A context and coordination layer for AI agents
01 CONTEXT
As context grows, critical decisions and constraints get buried where models are least likely to retrieve them.
Old assumptions, abandoned directions, and duplicated detail accumulate until the context becomes less trustworthy.
Known project context has to be rebuilt for every new conversation, adding repeated tokens, time, and cost.
Create durable project context once, then carry it across chats, agents, and workflows without rebuilding it.
Preserve stable facts and decisions apart from the temporary conversation exhaust that builds up as projects evolve.
Continuously assemble a focused context window and stream only what the active agent needs for the work at hand.
02 COORDINATION
More agents create more throughput only when their work shares intent, ownership, and a common understanding of the project.
Parallel agents repeat research, collide on implementation, and make locally sensible decisions that diverge globally.
Plans, evidence, and unresolved questions get compressed or dropped as work moves between agents and chats.
Ownership, dependencies, and status fall out of sync while the system changes underneath the people and agents building it.
Give every agent a live view of the same goals, decisions, constraints, dependencies, and current state.
Direct tasks and relevant context to the right agent at the right moment, with explicit ownership and boundaries.
Stream findings, blockers, and outcomes back into the project so parallel work stays legible and converges.
03 THE FIRST SYSTEM ON MIDDLEWARE
A next-generation AI harness built for complex, evolving codebases.
TRY BABEL