Why context is the biggest unlock for your AI strategy
Atlassian argues shared organizational context is the critical missing layer for AI agents to perform effectively, introducing a context graph to map relationships across tools, decisions, and workflows.
Atlassian positions shared context as the foundation for AI agents to operate meaningfully within organizations, noting that models alone cannot replicate the institutional knowledge of tenured employees. The company highlights that without context, AI lacks awareness of internal nuances such as overlapping project names or recent decision reversals, limiting its ability to execute tasks accurately.
The article introduces the concept of a context graph, which structures organizational data with common definitions and pre-inferred relationships to make context legible to AI. Atlassian’s Teamwork Graph, built from over 350,000 customer workflows, aims to provide a comprehensive blueprint of teamwork that AI can query efficiently across any model or agent.
Context engineering is presented as the process of curating, connecting, and maintaining organizational context so AI can retrieve relevant data, track history, and use safe, permission-aware information. Atlassian outlines three pillars for context quality: completeness, connectedness, and relevance, emphasizing that durable context compounds over time.
The company argues that while AI models are increasingly capable of taking actions, their potential is constrained without contextual understanding. Atlassian’s framework includes six types of context—people, work, communications, and others—drawn from across the toolchain to form an integrated picture of how organizations function.