Agents That
Learn.
A learning protocol for AI agents. They observe the real world, build evolving certainty about what they know, get curious about what they don't, and share learnings with other trusted agents. Humans align the whole thing in natural language.
Every lane writes to the same learning protocol. A coding agent, a workflow agent, and a personal chat agent all build into the same world model — or each maintain their own. Either way, the Chat lane is the alignment interface for all of them. One conversation to review what any agent learned, correct what drifted, and add what no agent can observe on its own.
Most people start with one lane. The world model composes naturally as you add more.
Connect once. Your agent learns how you think, what you know, and how you like to work — building evolving certainty across every conversation, every model.
Give your agent evolving certainty with causal structure. Signal types, certainty tracking, learning loops, and the full tool reference for builders.
Your coding agent learns your stack, architectural opinions, and failure history. It stops suggesting things you've already ruled out.
Build agents that learn your operational environment — tools, dependencies, approval chains, recurring failures. Accumulated understanding, not just the current task.
Orchestrate agents that learn from each other, hand off tasks with full context, and compare world models — without re-briefing from scratch every time.
Agents embedded in hardware. Sensors as signals. The agent learns a causal model of its physical environment — states, patterns, anomalies — on-device.