Asana expands its work graph to frame how agents work with enterprise teams
Perhaps the biggest challenge for traditional enterprise application vendors as they adapt to the impact of AI agents is the need to reach beyond their historic functional domains. This is because agents are increasingly able to orchestrate actions across multiple steps that cut across those pre-existing functional silos. It's a challenge because, while established vendors have built up huge knowledge which provides essential context for agents operating within their own domains — what I've called their Systems of Knowledge — they lack the context needed to accurately guide agents as they reach beyond those silos. Existing inter-agent protocols such as MCP don't resolve this challenge because they don't account for the possibility that contextual data might have a different meaning in another domain (a point I'll be returning to in more depth in a future article).
In principle, this gives an advantage to established vendors whose applications already straddle separate...
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