agentic AI
- AI Strategy (429)
- Generative AI (113)
- IT Leadership (38)
- Leadership (81)
- Product Management (394)
- Product Management Leadership (373)
- Uncategorized (32)
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AI Agent Swarms: An Operating Model for Product Leaders
A practical framework for deciding when agent swarms earn their complexity, building the control plane, and proving value before rollout.
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How to Build an AI Agent Business Case That Survives Scrutiny
A practical framework for proving AI agent ROI through workflow baselines, realized benefits, full costs, production pilots, and scale gates.
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Engineering Reliable Long-Running AI Agents in Production
A practical architecture for agents that can pause, recover, verify work, control side effects, and escalate safely across extended production runs.
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Designing AI Agents That Can Safely Act for Your Users
A practical framework for defining agent authority, building safe action flows, measuring execution, and expanding autonomy without losing user trust.
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How Autonomous AI Changes the Work of Product Leaders
A practical operating model for delegating ambitious work to autonomous AI while preserving human judgment, decision rights, security, and accountability.
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How to Design Data Context for Reliable AI Agents
A practical framework for defining, retrieving, testing, and governing the data context AI agents need to make dependable decisions.
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How Product Leaders Should Govern Frontier AI Releases
A practical release gate for assessing frontier-model capability, misuse controls, enterprise privacy, and dependency risk before you ship.
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Claude Fable 5.1: A Practical Adoption Guide for AI Leaders
A practical framework for deciding where Claude Fable 5.1 belongs, how to test its judgment, model whole-run cost, and govern sensitive use.
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AI Agent Governance Infrastructure: A Practical Control Plane
A practical framework for governing AI agents through identity, scoped authority, transaction controls, audit evidence, and incident response.
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Enterprise AI Agent Audits: A Framework for Safe Execution
A practical framework for proving that enterprise AI agents complete real work, stay within authority, leave evidence, and recover safely.
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How to Build Governance Infrastructure for Autonomous AI Agents
A practical control-plane blueprint for identifying agents, limiting delegated authority, monitoring behavior, and preserving recourse.
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The Organizational Infrastructure Responsible AI Actually Needs
A practical operating model for assigning AI ownership, limiting agent authority, funding human oversight, and turning failures into safer systems.
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How to Measure Enterprise AI Agent Performance and ROI
A practical framework for routing agent work, setting hard budgets, measuring accepted outcomes, and proving enterprise AI ROI against a credible baseline.
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How Product Leaders Should Evaluate Emerging AI Tools
A practical framework for choosing, testing, and governing emerging AI tools without turning promising demos into unmanaged workflow clutter.
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How to Build AI Agent Infrastructure That Proves Value
A practical operating model for tracing agent runs, diagnosing failures, controlling routing costs, and connecting performance to business outcomes.
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Cheaper AI Models: Measure the Cost of Accepted Results
A practical framework for routing AI work, controlling agent loops, and measuring whether lower token prices reduce cost per accepted business result.
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How to Roadmap AI Agents, Robotics and Dedicated Hardware
A practical framework for sequencing agent autonomy, robotics, and dedicated hardware without letting compelling demos outrun product evidence.
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Enterprise AI Spend Is Concentrating. Manage It as a Portfolio
A practical operating model for separating AI activity from value, attributing costs correctly, and scaling proven workflows without rewarding waste.
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The Real Economics of Self-Hosting Frontier AI Models
A practical break-even framework for deciding when token volume, model quality, platform staffing, and utilization justify owning AI infrastructure.
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Frontier AI Updates: A Product Leader’s Adoption Playbook
A practical framework for turning model, inference, open-weight, agent, and multimodal releases into measured product and architecture decisions.
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Browse topics
- AI Strategy (429)
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