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.

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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.
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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

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

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

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

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

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.

This episode breaks down why a single AI agent shared across client matters is a structural confidentiality risk. Why privilege is fragile and waiver can be permanent. Why cross-matter bleed is a conflict and an ethics problem, not just a technical bug. And what per-matter isolation looks like when the wall is built and logged.

Privilege is binary. It holds, or it is waived.

Keywords: attorney-client privilege, legal AI, AI agents law firms, client confidentiality, matter isolation, conflict of interest, AI governance, legal tech, agentic AI, CTO, general counsel

This is Maya. New episodes three times a week.

youtube.com/@mayabuildsai

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 valid adverse action notice. This episode breaks down why automating credit decisions without capturing the reasoning turns every denial into a compliance exposure. Why the decision lives in a layer nobody logs. Why you cannot reconstruct a reason you never recorded. And what a decision record looks like when it is built to satisfy the rule instead of fight it.

An automated decision you cannot explain is a regulatory liability with a deadline attached.

Keywords: adverse action notice, ECOA, Regulation B, AI lending, AI credit decisions, explainable AI, AI decisioning, fintech AI, AI governance, AI compliance, financial services AI, CTO

This is Maya. New episodes three times a week.

youtube.com/@mayabuildsai

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 used one line. It saw all of it.

Nothing leaked. And it is still a HIPAA violation.

This episode breaks down the minimum necessary rule and why over-access alone is a breach, even with no leak and no hacker. Why agents pull every record they can reach by default. Why logging the answer is useless if you never logged what the agent read to produce it. And what scoped, logged access looks like when it is built before the auditor asks.

A HIPAA breach does not require a leak. Over-access is enough.

Keywords: HIPAA, minimum necessary, AI agents healthcare, PHI access, healthcare AI compliance, AI governance, AI observability, scoped access, agentic AI, CTO

This is Maya. New episodes three times a week.

youtube.com/@mayabuildsai