AI workflows work best when the operator can inspect inputs, outputs, failure states, and handoff points. I learned this building three separate AI-powered products: ClinicaSync IA for medical appointment scheduling, JusTriagem IA for legal intake, and OmniAtende CRM for multi-agent WhatsApp customer service.
The first instinct is to build a single long chain: user message → LLM classification → action → response. That works for demos. In production, it breaks the moment you need to handle a message that doesn't fit your categories, or when the LLM hallucinates an appointment time that doesn't exist in the calendar.
What works better is what I call a 'checkpoint architecture.' Every step in the workflow produces an inspectable artifact: the raw input, the classified intent, the extracted entities, the proposed action, and the confirmation. If any step fails, the system falls back to a human handoff instead of sending garbage to the user.
For ClinicaSync, the workflow is: incoming WhatsApp message → intent classification (new booking, reschedule, cancel, question) → entity extraction (date, time, doctor name) → calendar availability check → confirmation message → booking creation. Each of those steps logs its input and output. When a patient writes 'quero marcar pra terça com o Dr. Silva,' the system extracts the entities, checks Dr. Silva's Tuesday slots, and proposes specific times.
The failure mode that killed our first version: the LLM would sometimes extract a date that was in the past. We added a simple validation layer between extraction and availability check — if the parsed date is before today, ask the user to clarify. That single guard eliminated 40% of our escalations to human operators.
For JusTriagem, the challenge was different. Legal intake requires collecting documents and assessing case viability. The AI agent conducts a structured interview via WhatsApp: What type of case? When did it happen? Do you have documentation? Each answer updates a case summary that gets sent to the lawyer as a pre-screening report.
The pattern I keep reusing: never let the LLM be the final authority on anything that touches real data. The LLM suggests, a deterministic layer validates, and the system executes. AI is the copilot, not the pilot.
I documented the full architecture of these agent workflows in AgentTrace Studio, an observability platform I built specifically to visualize agent execution traces, measure latency per node, and track token budgets across multi-step chains.