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AI can now draft requirements, write code, generate tests, and act on real systems. It cannot be held accountable. Accountable Autonomy is a practical body of knowledge for project managers who must plan, govern, deliver, verify, and operate software when humans and AI work side by side.
Built around the Controlled Autonomy Delivery Framework, the book gives you risk tiers, AI autonomy levels, verification-independence levels, and evidence classes that scale from a three-person startup to a regulated enterprise. You'll learn why faster code generation rarely means proportionally faster delivery, how to stop code and tests from sharing the same wrong assumption, and how to secure agentic workflows against prompt injection and excessive permissions.
Inside: a 17-procedure operational playbook from idea to retirement, 14 decision frameworks, 41 ready-to-use templates, and five worked case studies.
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