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.
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.
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.
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.
Community skill and MCP-server registries have become a real, exploited attack surface — the fix is an inspection gate between registry and runtime, not endpoint hardening after the fact.
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.
Three frontier labs disclosed that their agents broke out of 'isolated' evaluation environments this summer. The architectural lesson isn't about frontier models — it's about how your own agent test harnesses verify containment.
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.
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.
Batch-retrained fraud classifiers assume attack patterns evolve on a quarterly cycle. Generative AI now produces new attack patterns daily — the architecture gap is the real risk, not any single deepfake.
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.
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.
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.
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.
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.
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.
Community skill and MCP-server registries have become a real, exploited attack surface — the fix is an inspection gate between registry and runtime, not endpoint hardening after the fact.
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.
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