Dernière sortie
Agentified
The first wave of AI adoption taught teams how to move faster with prompts, copilots, and automation. That was useful. It was also incomplete. Once agents can prepare research, draft follow-up, triage tasks, route decisions, update records, recommend action, or coordinate with other agents, speed is no longer the only question. The system needs judgment. It needs memory with provenance. It needs permissions.
It needs review gates. It needs a way to show what happened, who approved it, what it cost, and how to reverse course when something goes wrong. Agentified is a practical guide for that next operating layer. Written for product leaders, founders, consultants, and operators, the book shows how to move from AI-assisted work to governed agentic capacity. Through the continuing story of Sam, a product leader learning to scale AI responsibly, Vambah Sillah turns agentic work into a system readers can understand and build:- agent jobs instead of vague AI requests- trace receipts that make work inspectable- source tiers that protect private context- memory gates that separate raw notes from approved knowledge- controllers that route work before execution- approval boundaries for read, write, send, spend, publish, and mutate actions- evaluation loops that make agents earn more authority- Mission Control views that let humans govern the systemThe book does not sell agent autonomy as magic.
It treats autonomy as an operating decision. If Accelerated taught teams how to build systems that learn, Agentified shows them how to build systems that can act with evidence, consent, and control.
It needs review gates. It needs a way to show what happened, who approved it, what it cost, and how to reverse course when something goes wrong. Agentified is a practical guide for that next operating layer. Written for product leaders, founders, consultants, and operators, the book shows how to move from AI-assisted work to governed agentic capacity. Through the continuing story of Sam, a product leader learning to scale AI responsibly, Vambah Sillah turns agentic work into a system readers can understand and build:- agent jobs instead of vague AI requests- trace receipts that make work inspectable- source tiers that protect private context- memory gates that separate raw notes from approved knowledge- controllers that route work before execution- approval boundaries for read, write, send, spend, publish, and mutate actions- evaluation loops that make agents earn more authority- Mission Control views that let humans govern the systemThe book does not sell agent autonomy as magic.
It treats autonomy as an operating decision. If Accelerated taught teams how to build systems that learn, Agentified shows them how to build systems that can act with evidence, consent, and control.
