AI Management Area · the new business management
From now on, the challenge for companies is not only to create agents. It is to know how to manage them: assign role, objective, context, tools, authority and evaluation, just as has always been done with people. And before that, a tougher decision: not to put agents to execute old processes faster. Redesign the company for the speed of agents. This area brings together the thesis, the 8 AI‑manager competencies, the LOOP‑R, the 2027 AI Management Training and INEMA’s courses and projects that already cover most of the journey.
People management + process management + agent management. It’s management, not just technology.
01 · What’s changing
Each era of management responded to a change in what the company had to coordinate. The 2027 era coordinates humans, agents, models, processes and computational budget all at once.
Role, responsibility, tools, limits, evaluation.
Design, document, train, execute. The process as an asset.
Automation: design, automate, monitor.
Prompts, skills, context, tools, memory. Learn to create.
Learn to manage what you built, and redesign work around it.
Any team today can build an agent in an afternoon. What blocks the company is what comes after: who is responsible for it, what it can do on its own, how to measure if it’s good and how much each result costs.
Putting an agent to run a workflow designed for people in 2010 only delivers the error faster. The question that changes everything: if I had humans, agents and automations available today, would this process still exist in this form?
With agents measuring and critiquing their own execution, the process becomes a living system: it executes, observes, proposes improvement, tests, validates, promotes. That’s what INEMA calls LOOP‑R.
On the agent‑building side (prompt, skills, context, tools, memory, evals), see the AGI‑ready area. This page is about the next step: the company that already has agents and needs to manage them.
Open the AGI‑ready area →02 · The thesis
The words change, not the logic. What the company did with a new employee, it now needs to do with each agent: define the role, give context and tools, set limits, evaluate.
Before · people management
employee · role · responsibility · tools · limits · evaluation
Each item existed because without it a person couldn’t work, or worked without control.
Now · agent management
agent · role · objective · context · tools · autonomy · evaluation
The same items, plus two that people didn’t have: explicit context and graduated autonomy. And one detail: the agent can be measured on practically everything.
Separation of duties, authority limits, and human review apply to the agent just as they did to the assistant. The agent goes up to the signing door, never beyond.
First read, never the final word. Responsibility isn’t delegated, it’s organized.
Administration for the AI, people for the team. The limit is a management decision made before the agent is turned on.
Don’t put agents to execute old processes faster.
Redesign the company for the speed of agents.
03 · The 8 competencies of AI Management
In the order a manager uses them. The first four decide what the agent will do. The last four decide whether it keeps doing it.
What result do we want?
First the result, then the tool. Don’t start with the model or the platform.
Should this process still exist?
Eliminate, simplify, redesign, and only then automate. That’s the AI Work Architect.
What exactly do I hand over to the agent?
Goal, responsibility, context, tools, deadline, and evidence of completion. It’s the heart of Super‑Agents.
One agent or many? In series or in parallel?
Human in the loop, supervising agent, reviewing agent. The Execution Architect decides parallel vs sequential.
What does it do on its own, what does it confirm, and what does it never execute?
Permissions, identity, credentials, audit, logs, and traceability. Authority before autonomy.
How much does it cost to produce the result?
Model, tokens, time, APIs, Computer Use, infrastructure. Cost per run, per task, per outcome. ROI.
Is it good? Compared to what?
Quality, speed, cost, failures, rework, autonomy rate, human intervention, commercial outcome. The agent has performance evaluation.
What did we learn from this run?
The company stops asking only “did the task finish?”. Enter LOOP‑R.
04 · Process is not sacred
The traditional process was designed once and trained. The automated one, monitored. The agentic one, measured. LOOP‑R closes the loop: the execution itself proposes the next improvement.
Before adding AI, break down the process: what exists out of habit, what exists out of a real requirement, what only existed because there was no other way. Automation comes last.
Redesigning the flow means deciding, step by step, what is a human task, what is an agent, what is simple automation, what is Computer Use, what is an API.
A tested and validated improvement is promoted; one that made things worse is reverted. The process has versions, like software. That’s what makes it a living, self‑improving system.
05 · The 2027 Manager
The list of items that land on a manager’s desk has grown. And the cost question has changed: it’s no longer “how much does a call cost?”, it’s “how much does producing the result cost?”.
Where agent economics comes into practice. Per token, frontier models cost more, not less. Economics only appears when measured as cost per completed task: fewer turns, less rework, calibrated effort. A cheap agent that makes a mistake and needs two attempts costs double the listed price. That’s a manager’s calculation, not a programmer’s.
06 · AI Management Training 2027
The AI Management course becomes the backbone. The ten modules below are the training program; each builds on courses and projects that INEMA has already published (next section). The final project is the Management: managing an agent‑centric company end‑to‑end.
What changed, agents as digital workforce, the manager’s role, the hybrid company.
Responsibility, delegation, trust, performance, supervision. What changes and what stays the same.
Eliminate, simplify, redesign. Automate later.
Human, automation, agent, Computer Use, APIs and systems: who does what at each step.
Roles, skills, memory, context, tools, specialization.
Authorities, escalation, review, approval, progressive trust.
Tokens, models, infrastructure, cost per result, computational budget, ROI.
Permissions, identity, security, audit, accountability.
KPIs, assessment, performance, quality, cost, speed.
LOOP‑R: experiments, validation, promotion, rollback, continuous improvement.
The learner receives or selects a business process and must: understand the goal · analyze the existing process · eliminate steps · redesign the flow · define human tasks and agent tasks · choose tools and models · set permissions · establish costs · define metrics · execute · observe · evaluate · improve with LOOP‑R. Fourteen steps, an agentic company management simulator.
Get to know Management →Honest state of the training. The 2027 AI Management Training is a packaging of a thesis, not a brand‑new course released today. Based on the collection, between 75% and 80% of the required material already exists, spread across courses, frameworks and open projects listed below. The 2027 work is to organize, connect and package. In the meantime, the path is to start with the courses already online, in module order.
07 · What INEMA already has
The course that becomes the foundation of the training: delegation, responsibility, memory, tools, autonomy, trust and agent evaluation.
Open the course → Delegation · 8 lessonsFor decision‑makers: which responsibility to assign to an agent, with authority level, autonomy matrix and trust sheet.
Open the course → What changes · 6 lessonsWhat changes in your work when AI stops waiting for orders. The starting point of Module 1.
Open the course → Work architecture · 8 lessonsRedesign processes for humans to direct and agents to execute. The question “should this process still exist?” originates here.
Open the course → EvolutionExecute, observe, critique, propose, experiment, validate, promote, repeat. The entire Module 10. It also includes the project with guide.
Open the course → Economics and governanceFrom AI‑assisted work to corporate process automation: work, process, agent, automation, Computer Use, metrics and governance. Redesign before automation.
Open the course → OrchestrationOne agent or many, in series or in parallel, with supervisor or reviewer. Competency 4.
Open the guide → Governance by professionLimits, authorities, human review and segregation of duties across three professions. Healthcare · Accounting · Legal.
Open the legal notebook → Final projectThe agentic company management simulator: objectives, agents, processes, outcomes, costs, errors, evaluations, approvals, decisions and improvements.
Open the guide →The operating system for AI‑enabled work: how the company organizes agents, skills and processes in one place.
Open → SpecificationSeven models that become an agent specification on a single page. It is the delegation sheet for Competency 3.
Open the guide → KitSupport materials for the Copilot + Agents for Companies course, ready to use in your company.
Open the guide →08 · Start now
Take a process that currently runs with people, ask whether it should still exist that way, and begin the training with the module that answers your question. The courses are open. What changes when you join the ecosystem is having people, material and support instead of doing it alone.
The portal with all INEMA courses and projects: tracks organized by level, search by topic and the latest updates. It's where you start.
The community of those applying AI to real businesses: feed curated by Nei with what matters, groups by topic and support material from all trainings.
The platform for those who want to use AI to grow in practice: the more than 400 ecosystem projects, the trainings and the Brain with content organized for reference.
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