Teams keep trying to make one protocol do both jobs — tool access and agent-to-agent negotiation. Here's why that's the wrong architecture, and what to build instead.

Bhanu Pratap ·

#ai-agents #ai-architecture #llm-infrastructure

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94% of teams have agent observability in place, yet only 12% say they can actually govern their agents — the gap isn't a tooling problem, it's an unassigned architectural responsibility.

Bhanu Pratap ·

#ai-agents #ai-governance #enterprise-ai

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New evidence shows agentic keyword search hits 94.5% of RAG faithfulness with zero vector store — here's how to decide whether your team still needs one.

Bhanu Pratap ·

#ai-architecture #llm-infrastructure #mlops

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The EU AI Act Omnibus deferred Annex III credit scoring compliance to December 2027 — but GPAI transparency enforcement goes live August 2, 2026. Banks using foundation models in production workflows have obligations they're not prepared for.

crazyaiml ·

#EU AI Act #GPAI #AI governance

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Standard RAG retrieves documents and lets the LLM generate field names freely — which means it will invent fields your schema never had. AK-RAG (Attribute Knowledge RAG) indexes your governed attribute catalog instead, and the LLM can only emit attribute_ids that actually exist. Here's the architecture and why it matters for regulated-industry AI.

crazyaiml ·

#RAG #AI architecture #LLM

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A Pennsylvania bank employee used an unauthorized AI tool on customer data — no hacker, no breach, no operational disruption — and the bank still filed an SEC Form 8-K. The first shadow AI disclosure is a blueprint for what your regulators are about to ask.

crazyaiml ·

#shadow-AI #AI governance #banking AI

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The CFPB eliminated disparate impact from ECOA on April 22 — but AI credit scoring teams that interpret this as clearance to drop bias testing are about to learn the hard way that fair lending risk lives in at least five other regulatory regimes they haven't fully inventoried.

crazyaiml ·

#fair-lending #credit-scoring #AI-governance

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ARPA-H is selecting teams this month to build the first FDA-authorized agentic clinical AI — a system that writes prescriptions, adjusts medications, and acts as a 24/7 cardiovascular care member. FDA has no published guidance for agentic AI validation, no clearance precedent, and no framework that covers autonomous clinical agents.

crazyaiml ·

#healthcare-ai #FDA #agentic-ai

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FinCEN's NPRM comment period just closed — and its effectiveness-based AML standard implicitly rewards AI adoption. The problem: banks rushing to deploy AI for compliance credit have no model governance framework, and OCC examiners are already asking about it in every exam.

crazyaiml ·

#AML #AI governance #model risk

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SpaceX's $60B acquisition of Cursor ends the era of multi-model, model-neutral AI coding platforms — and every enterprise team that built agentic CI/CD workflows in Cursor just inherited a vendor lock-in risk they didn't plan for.

crazyaiml ·

#agentic-ai #enterprise-ai #ai-architecture

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OpenAI replays 1.3M anonymized production conversations with candidate models before release — catching reward hacking and behavior shifts that adversarial evals miss entirely.

crazyaiml ·

#production-ai #ai-safety #llm-evaluation

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The FSB's 12 sound practices for responsible AI adoption include the most honest regulatory admission yet: human oversight of agentic AI in banking can't scale, so banks need AI to monitor AI.

crazyaiml ·

#banking-ai #ai-governance #FSB

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As autonomous AI agents move from demos to production — scheduling meetings, writing code, executing trades — most enterprises have no governance framework built for systems that act, not just predict. Here's what one looks like.

crazyaiml ·

#agentic-ai #ai-governance #enterprise-ai

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Most banks have the data to personalize credit card offers at scale. Most can't actually do it in production because their ML architecture wasn't designed for real-time decisioning. Here's what the right stack looks like — and why the wrong one loses to Amazon.

crazyaiml ·

#personalization #credit-card #ml-architecture

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Feature-Decision-Execution (FDE) is the layered architecture pattern that separates ML prediction from business logic from system action — the pattern that makes production ML systems maintainable, auditable, and safe to iterate on.

crazyaiml ·

#ml-architecture #feature-store #production-ml

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Wolters Kluwer's H1 2026 Banking AI Risk Index found 72% of banks lack kill switches or failure reporting for AI models — the minimum viable governance for agentic AI in production financial systems.

crazyaiml ·

#banking-ai #agentic-ai #ai-governance

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A hands-on tutorial on the REA (Resources, Events, Agents) framework applied to banking ontology — from McCarthy's 1982 origins to building a working OWL ontology with Python, RDFLib, SPARQL queries, and AI/ML integration patterns.

crazyaiml ·

#banking-ontology #rea-framework #fibo

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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.

Bhanu Pratap Singh ·

#vector database #RAG #Pinecone

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