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

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 dashboard reads healthy. Healthy is not the same as explainable.

This episode breaks down the gap between infrastructure monitoring and decision-level tracing. Why a 200 status code and a timestamp tell you nothing about why your agent approved a refund it should have flagged. What a real decision trace contains. And why, if you cannot reconstruct why your agent made a decision, you are not running it. You are watching it.

Keywords: AI agents, agent observability, agentic AI, AI orchestration, decision-level tracing, LLMOps, AI governance, AI accountability, production AI, enterprise AI, AI audit trail, CTO, structured tracing

This is Maya. New episodes three times a week.

youtube.com/@mayabuildsai

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 and total spend. You do not know which prompts are the noisiest, which agents are retrying failed calls fifty times a day, or which internal tool is quietly consuming more budget than the customer-facing product.

This episode walks through where AI costs actually hide in production. Why vendor dashboards show consumption without attribution. How debug modes, retry loops, and power users drive invoices up without anyone noticing. What cost attribution at the workflow level actually requires. And why your CFO is going to ask for it before your engineering team is ready to answer.

This is Maya. New episodes three times a week.

youtube.com/@mayabuildsai

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. Marketing auto-generates copy without review. Operations reclassifies tickets using prompts nobody outside their team has seen. Finance summarizes contracts from templates stored in personal Google Drives.

Nobody did anything malicious. They used approved tools in ways your governance layer cannot see.

This episode walks through the difference between shadow IT and shadow workflows and why the second one is harder to detect and more dangerous.

Shadow IT was unapproved tools. Your security team learned to catch those. Shadow workflows are approved tools used in unapproved ways. The API key is valid. The permissions are correct. Your governance layer sees an approved tool being used by authorized users. What it does not see is what those users are asking the tool to do, what prompts they are using, what data they are feeding in, and what decisions are being made on the outputs.

We cover how shadow workflows create conflicting sources of truth across departments. Why they are invisible to traditional governance frameworks. How they surface during audits, incidents, and data events. And what observability across your full AI estate actually requires.

If your visibility into AI usage stops at what the central team deployed, you are missing half the picture.

Keywords: shadow AI, shadow workflows, AI governance, AI compliance, AI observability, enterprise AI, LLMOps, MLOps, AI security, AI audit, unauthorized AI workflows, AI risk management, AI orchestration, AI infrastructure, CTO, CISO, regulated industries, healthcare AI, financial services AI, legal AI

This is Maya. New episodes three times a week.

youtube.com/@mayabuildsai

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. Contradictory entries from three different systems of record.

You were never testing AI performance. You were testing how well it handles your imagination of reality.

This episode walks through why AI systems that perform well in testing fail in production. Why the gap between curated test data and messy real data is where your failure rate actually lives. Why AI models silently improvise when data is incomplete and nobody logs it. And what production-realistic evaluation actually requires.

This is Maya. New episodes three times a week.

youtube.com/@mayabuildsai