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Enterprise AI infrastructure explained by someone who has seen what breaks in production. Every episode breaks down one concept that matters when you are running AI at scale in regulated industries. LLMOps. Orchestration. Governance. Compliance. Observability. The stuff between the models and the business logic that nobody talks about until something goes wrong. New episodes three times a week.
Unknown Host hosts Maya Builds AI.
You gave your AI agents access to thirteen platforms and now your strategy meeting is everyone drowning in insights nobody can use. One agent scrapes what is trending, one clusters themes, one writes a beautiful morning
Epic switched on a wave of AI across clinical care, the patient portal, and the revenue cycle. Art drafts notes and queues orders. Emmie answers patients in MyChart. Penny touches claims and appeals. On a good day, all o
Three phone calls. Three industries. Same AI agent failure pattern. In this episode, Maya walks through: Clinic – a caller reports chest pain, and the AI receptionist treats it like a routine scheduling request. Missing
Reddit keeps screaming that “AI agents are broken.” In this episode, Maya walks through three failure modes that show up again and again when teams ship agents into real workflows: The retry storm – everyone adds retries
Most teams think they “just use AI tools.” Regulators don’t care who trained the model. They care that your AI touches patients, money, or rights. If you plug an AI receptionist into a clinic, let an AI agent nudge credi
You paid for an AI agent. You got a chatbot in a trench coat. A chatbot answers. An agent acts. The real difference is whether it can finish work, hold its place across steps, and recover when one of them fails. This epi
Right now, someone on your team is pasting source code, customer records, or an internal document into a public AI model to move faster. It works. It also just left your company. Every prompt is a data export, and nobody
An AI agent was given access to a production database. It hit one thing it did not understand, guessed instead of stopping, and deleted the database along with every backup. Then it apologized. This episode breaks down w
This morning your AI agent worked one client's matter. This afternoon it is on another, and because it remembers, it carries the first client's context into the second. In law, that one disclosure can waive privilege. Th
Your AI agent just denied someone credit. Fast, clean, automated. Then federal law shows up and asks for the specific reasons, in writing, mailed to the applicant within days. In consumer finance, the AI decided is not a
A patient calls with a billing question. One lab result, one charge. Your AI agent answers it perfectly. To get there, it read the entire medical record. Oncology history, behavioral health notes, years of visits. It use
Your company has a dozen AI tools running right now. Each one works. So everyone assumes the system works. Here is the part nobody planned for. Every one of those tools is a plane in the sky. And there is no control towe
You connected an MCP server to your agent so it could actually do things. Query a database. Send an email. Update a record. Five minutes of setup. It worked. You moved on. The moment you connect that server, your agent c
Your AI agent took ten thousand actions today. A customer asks why one of them happened. And you cannot answer. Most teams running agents in production can see that the agent ran. They cannot see why it decided. The dash
Every month the AI invoice climbs. New projects, new agents, new experiments. Everyone says that is just growth. Ask one question. Which workflows burned this money? Most teams cannot answer. You know your total tokens a
Your central team deployed two models and three documented workflows. Clean architecture diagram. Fits on one slide. In reality every department has quietly wired those same approved models into their own stack. Marketin
In your test environment your AI looks brilliant. Every record is clean. Every field is filled. It is a world that does not actually exist. Then the system hits production. Half empty forms. Free text chaos. Contradictor
You ask the AI assistant to show your last transaction. It answers perfectly. Then another customer logs in. Different account. Different person. They ask about a delayed payment. And the AI replies using your transactio
You go to bed proud of your new AI agent. You wake up to 517 brand new tickets in your queue. None of them needed to exist. The agent was following its rules. If something looks off, create a ticket so a human can review
Your team approved an AI project. The demo was great. Leadership loved it. Someone used the phrase game changer in a slide deck. Three months later the tool is live. It is technically working. But something feels off. Yo
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