Technical Foundations

Work Squared is an AI-native environment “operating system” for businesses and organizations in which people and agents can collaborate.

This document introduces some of the underlying architectural foundations. We’ve found that the underlying layers of software–data storage, networking, execution runtime–constrain the possibilities of the product experience. In order to facilitate a truly new product experience, we need to think carefully about the technical foundations.

This is a rough sketch that points us in a differentiated direction, not a final answer.

Why?

Current chat with language models is limited in some key ways:

• Amnesiac Agents - Environment doesn’t accrete with artifacts as work is done. Most “work” remains in the context window, or very primitive artifacts.
• No direct manipulation - not an even playing field for agent and human.
• Chats are sync and don’t support have the naturalness and complexity of collaboration with a colleague.
• Environments aren’t extensible - the domain model and functionality can’t be extended at runtime.

Features

Data Innovations:

• Event-driven architecture means objects are merely read model projections of the underlying event store
• Agent tool-use is putting events on the event stream, meaning that agent work is non-blocking: it’s all background jobs
• Tool-use is always available the the human operator as well, through the same event structure and task-relevant UIs (direct manipulation
• Tools are all MCP-compatible tools
  • Needs experimentation

UI Principles:

• Divide the world into nouns and verbs
  • Nouns: objects or artifacts (read model projections of the event stream)
  • Verbs: tools or actions (events on the event stream)
• Center column for direct interaction with and manipulation
  • Center column is a “stack”, supporting interaction with many diverse data types
    • Stacks open up a lot of UI innovation surface area!
• Right column for chat, with contextual control (a la Cursor)

Autopoesis

• LLMs can generate their own tools using a tool-building tool: an MCP tool that can create additional MCP tools
• LLMs can customize their own read models and regenerate from the event stream
• LLMs can generate their own UI to dispatch events and views for the read models

Multiplayer

• Agent + single human is already a multi-player environment, but no reason you couldn’t add another human or AI to the chat / environment (multiplayer is hard in today’s LLM world)

Challenges

• Context window management - how to bring the right things into the context window
• Too general! Can do everything, but can it do anything?
• Mismatch with current post-training for agent-centric models - most agents are trained around the model of using a tool and waiting until it completes (sync) and this is inherently (async),
• What’s the model of an agent in this world? Is it one or many?