AI product development
AI Agent vs AI Chatbot: What's the Difference?
AI Agent vs AI Chatbot: What's the Difference? requires decisions about the difference between conversational answers and systems that plan, call tools and change external state. This guide explains the architecture, delivery and production practices needed to achieve a decision based on whether users need information, actions or a controlled combination of both.
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.
Retrieval, grounding and citations
A RAG pipeline needs source connectors, parsing, chunking, embeddings, metadata filters, retrieval and an update path. Store stable source identifiers and permissions with every chunk so deleted, changed or restricted content cannot remain silently available.
Evaluate retrieval separately from the generated answer. Require citations that link to the supporting source, use a relevance threshold and allow the assistant to say it lacks evidence. More context is not automatically better; unrelated passages can reduce answer quality.
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.
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.