace authorization in the system that owns a sensitive operation.
Tools
Tools turn language into operational capability. Each tool needs a clear contract, validated parameters, least privilege, a timeout and explicit error handling.
State and memory
State supports the current run; memory persists information across runs. Store only information with a purpose, retention period and correction mechanism.
Guardrails, approval and observability
Guardrails validate inputs, outputs and tool calls. Human approval belongs before irreversible or high-impact actions. Traces should capture decisions, tools, errors, latency, usage and outcomes without exposing secrets.
Agent, chatbot or automation?
| Approach | Best fit | Strength | Main risk |
|---|---|---|---|
| Chatbot | questions and guidance | simple interaction | plausible but unverifiable answer |
| Deterministic automation | stable rules | predictability | brittle when inputs vary |
| AI agent | variable task with tools | contextual adaptation | wrong action, cost and test complexity |
Choose the least autonomous approach that can deliver the outcome. Hybrid workflows often keep most steps deterministic and use AI only where interpretation is needed.
Common patterns
- tool-using agent;
- intent router;
- manager agent coordinating specialists;
- handoff to a specialist;
- agent that pauses for approval;
- hybrid workflow with deterministic checks.
Adding agents is not an objective. Split responsibilities only when it improves permission boundaries, evaluation or maintainability.
Evaluation and economics
Build a representative test set before launch. Include normal, ambiguous, incomplete, adversarial and prohibited cases. Record expected outcomes, allowed tools, cost and step limits, evidence requirements and escalation conditions.
Measure task completion, tool accuracy, safety, latency, cost and escalation quality. The useful economic unit is cost per correctly completed task, not tokens alone.
Risks and boundaries
Agents may choose the wrong tool, trust malicious content, expose data, repeat actions or accumulate excessive privileges. Use least privilege, isolation, confirmation for sensitive actions, execution limits, parameter validation, logs and rollback.
Connect these controls to the AI governance guide. To move from idea to implementation, use how to build AI agents and the AI agent evaluation template.
Do not use an agent when the process is fully predictable, success cannot be measured, data access cannot be secured or the action cannot be monitored and reversed. The right agent is not the most autonomous one; it is the system that completes a useful task with evidence, known cost and proportionate limits.
Frequently asked questions
What are AI agents?
AI agents are systems that receive a goal, interpret context, choose actions and use tools to produce an outcome within defined boundaries.
How is an AI agent different from a chatbot?
A chatbot usually responds to messages. An agent can also query systems, execute actions and repeat steps until it completes a task or reaches a limit.
Does every process need an agent?
No. Predictable workflows with stable rules are often safer and cheaper as deterministic automation. Agents help when variation and contextual decisions are real.
AI Agents
Understand, build, evaluate and operate AI agents with clear goals and boundaries.