Shared memory is the missing layer in AI orchestration

The key to successful AI agents within an enterprise? Shared memory and context. 

This, according to Asana CPO Arnab Bose, provides detailed history and direct access from the get-go — with guardrail checkpoints and human oversight, of course. 

This way, “when you assign a task, you’re not having to go ahead and re-provide all of the context about how your business works,” Bose said at a recent VB event in San Francisco. 

AI as an active teammate, rather than a passive add-on

Asana launched Asana AI Teammates last year with the philosophy that, just like humans, AI agents should be plugged directly into a team or project to create a collaborative system. To further this mission, the project management company has fully integrated with Anthropic’s Claude.  

Users can choose from 12 pre-built agents — for common use cases like IT ticket deflection — or build their own, then assign them to project teams and immediately provide a historical record of what tasks have already been completed and what is still yet to be resolved. Agents also have access to third-party resources like Microsoft 365 or Google Drive. 

“When that agent gets created, it’s not acting on behalf of someone, it manifests itself as a teammate and it gets all of the same sharing permissions, it inherits that,” Bose explained. Everything anyone does — humans and AI included — is documented to allow for “ease of explainability” and a “very transparent and trustworthy system.”

But just like human workers, AI agents are kept in check: Critically, workflows incorporate checkpoints, where humans can give feedback and ask the agent to tweak certain elements of a project or adjust research plans. This is documented in what Bose called a “very human-readable way.” 

Also importantly, the UI provides instructions and knowledge about agent behavior, and approved admins can pause, edit and redirect models in the API when they take actions based on conflicting directions or start acting “in a weird way.”

“The person with edit rights can delete those things that are conflicting and make it go back to its correct behavior,” said Bose. “We’re leaning into that common human-understandable interaction pattern.”

Overcoming challenges of authorization, integration 

But because AI agents are so new, there are still many challenges around security, accessibility and compatibility. 

Asana users, for instance, must go through an OAuth flow and grant Claude access to Asana via their MCP and other public APIs. But getting all knowledge workers to know that that integration exists — and more importantly, which OAuth grants are OK and which are to be avoided — can be a tall order.

Some of the challenges around direct OAuth grants between applications could be centralized by identity providers, Bose noted, or a centralized listing of approved enterprise AI agents with their skill sets, “almost like an active directory or universal directory of agents.”

Right now, though, beyond what Asana is doing, there’s no standard protocol around shared knowledge and memory, said Bose. His team has been getting “a lot of interesting inbound asks” from partners who want their agents to operate on the Asana work graph and benefit from shared work.

“But because the protocol or standard doesn’t exist, today it has to be a very custom bespoke conversation,” said Bose. 

Ultimately, there are three questions the CPO called “extremely interesting” in AI orchestration right now: 

• How do you build, manage and secure an authoritative list of known approved AI agents? 

• How can you enable app-to-app integrations as an IT team without potentially configuring dangerous or harmful agents?

• Today’s agent-to-agent interactions are very single player. Clouds can independently be connected to Asana or Figma or Slack. How can we finally get to a unified, multi-player outcome?

The increased adoption of modern context protocol (MCP) — the open standard introduced by Anthropic that connects AI agents to external systems in a single action, rather than custom integrations for every single pairing — is promising, he noted, and its widespread adoption could open up new and exciting use cases.

However, “I think there probably isn’t a silver bullet standard out there right now,” said Bose.