the moat

Enterprise & Governance

Agents that touch money, customers, or regulated data need more than a demo. This section translates model-risk discipline — SR 11-7, OCC, FFIEC, EU AI Act — into concrete controls for agentic systems. All examples generic, no vendor pitch.

The agent adoption ladder: personal → team → enterprise, without the faceplant

What actually changes when an agent graduates from one laptop to a team to a company — identity, secrets, review, cost — and the checklist for each rung.

Agent observability: what to log, what to alert on, and what to keep

You cannot debug, defend, or bill what you did not record. The trajectory-log specification, the four signals worth alerting on, and retention that satisfies both engineers and auditors.

FinOps for AI agents: metering token spend before it meters you

An architecture for agent cost governance: OpenTelemetry GenAI conventions for instrumentation, a gateway for enforcement, and the reporting dimensions finance will actually ask for.

AI agent governance for regulated industries: a practical framework

A working control framework for agentic AI in banks, insurers, and other regulated shops — translating model-risk discipline (SR 11-7, OCC, FFIEC, EU AI Act) into agent-specific controls.

Human-in-the-loop patterns that scale: approval design for agent actions

Naive HITL either rubber-stamps everything or drowns reviewers. Five approval patterns, a materiality matrix for choosing, and the metrics that tell you when to loosen the loop.

Prompt injection for agentic systems: a working threat model

When agents read email, web pages, and documents, every input is a potential instruction. The lethal trifecta, the controls that actually work, and the ones that only feel like they do.