All field notes

RAG systems fail in retrieval far more often than in generation. Here is what to measure and what to demand from a partner.

Serverless suits some AI workloads and fits others badly. This guide covers duration limits, cold starts, streaming, and where the model breaks down.

Writing the specification before the code is the single highest-return discipline in distributed engineering. Here is how it works in practice.

Most companies overestimate where they are. This guide gives executives a five-stage adoption curve with observable markers for each, a way to locate the company honestly, and the specific moves that advance it one stage.

Interoperability decides whether your agents and connectors are assets or liabilities. This explainer covers what the emerging standards cover, why they lower cost and lock-in, and what to require in procurement.

When the marginal cost of doing work falls, business models change. This guide gives executives the five business models agentic AI is producing, what each requires, and how to decide which fits the company you run.

Everyone has the same models. This guide explains where durable AI advantage actually comes from: proprietary data, encoded process knowledge, integration depth, and operating discipline, and how executives build and measure them.

AI programs stall when nobody knows who decides. This guide gives executives a decision rights framework that assigns every recurring AI decision to a level, with the evidence each needs, so decisions are made once and made fast.

An AI gateway is the least discussed and most useful piece of AI infrastructure. This explainer tells executives what it controls, what it costs to skip, and why it should exist before the third agent, not after the first incident.

AI-native means designing systems and delivery around models rather than adding features to either. Here is what that looks like in practice.

Evaluation shows whether a system works; red teaming shows how it breaks when someone tries. This explainer covers what red teaming tests, who should run it, how often, and how to read findings sensibly.

AI traffic breaks assumptions gateways were built on: long requests, streaming responses, and cost that varies per call. This guide covers what to check.

Data residency is where AI deployments meet real legal constraints. This explainer covers where data actually travels in an AI call, what provider commitments cover, and what multinationals must check.

AI workloads change what you need from a warehouse: vector support, different query patterns, and cost that scales with experimentation.

A Digital FTE is an agent accountable for a workflow the way a person would be. Here is what that requires and how it is built.

AI systems need pipelines that handle unstructured content, schema drift, and expensive reprocessing. This guide covers what to compare.

Feature stores solve a real problem for predictive models and a smaller one for generative AI. This guide covers when the layer earns its place.

One accountable senior engineer embedded in your systems, owning one outcome from specification to production.

Speed and safety are presented as opposites, and the framing produces slow, unsafe programs. This guide shows executives why the tension is mostly a design failure, the structures that produce both, and how to decide the cases where tension is real.

The first agent decides whether an AI program gains credibility or spends it. This guide gives executives a scoring method, the tempting candidates that usually fail, and what a good first choice looks like.

Model capabilities, prices, regulation, and competitors all move faster than planning cycles. This guide gives executives a way to lead anyway: separate what is uncertain from what is not, make robust decisions, and keep the organization steady.

An AI town hall is where leadership credibility on AI is won or lost. This guide covers what to say, the questions that will come, how to handle the one everyone is waiting for, and the follow-through that matters.

The headcount question is the one every employee is waiting for an answer to. This guide gives executives a way to think about it clearly: capacity first, three honest options, a sequencing that protects the program, and candor about the decision.

Board memos on AI are usually too long, too technical, and too optimistic. This guide gives executives a one-page structure, the numbers directors actually need, and how to report problems without losing confidence.