How to build an AI agent?
Build an AI agent as a bounded application, not a magic prompt: define one outcome, give a model a small set of typed tools, execute tool calls in your code, persist only the state it needs, and stop or ask for approval at clear boundaries. Use a fixed workflow when steps are predictable. Add evals, permissions, tracing, budgets and rollback before the agent can change real data.
Why — the first-principles explanation
An agent is an application loop around a model. The model interprets a goal and may request a tool; your application validates that request, executes the tool with its own credentials, returns the result, updates state and decides whether to continue, stop or ask a person. The model does not directly own your database, inbox or payment account. That execution boundary is the foundation for security, billing and debugging.
First choose the architecture. Anthropic distinguishes a workflow, where code fixes the path through model and tool steps, from an agent, where the model dynamically directs parts of the process. A workflow is usually easier to test for a known sequence. An agent earns its complexity when the path is open-ended and the system must choose actions from real observations. More autonomy brings more latency, cost and opportunities for compounding errors, so start with the simplest design that meets the acceptance test.
The minimal loop is: goal and policy → model response → validate a structured tool call → authorize and execute → return a typed result → update state → evaluate the stop condition. Around it sit the controls that make a prototype a product: least-privilege credentials, idempotency, time and step budgets, approval gates for side effects, prompt-injection handling, sandboxing, traces, test cases and rollback. A framework can help with these concerns, but it cannot choose your permission model or prove that the result is safe.
Build one narrow vertical slice first. Measure task success, tool-call correctness, retries, latency, token and tool cost, human overrides and unsafe attempts. Expand tools or add specialists only when the data says the current boundary is the bottleneck.
An example that makes it click
Imagine a support agent that can read an order and draft a refund recommendation. The model can call `lookup_order` and `check_policy`; the application validates the customer identifier, enforces read-only credentials, logs the result and asks a human to approve any refund. Only after approval can a separate, idempotent `create_refund` tool run. If the policy lookup fails, the agent must say it lacks evidence rather than inventing a decision. The useful product is the controlled path and audit trail, not the chat persona.
How to do it
- Write the outcome, inputs, non-goals and acceptance test in one page. Name every side effect the system must never take without approval.
- Try a deterministic function, prompt chain, router or RAG workflow first. Choose an agent only when the number or order of steps genuinely depends on observations at runtime.
- Define one model contract and a small tool set. Give every tool a typed schema, narrow description, allowed caller, timeout, error shape and explicit read/write label.
- Implement the smallest loop in application code: receive a tool request, validate arguments, authorize it, execute it, return a structured result, update state and stop at a hard maximum of steps, time and cost.
- Use least-privilege credentials and separate read tools from write tools. Make writes idempotent, record an operation key and require human approval for money, deletion, external messages, permission changes or other irreversible actions.
- Choose state deliberately: keep the conversation, task state and retrieved documents separate; redact secrets; set retention; and make the agent able to resume or fail closed when context is missing.
- Add guardrails for input and output, including prompt-injection tests, untrusted tool results, schema validation, rate limits, sandboxing and a safe fallback when evidence is absent.
- Create an evaluation dataset from real tasks and edge cases. Measure final-task success, factual or policy correctness, tool selection, unsafe attempts, escalation rate, latency, retries and cost per accepted result.
- Instrument traces and every tool call. Inspect failures before adding another framework, model or specialist; a visible failure is cheaper than an autonomous one hidden in production.
- Deploy gradually with a dry-run mode, approval queue, feature flag, rollback and periodic review of permissions, models, prompts, costs and evaluation results.
Key facts
- Anthropic distinguishes workflows with predefined code paths from agents that dynamically direct their own process and tool use, and recommends starting with the simplest solution because agents trade latency and cost for flexibility.
- OpenAI describes agents as applications that plan, call tools, collaborate across specialists and keep enough state to complete multi-step work; its Agents SDK documentation starts with a single-agent quickstart before adding capabilities.
- Tool calling does not give a model direct authority: the surrounding application must validate the request, run the function with its credentials and return the result.
- OpenAI documents guardrails and human review as controls for blocking or pausing risky work before an agent continues.
- OpenAI’s dataset guidance recommends growing an evaluation set with edge cases and blind spots over time instead of trusting a single demo.
- Google’s Agent Development Kit combines agent prompts and tools with graph-based workflows, evaluation and deployment options; a framework still needs application-level permissions and acceptance tests.
- MCP uses a host, client and server architecture for providing context and tools; connecting a server does not remove the need to inspect its permissions, data flow and trust boundary.
- Multi-agent orchestration is a design choice, not a quality guarantee. More handoffs can add cost, latency, state complexity and new failure modes.
Build the smallest agent you can audit
Start with a bounded task, then compare frameworks, tools, permissions, evaluation effort and total operating cost before granting autonomy.
▶ The 60-second explainer (script)
How do you build an AI agent? Start with one outcome and ask whether the path is really open-ended. If the steps are predictable, use a normal workflow or prompt chain. If the model must choose actions from observations, build a small loop: give it typed tools, validate each request in your application, execute with least-privilege credentials, return a structured result, update state and stop or ask for approval. Put write actions behind idempotency and human review. Then add an evaluation dataset, traces, budgets, prompt-injection tests and rollback before production. The framework is optional; the control boundary is not.
What authoritative sources say
People also ask
Do I need an agent framework?
No. A small application can implement the model–tool–result loop directly. Adopt a framework when tracing, retries, state, handoffs or deployment needs justify it, and keep the underlying contracts visible enough to test.
What is the difference between an AI agent and a chatbot?
A chatbot can produce a reply. An agent is an application that can choose from allowed tools, observe real results, continue across steps and stop or request approval. A product can contain both interfaces.
Can an AI agent send money or delete data?
Only if your application exposes a tool with credentials that permit it. Keep irreversible actions separate, validate arguments, use idempotency and require a human approval or an equivalent control before execution.
Should I use RAG inside an agent?
Use retrieval when the agent needs current or private evidence. Keep ingestion, permissions, retrieval quality and citation checks separate from the agent loop; RAG is not a substitute for authorization or evaluation.
How do I secure an AI agent?
Start with least privilege, separate read and write tools, schema validation, short-lived credentials, sandboxing, rate and spend limits, prompt-injection tests, untrusted-result handling, approval gates, logs and rollback. Review the whole data flow, not only the prompt.
When should I build a multi-agent system?
Only when separate specialists have clear contracts, ownership and measurable value over one agent or a workflow. More agents add handoffs, latency, cost and state failure modes; test the simpler design first.
Can a beginner build an AI agent?
Yes. Begin with one read-only task, one model, one or two typed tools and a dry-run mode. Learn the loop and write tests before granting write access or adding a framework.
How do I evaluate an agent?
Create a dataset of representative tasks and edge cases. Score final outcome, tool choice, arguments, evidence, unsafe attempts, escalation, latency, retries and cost per accepted result. Re-run it after changing the model, prompt or tools.
How much does an AI agent cost to run?
Count model calls and tokens, tool/API charges, storage, browser or sandbox time, retries, human review and monitoring. A multi-step run can cost much more than one chat turn, so measure cost per successful outcome rather than only the model price.
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