Writing / Talking Agents
Architecture, research, and practical lessons from working with agents and building AgentPlanner.
Commentary / Agents / Information Systems / Organization Design
Hierarchy was always an information-routing system. If agents are to participate in coordination, the real challenge is architectural: how should firms represent state, memory, and authority?
AI Agents / AgentPlanner / Research / Architecture
The Belief-Desire-Intention model from 1995 is the most cited paper in multi-agent systems. Its core insight — that an agent's intentions must remain consistent with its beliefs — explains exactly what's missing from modern AI planning tools. Here's how we're building it into AgentPlanner.
AI Agents / AgentPlanner / Research / Product Thinking
Karpathy's autoresearch experiment ran 700 ML experiments in 2 days and found an 11% training speedup. But the deeper insight isn't about model training — it's a generalizable pattern for any goal-driven agent system. Here's what it means for how AI agents should plan.
AI Transformation / AgentPlanner / Strategy / Enterprise AI
There's a difference between using AI tools and agent-driven transformation. One is augmentation. The other is restructuring how work gets done — with agents as active participants. A phase-by-phase playbook for getting there, with AgentPlanner as the coordination layer.
AgentPlanner / AI Agents / Product Thinking
The fundamental assumption of every project management tool is that a human is creating, assigning, and completing tasks. That assumption is breaking. When agents are the primary operators of a plan, the design principles change completely — and most existing tools aren't built for it.