AI product development
How to Build an AI Agent for Your Business
How to Build an AI Agent for Your Business requires decisions about bounded tool use, workflow state, permissions, approvals, idempotency and step-by-step audit logs. This guide explains the architecture, delivery and production practices needed to achieve an agent that completes one measurable workflow with human confirmation for consequential actions.
Define the AI job before choosing a model
Write the user request, the information available, the expected output and the consequence of a wrong answer. A narrow job such as classifying a support ticket or drafting from approved records is easier to evaluate than an assistant expected to handle every question.
Set refusal and escalation rules before implementation. The product should tell users what the AI can do, show when information is uncertain and make a person reachable when the task affects money, health, access or another high-impact decision.
Design tools with narrow permissions
An agent tool should represent one bounded operation with validated inputs, explicit authorization and an understandable result. Separate read tools from write tools and require confirmation before sending messages, moving money, deleting records or changing customer data.
Use idempotency keys for repeatable actions and log every tool call, argument, result and approval. Limit steps, time and spend per run so a planning loop cannot consume unbounded resources or repeat a consequential action.
Evaluate before and after launch
Build a dataset of representative, difficult and adversarial cases before tuning prompts. Score factual correctness, task completion, citation support, refusal behavior, latency and cost; a fluent answer is not evidence that the system worked.
Store traces with the model and prompt version, retrieved evidence and tool results, subject to privacy rules. Add production failures to the regression set so provider or prompt changes cannot quietly reintroduce them.
Keep authentication and secrets at trusted boundaries
The backend should own credentials, session validation and privileged integrations. Browser and mobile clients may store only the tokens needed for their session using platform-appropriate protections, and every data request still needs server-side authorization.
Plan expiry, refresh, logout, revocation and compromised-device response. A valid identity does not automatically grant access to another organization's record.
Model build cost and operating cost separately
Implementation includes workflow design, data preparation, prompts, tools, evaluation, safety, integration and monitoring. Monthly operations include input and output tokens, embeddings, vector storage, queues, observability and the people who review failures.
Estimate requests per active user and tokens per successful task. Smaller models, caching, shorter context and deterministic code for simple steps can reduce cost, but every optimization should be checked against the evaluation set.