Bhanu Pratap Do You Still Need a Vector Database for RAG?
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.
Articles about MLOps
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OpenAI's Pre-Release Safety Trick: Make Models Think They're in Production
OpenAI replays 1.3M anonymized production conversations with candidate models before release — catching reward hacking and behavior shifts that adversarial evals miss entirely.
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FDE Architecture Framework: Build Production ML Systems That Don't Break
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.
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Google's Agent Stack Is Production-Ready. The Ephemeral Execution Model Underneath It Wasn't Built for Finance — and Most Teams Won't Find Out Until the Audit.
Google I/O 2026 shipped a complete agent stack — Gemini 3.5 Flash, Managed Agents API, Antigravity 2.0, and Agent Identity. The ephemeral-by-default execution architecture is elegant engineering and a potential compliance trap for any regulated industry running AI at decision-level stakes.
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MCP's Security Debt Just Came Due: Tool Poisoning Is in Production, 200,000 Instances Are Exposed, and Your Agents Can't Tell the Difference
A May 2026 OX Security disclosure revealed a systemic vulnerability across MCP SDKs with 150M+ downloads — and the real problem isn't the CVE, it's that most enterprise agent deployments have no controls in place to stop what comes next.
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OpenAI's Guaranteed Capacity Turns Your LLM Stack Into a Three-Year Bet — Here's the Architecture Your Team Needs to Win It
OpenAI's new Guaranteed Capacity offering looks like a cloud pricing deal. It's actually a multi-year architectural commitment that changes how teams design for reliability, route across models, and govern production AI — and most enterprise teams aren't modeling the risks before they sign.
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The Hidden Bottleneck Inside Every LLM Inference Stack — and Why llm-d v0.7 Just Made Disaggregation an Enterprise Architecture Decision
llm-d v0.7 ships predicted-latency scheduling to GA and joins the CNCF — forcing enterprise AI teams to confront the structural ceiling of monolithic inference and treat LLM serving as a real distributed systems problem.
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The Harness Does the Work: Inside Microsoft's 100-Agent MDASH Architecture That Found 4 Critical Windows RCEs — and Why 'Which Model?' Is the Wrong Question
Microsoft's MDASH multi-model agentic scanning harness found 16 Windows vulnerabilities including 4 Critical RCEs — not because of any single model, but because of a 5-stage pipeline of 100+ specialized agents. The architecture lesson rewrites how enterprise teams should think about agentic AI for production security work.
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OpenAI's $4B Deployment Company Proves Enterprise AI Has a Last-Mile Problem
OpenAI's Deployment Company and Tomoro acquisition show why enterprise AI fails after the demo: integration, governance, data access, observability, and FDE handoff risk.
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NVIDIA OpenShell Is Now in 17 Enterprise Stacks — and the Agent Runtime Governance Race Just Became an Infrastructure War
SAP Sapphire and Red Hat Summit both landed this week with NVIDIA OpenShell at the center of their agent architectures. When the same runtime sandbox shows up in 17 enterprise stacks simultaneously, that's not adoption — it's standardization, and it reshapes how you design production agent systems.
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MCP Bloat Tax: Token Economics and Context Waste in Enterprise Agents
Atlassian's new MCP tools cut token costs by 48% — which tells you everything about how badly MCP context bloat was burning enterprise budgets. Combined with ServiceNow and SAP metering agent data access, the real cost of cross-domain agent orchestration just got a price tag.
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The Seven-Model Problem: Enterprise AI Inference Has Left the Lab — and the Control Plane Hasn't Caught Up
F5's 2026 State of Application Strategy Report drops a number that should alarm every platform architect: the average enterprise is now running seven AI models simultaneously in production. The traffic cop that routes between them, governs them, and keeps them from burning your budget? Most enterprises don't have one.
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Forward-Deployed AI and Enterprise Lock-In Risk in 2026
When the two largest model labs simultaneously launched forward-deployed engineering ventures backed by Wall Street capital, they didn't just change how AI gets sold — they changed who owns your production AI architecture. Here's what that means for engineering teams trying to stay in control.
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Five AI Vendors Shipped Agent Registries in One Quarter — That's Not Competition, It's a Production Crisis Signal
When Microsoft, AWS, Google, ServiceNow, and Okta all ship 'agent registries' within weeks of each other, enterprise architects need to read that convergence carefully — because the agent inventory problem is now a compliance deadline, not a backlog item.
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Pinecone Nexus and Context Compilation: RAG at Agent Scale
Pinecone's pivot from vector database to 'knowledge engine' exposes a structural flaw in how enterprise teams built their RAG stacks — and signals a new architecture layer between raw data and agent runtime that will reshape how production AI systems are designed.
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Cerebras Files for $26.6B IPO With OpenAI as 86% of the Backlog: The Wafer-Scale Tier Just Became an Architecture Decision
Cerebras's S-1 lands with a 750 MW OpenAI inference contract, an $1B circular loan, and 86% revenue concentration in two customers — and quietly forces enterprise AI teams to make a routing decision they've been postponing.
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The Enterprise AI Control Layer Goes Live: Microsoft Agent 365, NVIDIA OpenShell, and the End of Shadow Agent Chaos
Microsoft Agent 365 hits general availability today — the enterprise control plane for AI agents. NVIDIA's OpenShell adds the open-source runtime half. Together, they mark the moment enterprise AI governance stopped being a whitepaper and started being a $15/month line item.
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Vertex AI Is Dead, Long Live the Gemini Enterprise Agent Platform: Google's Agentic All-In at Cloud Next '26
Google just retired the Vertex AI brand and replaced it with the Gemini Enterprise Agent Platform — a unified build-scale-govern-optimize stack for autonomous agents, backed by 200+ models and two brand-new TPU generations. Here's what it means for practitioners.
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🤖 Master Machine Learning: The Complete Guide That Will Transform You Into an AI Expert!
Explore what Machine Learning is, the key types, real-world applications, and the latest trends in AI.