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.

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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

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 tower.

This episode breaks down why running multiple AI agents without a coordination layer leads to conflicting actions, out-of-sequence execution, and failures no single dashboard can see. Why the model quality is rarely the problem. What an orchestration layer actually does, using air traffic control as the frame. And what good looks like when every task is routed, sequenced, conflict-checked, and logged.

The model was never the problem. Nobody built the tower.

Keywords: AI orchestration, agentic orchestration, AI control plane, multi-agent systems, AI coordination, AI governance, AI observability, LLMOps, enterprise AI, AI infrastructure, CTO

This is Maya. New episodes three times a week.

youtube.com/@mayabuildsai

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 can call every tool it exposes. Not the one you had in mind. All of them.

This episode breaks down why Model Context Protocol gives agents reach without governing it. Why a confusing input or a prompt injection can make an agent invoke a tool you never intended. Why most teams have no log of which tools their agent called or with what arguments. And what scoped, logged MCP access actually looks like.

MCP gives your agent reach. Scoping and logging decide whether that reach is safe.

Keywords: MCP, Model Context Protocol, MCP security, AI agents, agent tool access, AI governance, prompt injection, AI observability, LLMOps, enterprise AI, AI infrastructure, CTO, CISO

This is Maya. New episodes three times a week.

youtube.com/@mayabuildsai