Maya Builds AI

Maya Chen

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.

  1. 1d ago

    Your AI Agents Watch 13 Platforms. None of Them Know What You Ship.

    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 deck. It feels powerful, right up until you realize the whole stack has no idea what you actually ship. This episode breaks down the quiet failure mode of trend agents that know every platform but not your product, the three filters a useful signal agent runs first, and the one question every item has to answer before it reaches your deck. An agent that watches everything tells you nothing. Keywords: AI agents, agentic AI, trend intelligence, signal vs noise, market intelligence, AI monitoring, product strategy, vertical AI, AI orchestration, AI infrastructure, founder, CTO This is Maya. New episodes three times a week. youtube.com/@mayabuildsai

    4 min

  2. 3d ago

    Epic Turned On AI Across Your Hospital. Who Owns It When One Feature Misbehaves?

    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 of it saves time. This episode asks the question that actually decides whether it is safe. When one of those features misbehaves, who notices, who owns it, and what proof do you have afterward? Maya breaks down why features ship faster than runbooks, and the four part bar every hospital needs before it flips the switch. Oversight, logs, boundaries, and runbooks. Features ship faster than runbooks. That gap is the whole risk. Keywords: Epic AI, healthcare AI, EHR AI, ambient scribe, clinical AI governance, AI oversight, audit logs, patient safety, revenue cycle AI, AI deployment, AI compliance healthcare, CMIO This is Maya. New episodes three times a week. youtube.com/@mayabuildsai

    6 min

  3. 6d ago

    Three Calls Your AI Agent Can’t Afford to Mess Up

    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 escalation rules turn clinical risk into a calendar slot. Bank – an AI agent helps deny a loan, and the adverse‑action letter just says “AI decision”. Without logged reasons, you can’t meet legal obligations. Law firm – an AI assistant mentions another client’s case on a call, bleeding privileged context across matters and creating privilege and ethics problems. Different industries, same core mistake: shipping AI agents into regulated workflows without escalation rules, decision records, or context walls. Keywords: AI agents, AI compliance, healthcare AI, fintech, legaltech, AI receptionist, clinical escalation, adverse action, attorney‑client privilege, digital labor, AI orchestration, enterprise AI, production AI This is Maya. New episodes three times a week.youtube.com/@mayabuildsai

    4 min

  4. Jul 1

    Reddit vs Reality: 3 AI Agent Failure Modes

    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 to “make it reliable”, and quietly creates a self‑inflicted DDoS and a runaway LLM bill. The memory leak – agents get “long‑term memory” with no hard boundaries, and start mixing one customer’s context into another. The decision nobody logged – agents influence real outcomes, but the reasoning lives in a layer with zero logging, so nobody can explain what happened later. These aren’t exotic edge cases. They’re how AI agents actually fail in clinics, banks, law firms, and enterprise ops when nobody plans for retries, memory boundaries, or decision logging. Keywords: AI agents, agentic AI, AI failure modes, AI in production, AI reliability, retry storms, AI memory leaks, AI decision logging, AI orchestration, AI infrastructure, platform engineering, DevOps, SRE, CTO, VP Engineering This is Maya. New episodes three times a week. youtube.com/@mayabuildsai

    3 min

  5. Jun 29

    You Don’t “Use” AI Agents. You Deploy Them.

    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 credit decisions, or route privileged calls through an AI assistant at a law firm, you’re not a casual user anymore—you’re a deployer. In this episode, Maya breaks down the difference between using and deploying AI agents in regulated workflows, the “last Tuesday” test for whether you’re really in control, and the four boring obligations that come with deployment: Oversight – someone owns what the agent is allowed to do Logs – you can answer “what did it do last Tuesday?” with specifics Explainability – you can show the reasons behind decisions that affect real people Boundaries – hard limits on what data the agent can see and when it must escalate If your AI agent touches care, credit, or privileged client information, you’re already a deployer. Better to build the oversight, logs, and boundaries on purpose than pretend you’re “just using AI” until someone with authority disagrees. Keywords: AI agents, agentic AI, AI deployer, AI compliance, AI regulation, EU AI Act, AI receptionist, healthcare AI, fintech, legaltech, AI governance, AI orchestration, AI infrastructure, CTO, VP Engineering This is Maya. New episodes three times a week.youtube.com/@mayabuildsai

    5 min

  6. Jun 26

    You Were Sold an AI Agent. You Got a Chatbot.

    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 episode breaks down the three step test that separates a real agent from a dressed up chatbot, why most agents stall or report false success, what Gartner means by agent washing, and the test you can run before you buy. The recovery is the whole job. Everything else is just typing. Keywords: AI agents, agentic AI, agent washing, agent vs chatbot, multi-step agents, agent recovery, state management, error handling, AI orchestration, AI infrastructure, build vs buy, CTO This is Maya. New episodes three times a week. youtube.com/@mayabuildsai

    4 min

  7. Jun 24

    Your Company's Private Data Is Already Inside a Public AI Model

    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 logged it. This episode breaks down why the real gap is the missing checkpoint on the way out, why you cannot unsend a prompt once it crosses the boundary, what Samsung learned in three incidents inside twenty days, and what an outbound boundary looks like when it inspects, blocks, and logs before the data leaves. You cannot unsend a prompt. So you stop it at the boundary. Keywords: AI data leakage, data loss prevention, shadow AI, enterprise AI security, LLM security, outbound boundary, proprietary data, model training data, AI governance, AI infrastructure, CISO, CTO This is Maya. New episodes three times a week. youtube.com/@mayabuildsai

    4 min

  8. Jun 22

    An AI Agent Deleted an Entire Company's Database in 9 Seconds

    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 why the agent destroyed everything instead of stopping, the three controls that were missing in every version of this incident, and what permission design looks like when an agent can take real actions in production. The model was not the problem. The access was. Keywords: AI agent safety, agent permissions, scoped access, least privilege, blast radius, approval gate, production database, agentic AI, AI governance, AI orchestration, AI infrastructure, CTO This is Maya. New episodes three times a week. youtube.com/@mayabuildsai

    5 min
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About

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.

Information

  • Creator
    Maya Chen
  • Years Active
    2K
  • Episodes
    20
  • Rating
    Clean
  • Copyright
    © Maya Chen
  • Show Website
    Maya Builds AI