Opinionated briefings and high-signal takes from the SuperML viewpoint.
The vector database you choose for your RAG system determines more than retrieval speed — it shapes your architecture's scalability ceiling, operational complexity, cost model, and what filtering capabilities you have at query time. This is the production comparison for teams choosing in 2026.
LangChain and LangGraph solve different problems and the choice between them is not about preference — it's about the shape of your workflow. This is the architecture decision guide: when chains are enough, when you need stateful graphs, and when to use neither.
Most AI architecture guides are written for startups deploying on greenfield infrastructure. Financial services has different constraints: regulatory audit requirements, latency SLAs on core banking integrations, data residency rules, fair lending exposure, and model risk governance. This is the pattern library for AI architects building production systems inside those constraints.
John Boyd designed the OODA loop for fighter pilots making life-or-death decisions in milliseconds with incomplete information. It turns out this is a better mental model for production AI agents than the ReAct loop — especially in high-stakes, time-pressured environments where agents need to fail fast, course-correct, and maintain situational awareness across a multi-step decision horizon.
Fresh tutorials, walkthroughs, and practical AI/ML build notes.
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.
A new benchmark shows models follow individual RAG instructions 84% of the time but satisfy all of them in only 27% of responses. Here is the architecture that handles noisy, missing, and conflicting evidence outside the prompt.
Most enterprises believe their AI agent inventory is complete. The data says otherwise. Here's how to build agent inventory as a continuously reconciled discovery pipeline, with a canonical agent record, risk tiering, and evidence that is ready before an examiner asks.
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.
Learn how Thompson Sampling solves the explore-exploit dilemma for real-time personalization. From Bayesian foundations to a full Python implementation for credit card offer ranking, with production deployment patterns.
The vector database you choose for your RAG system determines more than retrieval speed — it shapes your architecture's scalability ceiling, operational complexity, cost model, and what filtering capabilities you have at query time. This is the production comparison for teams choosing in 2026.
LangChain and LangGraph solve different problems and the choice between them is not about preference — it's about the shape of your workflow. This is the architecture decision guide: when chains are enough, when you need stateful graphs, and when to use neither.
Most AI architecture guides are written for startups deploying on greenfield infrastructure. Financial services has different constraints: regulatory audit requirements, latency SLAs on core banking integrations, data residency rules, fair lending exposure, and model risk governance. This is the pattern library for AI architects building production systems inside those constraints.
John Boyd designed the OODA loop for fighter pilots making life-or-death decisions in milliseconds with incomplete information. It turns out this is a better mental model for production AI agents than the ReAct loop — especially in high-stakes, time-pressured environments where agents need to fail fast, course-correct, and maintain situational awareness across a multi-step decision horizon.
Hey there! Ready to dive into Machine Learning Stacking A Visual Guide? This friendly guide will walk you through everything step-by-step with easy-to-follow examples. Perfect for beginners and pros alike!
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