All field notes

AI teams fail from the wrong roles at the wrong time and from hiring on credentials rather than production evidence. This checklist covers roles by phase, evidence to require, candidate evaluation, domain fit, build-versus-augment, and onboarding.

Models inherit every flaw in their data. This checklist covers provenance and rights, representativeness, labeling quality, leakage prevention, privacy, splits and versioning, and documentation for training and evaluation data.

Buying AI means buying a method and a team as much as a product. This checklist gives buyers the questions and evidence to demand across capability, method, security, IP, operations, stability, and references.

The EU AI Act applies to many organizations outside Europe. This checklist walks through scope and role, risk classification, prohibited practices, high-risk obligations, transparency duties, general-purpose model duties, and timelines.

A forward deployed engineer is only as effective as the first two weeks allow. This checklist covers mission scope, business owner, access, embedding, discovery plan, specification, and the first review, for both the client and the engineer.

Palantir invented the forward deployed engineer. How the model worked, why it spread across the AI industry, and what it means when you hire one today.

The real reasons US companies outsource AI—talent scarcity, cost, and speed—and how to do it without losing coverage, security, or control of your IP.

Evaluation is how a probabilistic system is proven correct. This checklist covers specification coverage, the golden dataset, task metrics, calibrated graders, safety tests, CI gates, production sampling, and reporting.

Fairness is a tested property, not a stated value. This checklist covers scope and protected groups, metric selection, data assessment, pre-launch and production testing, remediation, documentation, and governance for AI systems that affect people.

AI governance is a set of structures and controls, not a policy document. This checklist covers the register, ownership, policies, risk tiering, evaluation, oversight, audit, vendor risk, and cadence an enterprise needs.

The first AI incident arrives before the process is ready unless the process is written first. This checklist covers detection, containment, reconstruction, remediation, disclosure, root cause, and prevention for AI systems and agents.

A model that passed evaluation can still fail in deployment through skew, missing fallbacks, or an untested rollback. This checklist covers registry and approval, parity, progressive rollout, monitoring, rollback, and documentation.

AI systems can be up and fast while being wrong. This checklist covers what observability must include: traces, operational metrics, quality signals, cost telemetry, drift indicators, alerting with runbooks, redaction, and retention.

Pilots fail by proving nothing: no hypothesis, no baseline, no exit criteria. This checklist covers what a pilot must define before it starts and deliver before it ends, so the go or no-go decision is made on evidence.

AI systems create new personal-data flows through prompts, retrieval, logs, and model providers. This checklist structures a privacy impact assessment: data mapping, legal basis, minimization, transfers, rights, retention, risks, and mitigations.

A handoff is complete when the receiving team can operate, change, and extend the system without the builders. This checklist covers documentation, runbooks, evaluation assets, access, trained owners, support transition, and backlog.

Agentic AI rarely arrives as a clean spec. Why forward deployed engineers are how autonomous systems actually reach production—safely and with adoption.

US startups live on speed and runway. How offshore AI developers help you ship the hard AI bet fast, protect cash, and keep the capability you build.

Before an AI agent touches production systems, every item on this checklist should be true and evidenced. Specification, permissions, gates, evaluation, safety, reliability, observability, and operations.

APIs that expose AI capabilities carry latency, cost, non-determinism, and safety concerns ordinary APIs do not. This checklist covers contracts, streaming, idempotency, errors, versioning, authentication, limits, observability, and cost.

AI budgets that count only licenses and a build fee are wrong before they are approved. This checklist covers the full cost categories, the baseline and value case, platform amortization, run and oversight costs, contingencies, and governance.

AI systems are abandoned for human reasons more often than technical ones. This checklist covers communication, role redesign, involvement, training, trust calibration, adoption measurement, incentives, and resistance, for each deployment.

Chatbots fail publicly when they answer outside their knowledge, make promises, or strand users. This checklist covers scope, grounded knowledge, guardrails, escalation, evaluation, privacy, analytics, staged rollout, and operations.

AI spend is optimized by making it visible, then attacking the largest lines in order. This checklist covers attribution, caching, routing, context discipline, batching, budgets, review efficiency, and unit economics.