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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How to Budget for Enterprise AI Agents That Keep Expanding
A practical model for forecasting agent consumption, funding adjacent workflows, and expanding enterprise AI budgets with cost and risk controls.
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Google AI Engineering Interview Prep: A Practical Plan
A practical plan to prioritize coding, ML depth, system design, RAG, agents, and resume stories without memorizing a speculative question bank.
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How to Make AI Agents Finish and Hand Off Work Cleanly
A practical completion contract, handoff format, stop rule, and evaluation system for agents that deliver results without creating more work.
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How to Build Proactive AI Assistants Without Losing Control
A practical framework for turning new AI models into proactive assistants with clear scope, permissions, evaluations, and human control.
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How to Design AI Agent Permissions and Spending Controls
A practical framework for limiting agent authority, layering spend controls, designing meaningful approvals, and making every action traceable and reversible.
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Anthropic’s Agentic AI Operating Model for Product Leaders
A practical framework for building agentic products with durable context, bounded autonomy, task-level evals, and pods that own outcomes.
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Brand Visibility in Agentic Commerce: What to Build Now
A practical framework for making products legible to AI agents, earning recommendation slots, measuring sales impact, and governing automation.
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How to Evaluate an Agentic Customer Support Platform
A practical framework for choosing agentic support workflows, testing platform controls, designing handoffs, and measuring verified resolutions.
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A Funding Playbook for AI Cyber Resilience Startups
A practical playbook for proving demand, pricing shared value, and financing AI cyber resilience before a crisis creates urgency.
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AI Agents for Product Experimentation: A Practical System
A practical operating model for turning product signals into instrumented experiments with AI agents while preserving accountable product judgment.
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How Product Leaders Should Respond to Frontier AI Launches
A practical framework for deciding which frontier AI launches to ignore, watch, test, or adopt based on product evidence rather than hype.
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How to Build Reliable AI Agent Workflows and Handoffs
A practical framework for preserving evidence, decisions and state across AI agents without inheriting errors, permissions or false commitments.
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How to Scale AI Agents Without Scaling Their Failure Rate
A practical operating model for expanding agent autonomy with pre-action checkpoints, closed-loop verification, trajectory evals, and risk-based routing.
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Reliable AI Agents: Architecture, Evals, and Release Gates
A practical architecture and evaluation playbook for turning fragile AI agent demos into bounded, observable systems you can release with confidence.
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Where AI-Driven AI Research Breaks Down—and What to Do
A practical operating model for using AI in AI R&D without mistaking persuasive output, faster iteration, or self-evaluation for reliable discovery.
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China AI Governance: An Operating Plan for Product Leaders
A practical plan for adapting AI products to China’s focus on deployment risk, agent safety, human override, incident response, and auditability.
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AI Governance for Agents, Privacy, and Open-Model Choice
A practical operating model for shipping AI agents with bounded data access, auditable decisions, and enough leverage to change models.
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Frontier AI Scaling: A Product Leader’s Agent Economy Playbook
A practical framework for turning abundant frontier-model capacity into trusted, measurable agent work without mistaking supply for demand.
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From Chat to Action: How to Build AI Agents You Can Trust
A practical framework for choosing agent workflows, defining permissions, verifying outcomes, and expanding autonomy without losing control.
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Trustworthy Agentic Product Engineering: A Release Playbook
Use this release playbook to bound agent authority, stop inherited errors, evaluate failures, explain changes, and make AI actions reversible.
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Browse topics
- AI Strategy (429)
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