An AI agent is a system that uses a model to pursue a goal over multiple steps. It observes state, chooses an action, calls tools, evaluates the result, and repeats until it reaches a stop condition or hands control back to a human.
The useful distinction is not “agent” versus “chatbot.” It is whether the system can safely take actions over time with enough context, tooling, and supervision to be useful.
Agent loop
flowchart LR Goal["Goal"] --> Observe["Observe state"] Observe --> Plan["Plan next step"] Plan --> Act["Call tool or respond"] Act --> Evaluate["Evaluate result"] Evaluate -->|"Continue"| Observe Evaluate -->|"Done or blocked"| Stop["Stop or hand off"]
The loop should be explicit in production systems. Hidden loops are hard to cap, audit, and debug.
Building blocks
- Instructions. The durable rules for role, behavior, output shape, and boundaries. See prompt engineering.
- Tools. Typed functions the model can invoke, such as search, code execution, database lookup, or ticket updates.
- State. The current task, intermediate results, tool outputs, and decisions already made.
- Memory. Optional longer lived context, usually retrieved rather than blindly appended.
- Retriever. A RAG pipeline that brings relevant documents into the task.
- Evaluator. Rules, tests, model checks, or human review that judge whether the next step is safe or useful.
- Control policy. Budgets, iteration limits, approval gates, and stop conditions.
Tool design
Tools should be narrow, typed, and observable. A tool named run_shell_command is powerful but risky. A tool named get_invoice_status is easier to validate and audit. Destructive tools should require confirmation, and every tool call should record inputs, outputs, latency, and errors.
The model should not infer hidden side effects. Tool descriptions need to say what is read, what is written, and what approval is required.
Memory and context
More context is not automatically better. Agents need the right context at the right time.
- Use short-term state for the active task.
- Use retrieval for reference material.
- Use durable memory only for facts that should influence future tasks.
- Avoid storing sensitive data unless the product has a clear retention and deletion model.
Production concerns
Agents multiply failure modes because they combine model uncertainty with external side effects.
- Runaway loops that spend budget without progress.
- Tool misuse caused by vague schemas or missing validation.
- Silent partial success, where some steps worked and others failed.
- Stale retrieved context.
- Prompt injection through documents, websites, or tool outputs.
- Overconfident completion when the goal is only partly satisfied.
Evaluation
Evaluate the whole loop, not only the final answer. Track task success, tool error rate, retry count, human intervention, cost, latency, and policy violations. Keep representative traces so regressions can be replayed after model, prompt, or tool changes.
When to use agents
Use an agent when the task requires multi-step adaptation, external tools, and judgment under changing state. Do not use one for a deterministic workflow that a normal job, form, or rules engine can handle. The agent should earn its complexity.