Grid
A coordination layer for multi-agent AI systems. Shared intent, conflict resolution, and human-AI alignment built as infrastructure — not as an afterthought.
Networks of agents are not systems. They are collections of isolated processes.
The standard architecture for multi-agent AI is isolation: each agent gets a context window, a set of tools, and a task. Agents communicate by writing outputs that other agents read as inputs. An orchestrator routes messages between them.
This works for simple pipelines. It fails for anything with shared state, parallel execution, or conflicting goals — which is most real work.
The failure mode is not agents making bad decisions. It is agents making locally correct decisions that are globally incoherent. Three agents researching the same company can produce a report that contradicts itself in three different ways, with no single agent having made an error.
Coordination is not an emergent property of capable agents. It requires explicit infrastructure. Grid is that infrastructure.
Shared Intent Representation
How do you give a group of agents a common model of what the system is trying to accomplish? We are building typed intent objects — structured, versioned, and queryable — that serve as the coordination surface.
Conflict Detection & Resolution
When two agents are about to take contradictory actions, the system needs to catch this before execution. We are building semantic conflict detection that understands the meaning of agent intentions, not just their API calls.
Human-AI Coordination
Agent-agent coordination is tractable. Human-agent coordination is harder because human intent is implicit, evolving, and often unstated. Grid needs to model what humans want even when they haven't said it directly.
Mandate Systems
The most reliable multi-agent systems are constraint-first. What is the system not allowed to do? Grid's mandate system makes these constraints explicit, versionable, and enforceable across all agents in the network.
Most coordination failures are not capability failures.
When agents produce incoherent outputs, the root cause is almost never that an agent lacked the ability to do the right thing. It is that the agent had an outdated or incomplete model of system intent.
Constraint-first design produces more predictable systems.
Defining what agents are not allowed to do is more tractable than specifying everything they should do. Mandates are the more stable abstraction.
The coordination layer is as fundamental as the model.
Every non-trivial multi-agent system will need explicit coordination infrastructure. This is not an optional feature — it is a prerequisite for system-level coherence.
Active research. Early prototype.
Grid is our active research project on multi-agent coordination. We are building the coordination layer by running into the problems that require it — in our own production systems, where multiple agents operate concurrently across Discord, Telegram, and the browser.
The core data model — typed intent objects, mandate system, conflict detection — is designed and partially implemented. We are pressure-testing it against real coordination failures before publishing a full spec.
Working on multi-agent systems?
We are most interested in coordination failures that happen in production — where the system worked fine in demos but broke when agents ran in parallel or when human intent changed mid-execution.