Unlike standard AI chats which are isolated, Cowork's "Projects" feature allows you to chain multiple tasks together. All tasks within a project share the same context and memory, allowing the AI to build on previous work and understand the larger goal.

The foundation of an AI-native company is a "brain"—a central context layer where all company information (SOPs, meeting notes, emails) is captured, curated, and structured. This makes the company's knowledge "readable" to AI agents, giving them the perfect vision to execute tasks.

Instead of starting new chats for every task, use single, long-running 'monothreads' for each major workstream. Advanced context compaction in tools like Codex allows these threads to persist memory over time, turning the AI from a simple Q&A bot into an ongoing project collaborator with deep context.

The primary benefit of an agent orchestrator isn't raw productivity or new agent skills. It's the ability to consolidate a task's entire lifecycle—spec, execution plan, rework logs—into a single context. This makes debugging failures and improving future performance much easier.

Enterprise AI vendors are moving beyond simple search or chat applications. The real value and defensibility lie in the underlying 'context engine' that connects and understands siloed company data, user activity, and permissions. This engine provides the accuracy and relevance that generic LLMs fundamentally lack.

Most AI tools are single-player experiences. Linear is designing its agent sessions to be shared, collaborative spaces. Multiple people, like a PM and a designer, can jump into the same chat with an agent, see its work, and give it feedback together, collapsing the collaboration loop.

The next frontier for AI isn't just personal assistants but "teammates" that understand an entire team's dynamics, projects, and shared data. This shifts the focus from single-user interactions to collaborative intelligence by building a knowledge graph connecting people and their work.

For any product involving ongoing user interaction (support, sales), the key differentiator is not raw model capability but a persistent knowledge base. This allows the AI to remember a user's history across sessions, transforming it from a simple question-answer tool into a stateful, effective partner that understands context.

When a user's personal agent (in an environment like Codex) interacts with an app, it can automatically share vast context about the user's goals and history. This eliminates tedious onboarding and enables a deeply customized experience from the first interaction, changing how software is designed.