AI agent
- AI Strategy (429)
- Generative AI (113)
- IT Leadership (38)
- Leadership (81)
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- Product Management Leadership (373)
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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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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 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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Why AI Assistant Memory Matters More as Models Open Up
A product strategy for owning assistant memory, preserving user trust, and using open-weight models without locking your product to one vendor.
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Consolidating MCP Tools Without Sacrificing Capability
A practical framework for reshaping an MCP tool catalog around user intent, reducing context waste, and validating changes with task-level evals.
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Designing AI Agents for Instant, Useful Lead Response
A practical operating model for using AI agents to respond immediately, advance the buyer, hand off with context, and measure commercial quality.
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Knowledge Management That Keeps Service and Sales Agents Ready
Build a governed knowledge system that keeps service and sales agents current, role-aware, testable, and synchronized with every product release.
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AI Agent Trust Starts With Organizational Readiness
A practical operating model for deciding what AI agents may do, where humans must approve, and how to earn trust before scaling autonomy across the business.
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AI Service Agent Access: A Helpdesk Interoperability Playbook
A practical framework for connecting AI service agents to existing helpdesks, granting safe access, preserving handoffs, and testing failures.
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How to Run AI Support Quality as a Continuous Operation
A practical operating model for measuring AI support quality, gating releases, shaping conversations, and turning live failures into controlled improvements.
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Before You Renew Software, Audit the Agent Workflow
A practical workflow audit for deciding which software capabilities to renew, unbundle, build, or make portable before the next enterprise contract.
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AI Agent Economics: How to Plan Capacity Before You Scale
A practical framework for measuring accepted agent output, calculating its full unit cost, routing models, and deciding when added capacity pays.
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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)
- Generative AI (113)
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- Leadership (81)
- Product Management (394)
- Product Management Leadership (373)
- Uncategorized (32)
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