All field notes

Nonprofits carry heavy administration on thin budgets, which is where agents help most. This guide shows nonprofit leaders where to start, what must stay human when beneficiaries are involved, and how to talk to funders.

Operating partners are asked whether AI is a value creation lever or a distraction. This guide gives a diligence checklist, a portfolio playbook that repeats across companies, and the measures that survive a buyer's scrutiny.

Partnerships face AI decisions that corporations do not: no retained capital, partners who must agree, and profit pools that change when leverage changes. This guide addresses the governance problem directly.

Public companies face AI questions from investors, auditors, and regulators at once. This guide covers disclosure discipline, controls over financial reporting, the investor narrative, and what the board should receive.

Scaleups hire to keep up, then discover the headcount was the problem. This guide shows scaleup leaders how to use agents to absorb growth load, hire differently, and build a platform before every team builds its own.

An agent scorecard turns a vague sense that it is working into numbers. This template covers completion, quality, escalation, cost, and safety measures.

A capacity model turns guesses into numbers you can defend. This template covers the inputs, the arithmetic, and the assumptions that need stating.

Coding agents write, test, and change software with limited supervision. This explainer gives executives the business meaning: where output rises, where the bottleneck moves, what can go wrong, and the policy and metrics to set.

A data map answers where data came from, where it went, and who saw it. This template covers what to record so the map answers real questions.

Evaluation results that nobody trusts change nothing. This template covers reporting scope, method, results, and limitations so the report supports a decision.

Evaluation is how a company knows an AI system works before customers find out it does not. This explainer gives executives the concept, the numbers to ask for, and why evaluation is the single practice that separates shipped AI from demos.

Hallucination is the reason executives hesitate to put AI in front of customers. This explainer describes what it is, why it happens, which controls reduce it to an acceptable rate, and how to know what that rate is.

When behaviour changes unexpectedly, the change log is what makes investigation possible. This template covers what to record per entry.

A risk assessment is useful when it names concrete failures and who they harm. This template covers scenarios, mitigations, and the acceptance that closes it.

Model comparisons age within a release cycle. This guide covers the dimensions that matter for agent work and how to run the comparison on your own workload.

Quality is a contract term, not a hope. Here are the standards to specify and how to verify them without reading every line.

Computer-use agents operate applications the way a person does, through the screen. This explainer gives executives what they are, where they fit next to APIs and RPA, the specific risks, and the decision rule for using them.

Pakistan's holiday calendar moves each year and Ramadan changes working hours. Both are entirely plannable if you ask early.

You do not need to read the research papers to understand an AI agent. This explainer walks through the five parts of an agent, what each one does, and which questions each part raises for a leader responsible for outcomes.

Vendor transitions fail when they start with a rewrite. Here is the sequence that recovers a system without repeating the original mistake.

A pilot is an information-buying exercise. Here is how to design one that answers the questions a proposal cannot.

Large language models are the engine behind every AI agent, assistant, and copilot. This explainer gives executives what they need: what LLMs do well, where they fail, how cost works, and the decisions that actually matter.

MCP is the standard that lets AI agents connect to business systems the same way, every time. This explainer gives executives the business meaning: lower integration cost, a central control point, and the risks to manage.

Prompt injection is the attack that turns an AI agent's permissions against its owner. This explainer describes how it works in business terms, why it cannot be filtered away, and the controls executives should require.