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.
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.
Microsoft just announced at Build 2026 that GitHub Copilot will replace GPT-4 Turbo with its own homegrown Polaris model in August — and enterprise teams running agentic coding workflows need to treat this as a model substitution event, not a feature upgrade.
Claude Code is now authoring 4% of all public GitHub commits. Opus 4.8's Dynamic Workflows can migrate entire codebases with hundreds of parallel subagents. The governance gap is no longer theoretical.
A production architecture guide for LangChain workflow management: how to combine LangChain and LangGraph, decide between workflows and agents, and design retries, state, memory, observability, human review, security, and cost controls.
NVIDIA's Vera Rubin NVL72 rack claims 10x lower cost per token and 10x inference performance per watt — and it just shipped to top AI labs. Here's what that means for enterprise LLM routing, agentic cost models, and the committed-capacity contracts your team is signing today.
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.
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.
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.
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.
Enterprise agent hubs can simplify governance, but they also create concentration risk when one platform controls orchestration, identity, policy, and model routing. This guide explains how to design an agent hub architecture that preserves portability, auditability, and resilience.
Fiserv just launched an agentic AI operating system natively wired into its core banking stack — and the governance, policy enforcement, and audit trail all live in the vendor layer. That's not just a product announcement; it's the moment your core vendor became your AI risk manager.
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.
OpenAI's Deployment Company and Tomoro acquisition show why enterprise AI fails after the demo: integration, governance, data access, observability, and FDE handoff risk.
FIS and Anthropic's Financial Crimes AI Agent promises to compress AML investigations from days to minutes — but the production requirements for a regulated, auditable, human-supervised agent in anti-money laundering are more demanding than any demo will show you.
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.
AI evaluation has crossed a cost threshold that fundamentally changes who can afford to verify what they're deploying. The Holistic Agent Leaderboard spent $40K on a single benchmark sweep — and that number reveals a structural crack in how enterprise teams govern production AI.
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.
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.
Everyone's deploying AI, but most enterprise systems are reasoning over raw data with no shared understanding of what that data means. Ontologies fix that — and Palantir built an entire $50B platform on this idea. Here's what you need to know.
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.
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.
Most enterprise AI systems fail in production not because the models are wrong, but because nobody defined what 'customer', 'transaction', or 'risk' means consistently across systems. This is a practical implementation guide for building the semantic layer that makes AI grounded, governed, and production-ready.
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.
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.
OpenAI's $10B 'Deployment Company' and Anthropic's $1.5B Blackstone-Goldman venture both announced May 4 — and both use the same playbook: embed engineers inside your company, redesign your workflows around their model. Here's what that actually breaks in your AI architecture, governance stack, and vendor strategy.
Datadog's State of AI Engineering 2026 report found 5% of all LLM calls fail in production — and 60% of those failures are caused by rate limits, not model quality. Here's what that number actually means for enterprise AI systems and why finance teams should be alarmed.
A production deployment study from Salesforce's Agentforce platform — 722K daily inferences peaking at 1.4M — reveals the three compound AI failure modes that will silently kill your enterprise agent deployment. Here's the architecture that survived.
From hybrid spiking-neural-network agents to GPT-5.4's unified reasoning-plus-coding brain, the cutting edge of AI has decisively shifted from 'bigger' to 'smarter.'
Chunking directly affects retrieval quality, answer grounding, latency, and cost in RAG systems. This guide explains five practical chunking strategies, when to use each one, and how to evaluate chunk quality in production pipelines.
A production RAG system is more than a vector database connected to an LLM. This guide breaks down the core components: ingestion, chunking, embeddings, retrieval, reranking, prompt assembly, generation, evaluation, observability, and governance.
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