Bhanu Pratap Context Rot Is a Memory Architecture Problem
Agents degrade well before their context window fills — a measurable failure mode called context rot. Fixing it means building an explicit memory layer, not stuffing more into the prompt.
Articles about llm-infrastructure
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Why Multi-Agent Systems Break at the Handoff
A 1,600-trace failure taxonomy shows 79% of multi-agent failures come from specification and coordination problems at agent boundaries, not from any individual agent's reasoning — here's what that means for how you architect handoffs.
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Prefill, Not Decode, Is Your Agent's Real Bottleneck
Time-to-first-token, not tokens-per-second, is what's quietly breaking under RAG and multi-agent workloads — here's why prefill and decode need separate infrastructure, and how to build for it.
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Access Control Isn't Action Control for AI Agents
IAM answers whether an agent can enter a system. It says nothing about whether a specific action, at this moment, under these instructions, should proceed — and that gap is where production incidents are happening.
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Your Agent's Cache Hit Rate Is Lying to You
The KV cache hit rate in your traces and the KV cache hit rate your GPUs actually deliver are two different numbers — and the gap between them is quietly setting your inference bill and your agent latency floor.
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Why Cache Economics Now Dictate Context Architecture
Anthropic's Fable 5.1 cache-read pricing quietly rewrote the economics of agent context design — a frozen prefix is now 40x cheaper than a mutable one, and three common prompt patterns just started failing.
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Why Agent Permissions Drift After You Ship
Agent access models look fine at launch and then quietly widen with every new tool and workflow. Here's the architecture for provisioning, monitoring, and revoking agent permissions before drift becomes a breach.
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Tracing Isn't Evals: The Agent Reliability Gap
Most enterprise agent teams have full tracing but no evaluation loop — and that gap, not the model, is why production accuracy runs 20+ points below benchmark.
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Why Agent Browser Tools Need Their Own Runtime
Headless Chromium is the default 'browser tool' for AI agents, and it's quietly becoming the most expensive, least reliable line item in agent infrastructure. Two hyperscaler moves this month show what replaces it.
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Why Agentic Workloads Break Your Inference Stack
Agent loops are decode-bound, not prefill-bound, and most enterprise serving stacks are still sized for the wrong bottleneck. Here's what changes in your architecture and why disaggregated inference is becoming the default pattern, not a hardware shopping list.
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MCP vs A2A: Why Your Agent Stack Needs Both
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.
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Agent Governance: Why Nobody Owns Agent Behavior
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.
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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.
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Vector Database Comparison for RAG 2026: Pinecone vs ChromaDB vs Redis vs Weaviate
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.
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Fintech AI Architecture Patterns 2026: A Production Pattern Library for Regulated Financial Services
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.
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RAG Pipeline Production Architecture 2026: Chunking, Retrieval, Re-ranking, and Evaluation
Most RAG tutorials get you from zero to a working demo in 30 minutes. Production RAG takes 6–12 months to get right, and the problems that sink it are not the ones covered in the tutorial. This is the production engineering guide: chunking strategy, hybrid retrieval, re-ranking, evaluation frameworks, and the operational patterns that keep RAG systems working after launch.
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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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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.