CFPB removed disparate-impact liability from Regulation B, and most lenders read it as a green light to ease AI governance. Freddie Mac, Fannie Mae, state examiners, and the Fair Housing Act didn't get that memo.
The Fed, OCC, and FDIC are now asking banks seven specific questions about AI agent controls in routine exams — while SR 26-2, issued in April, explicitly excludes agentic AI from its scope. The gap is already live.
FDA's August 2026 discussion paper proposes a competency-based framework for generative AI medical devices — two risk axes, ten benchmark elements, five clinical confirmation rungs. The comment window closes October 19. Most manufacturers haven't mapped their products to it yet.
Post-hoc log review catches agent failures after the damage is done, and a guardian agent on every step doubles your cost. A streaming trajectory monitor offers a third design, and the access regime you have determines how much it can see.
The Digital Omnibus pushed the EU AI Act's Annex III high-risk deadline from August 2026 to December 2027 — exactly one conformity assessment cycle. Banks that treated the delay as breathing room are already behind.
An analysis of 150 production incidents finds that compound AI systems break at component boundaries, and half of those failures are silent. Here is how to place circuit breakers, quality gates, and typed interfaces where the damage actually starts.
Most teams treat a green eval run as permission to ship an agent change. The data says that's the wrong contract. Here is how to build risk-tiered release gates where evals narrow the review queue instead of replacing it.
A production agent that crashes loudly is a solved problem. One that converts an error into a fluent, believable answer is not. Here is the failure class, why tests miss it, and the architecture that contains it.
A max_budget setting on your LLM gateway is a promise, not a control. Here is why budget enforcement silently fails in multi-replica agent deployments, and the reserve-then-settle architecture that actually holds.
Full-pass agent benchmarks now cost hundreds to thousands of dollars per run. Early outcome prediction halts runs whose result is already evident, and the architecture around it decides whether your rankings survive.
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