An AI agent is a language model given a set of tools and run in a loop: it decides which tool to call, you execute it, you feed the result back, and it repeats until the task is done. That open-ended control is what makes agents powerful and what makes them expensive, slow and hard to debug when misapplied. This guide covers the loop, the tools, and the guardrails, and when a plain workflow is the better choice.

Agent or workflow?

A workflow is a sequence of steps you code, calling the model at fixed points: extract, then classify, then summarize. An agent decides the steps itself. Use a workflow whenever the steps are known in advance, which is most of the time. Reach for an agent only when the task is genuinely open-ended and hard to specify up front.

Should you build one?

• ✓Complexity: is the task multi-step and hard to fully specify? If you can write the steps, write a workflow.
• ✓Value: does the outcome justify higher cost and latency? An agent run is many model calls, not one.
• ✓Viability: is the model actually good at this kind of task? Test on real examples first.
• ✓Cost of error: can a wrong action be caught and undone? Tests, review, a rollback path.

The loop

The core is small: send the prompt and the tool definitions; if the model returns a tool call, execute it, append the result, and send again; stop when the model returns a final answer or you hit a limit. Most SDKs give you a helper that runs this loop, with per-turn hooks for approval, logging and error handling, so you rarely hand-write it.

prompt + tools
     |
  model -> tool call?  --no--> final answer
     | yes
  execute tool (validate args, scope to user)
     |
  append result -> back to model
     |
  stop on: final answer | max steps | budget exceeded

Tools: narrow, validated, scoped

Each tool is a function the model can call. Keep them narrow (one clear job), validate every argument the model passes, and scope every action to the authenticated user on the server. The model proposes; your code decides whether to run it. Return failures as data ("order not found") rather than throwing, so the agent can react.

Guardrails, none of them optional

• ✓A hard cap on steps and a token budget, so a confused agent stops instead of looping forever.
• ✓Human approval for any risky or irreversible action: sending money, deleting data, emailing a customer.
• ✓Output validation: check the final answer against a schema or rules before you act on it.
• ✓Sandboxing: if the agent runs code or shell, isolate it from your real infrastructure.
• ✓Untrusted tool output: a document the agent retrieves can contain instructions aimed at the model. Keep retrieved content separate from your instructions, and never let it expand what tools are allowed.

Context management

Every loop iteration adds the tool call and its result to the context, which fills up fast on a long task. Summarize or clear old tool results, or split the work: a coordinator agent delegates reading-heavy sub-tasks to cheaper worker agents and only keeps their conclusions.

Observability

Log every step: which tool, what arguments, what result, how many tokens, how long. Without that trace, a bad answer is unexplainable, and you cannot tell whether the problem is the prompt, a tool, or the model losing the thread.

Workflow vs agent

An agent is a loop, a set of tools, and a pile of guardrails. The loop is easy; the guardrails are the work. Build the tools narrow and validated, cap the loop, require approval for anything irreversible, log every step, and reach for a workflow whenever the steps are actually knowable.