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Learn AI-native work, in plain language.
Plain-language guides, diagrams and a glossary for leaders running an AI transformation: forward-deployed engineering, where AI belongs, model costs, evals, skills and governance.
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Guides
Each one opens with a short answer, explains the idea with diagrams and real studies, and cites its sources.
SeeWhat is a forward-deployed engineer?What FDEs do, the playbook they follow, and how to give every person the same capability.9 min readEquipWhat does AI-native actually look like?A plain definition, the five stages, an AI-native workday, and ten signs you are getting there.9 min readDecideWhen to use an AI agent: sort every step into four bucketsDelete, plain code, agent or human: a decision tree for every step, with real data and a worked example.10 min readDecideHow to choose an AI model by cost per taskMeasure cost per task, not price per token, and find where a cheaper model does the same job.10 min readEquipWhat are AI skills, and how do you share the ones that work?What AI skills are, how to share them across a company, and how to tell which ones work.9 min readProveWhat is shadow mode? Testing AI agents on real work before they go liveRun an agent on real work without sending anything, score it against your people, and promote it one step at a time.9 min readProveHow to benchmark AI on your own work: people, people with AI, and agentsCompare people, people with AI and agents on your own cases, and pick each workflow’s setup by cost per correct result.10 min readDecideBuild vs. buy AI agents: when to build, buy or foregoBuild, buy or forego: the criteria, the full cost of each path, and a worked example.10 min readEquipWhat is an AI agent harness? The software that turns a model into an agentThe software that turns a model into an agent: what it does, why it matters, and what to ask vendors about theirs.10 min readSeeHow to find where AI belongs: AI discovery vs. consultants vs. process miningSix ways to find AI opportunities compared, and how to rank what you find by value and readiness.9 min readSeeHow to map a process for AI: people, systems and docsA step-by-step method to map how a workflow really runs, from people, systems and docs, so every step is ready to sort.9 min readSeeWhat is agent experience (AX)? How to make your company navigable for AI agentsWhat AX means, why AI agents get lost inside companies, and how to give them what a new hire gets.11 min readSeeWhat is shadow AI? How to see which AI tools your people actually useWhat shadow AI is, why bans fail, how to find the AI tools people use, and how to turn what works into shared skills.9 min readProveHow to measure AI impact and ROI: cycle time, quality and cost per taskTake a baseline, track five measures per workflow, and show the board what AI changed.10 min readProveAI agent observability and governance: how to monitor agents after go-liveWhat to record for every agent run, what to watch after go-live, and the controls and frameworks that keep agents accountable.11 min readEquipSidekick agents vs. background agents: which work should each do?Which work suits an agent at your side and which suits one that runs on its own, plus approvals, risks and controls.10 min readEquipHow to write a PRD for an AI agent, starting from real workA 13-section agent PRD template, built from real work: evals that define “correct,” limits on what the agent may do, and a staged rollout.13 min readEquipWhat is an on-screen AI assistant? AI that sees your screen and helps in any appWhat AI that sees your screen can do today, where in-app help pays off, and which privacy controls to insist on.10 min read
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Tools
Interactive versions of the guides’ methods. Use them on your own workflows.
Topics
The library, by what you’re trying to do
Four steps take a company from AI tools to AI-native work. Every guide and term sits under one of them.
01 · See
See how work really happens
Map the real process from people, systems and docs, and find the know-how that lives in people’s heads.
- What is a forward-deployed engineer?
- How to find where AI belongs: AI discovery vs. consultants vs. process mining
- How to map a process for AI: people, systems and docs
- What is agent experience (AX)? How to make your company navigable for AI agents
- What is shadow AI? How to see which AI tools your people actually use
02 · Decide
Decide where AI belongs
Sort every step, choose the right model for each task, and price it before you build or buy.
03 · Prove
Prove it on your own work
Benchmark people, agents and both together on your real tasks, before and after go-live.
04 · Equip
Equip every person
Share the skills, SOPs and agents that work, and make your people the experts.
- What does AI-native actually look like?
- What are AI skills, and how do you share the ones that work?
- What is an AI agent harness? The software that turns a model into an agent
- Sidekick agents vs. background agents: which work should each do?
- How to write a PRD for an AI agent, starting from real work
- What is an on-screen AI assistant? AI that sees your screen and helps in any app
Glossary
The words, defined
Short, quotable definitions with an example for each.
- Forward-deployed engineer (FDE)A forward-deployed engineer (FDE) is an engineer who embeds with a customer’s team, learns how the work actually gets done, and builds and deploys software inside that team’s real workflows. Palantir pioneered the role.
- AI-nativeAn AI-native company designs its work around AI. Agents handle routine steps, people focus on judgment, review and improvement, and what works is captured as shared skills, SOPs and evals that keep getting better.
- The four bucketsThe four buckets are a way to sort every step of a workflow before automating it: delete it, run it as plain code, give it to an agent, or keep a person on it.
- Shadow modeShadow mode means running an AI agent alongside the real work without letting it send or change anything, then comparing its output with what people actually did.
- Personalized benchmarkA personalized benchmark scores AI on your own work: your real tasks, judged by your team’s own definition of correct, and compared with how your people do the same work.
- Cost per taskCost per task is the full cost of getting one task done correctly with AI: the model usage across every attempt, plus the time people spend reviewing the output and fixing its mistakes.
- Skill (AI skill)An AI skill is a packaged, reusable set of instructions, and sometimes files or scripts, that teaches an AI assistant or agent how to do a specific task the way your team does it.
- HarnessA harness is the code and setup around an AI model that turns it into a working agent: its instructions, tools, memory and context, permissions, retries, logging and checks.
- Digital orgA digital org is a living, machine-readable map of how your company works: people, roles, workflows, systems, documents and rules. Both people and AI agents use it to find their way around.
- Agent experience (AX)Agent experience (AX) is how easily AI agents can understand and work inside your company: finding the right information, knowing who owns what, and using tools safely. It is the agent counterpart of user experience.
- EvalAn eval (evaluation) is a repeatable test that checks an AI system’s output against what “correct” means for a task, using real cases and answers or grading rules the team agrees on.
- Shadow AIShadow AI is AI tools and agents that employees use for work without the company’s knowledge or approval.
Nolli
See where AI belongs in your company.
Nolli learns how your company actually works and shows where AI belongs, what it is worth, and how it performs on your own work. It makes every person forward deployed.