Agents you can coach: how Asana builds human-agent teams with Claude

來源原標題

Agents you can coach: how Asana builds human-agent teams with Claude

機構

Anthropic

原始標籤

Agents

原始連結

https://claude.com/blog/agents-you-can-coach-how-asana-builds-human-agent-teams-with-claude

摘要原文

Agents you can coach: how Asana builds human-agent teams with Claude Arnab Bose, Chief Product Officer at Asana, on how Asana runs AI agents as teammates with scoped roles, shared memory, and work that everyone can see. ‍ Category Agents Product Claude Platform Date September 29, 2026 Reading time 5 min Share Copy link https://claude.com/blog/agents-you-can-coach-how-asana-builds-human-agent-teams-with-claude Author(s) Aleksandra Todorova Kristen Swanson This is the third post in our series on building human-agent teams. The first shared what we’ve learned working with multiplayer AI at Anthropic. The second shared how Slack turns workplace conversation into the context agents need. This one looks at what changes when agents operate on the same platform where teams work. Years before they introduced AI agents, teams at Asana were iterating on ways to encode structure and accountability into how teams work together. They ultimately built the W ork Graph ® model, which maps out every task, project, goal, and conversation on a web of relationships, with defined owners, contributors, and dependencies. When they started building AI agents, they decided that rather than adding new context structures for AI, agents would operate within this same model. They would have defined roles, be assigned tasks, read and write messages, and show up in activity feeds alongside human collaborators—with additional safeguards around what agents can access, and share. We talked with Arnab Bose, Asana’s Chief Product Officer, about how Asana’s own teams use Claude and work alongside these agents, where Claude models power complex tasks: how each agent gets its role and access, who trains it, how work stays visible to everyone, and the types of jobs agents have on human-agent teams at Asana. Think through and structure your work before agents act on it For Asana employees, Claude is the default AI tool, connected to the platforms employees use to work, including Google Drive, Slack, and of course, Asana. "A person makes sense of their day by taking unstructured data, an idea they have, a conversation in Slack, a meeting recording in Zoom, a Databricks report, information from Google Docs,” Arnab says. “They talk it through with Claude, and then they can pump all of that into the structure that Asana provides with projects and tasks." Once the structure is in place, agents can act on it, with the three capabilities we described in the first post of this series, Building effective human agent teams : persistent memory, their own credentials, and shared context. How to put this into practice: Start with your own ideas. Bring the unstructured pieces of your day, an idea, a Slack conversation, or notes spread across docs, to Claude. Debate and discuss with Claude. Use it as a thinking partner: talk the idea through until the next steps are clear. Log the actionable items to the Work Graph. Move what's actionable into projects and tasks, where agents and colleagues can pick it up. Give every agent a role and the tools and access it needs Asana employees can work with AI agents in any project like they would with a human colleague. Humans design the teams they work on; each employee gets a list of recommended agents they can use to augment their teams’ goals. Agents are built around roles or types of work, for example, content writer, insights analyst, project manager, work intake specialist, campaign analyst, or campaign coordinator. Each agent comes with pre-built skills based on Asana’s research into how its customers do that work, and with the integrations they would need, such as Hubspot or a document drive. Each agent also has a profile page that lists its name and purpose, the people who can use it, the administrators, instructions, skills, integrations, and permissions. Asana gives you the tools to make access intentional. “Asana is a contained work surface,” Arnab says. “You could choose to grant access to a specific set of projects versus everything, or a specific set of documents, or a combination of documents and apps.” Like human users, agents are subject to explicit access controls, but have an additional safeguard: Asana says an agent’s effective access is bounded by the permissions of the person who triggers it. This allows agents to have broad access to public content, while minimizing the risk of anyone accessing information the agent has learned in a private context. How to put this into practice: Define the role before you create the agent. Think about the types of jobs that agents can do on your team, then write down the agent’s purpose, instructions, and which specific jobs it owns, the way you would for a new hire’s first quarter. Scope access. Decide which projects and documents the agent can read, which applications it can access, and which actions it can take. Name users and admins separately. Many people can work with an agent, but only a small, named group should govern what it can access and how it behaves. Separate working with an agent from training it A key feature of Asana’s agents, which it calls AI teammates, is shared memory, which enables an agent to retain information from previous instructions, allowing multiple users to reuse that memory to complete tasks or jobs faster. “AI teammates can be coached and trained as if they were a person on your team,” Arnab says. There is one role-based restriction in how shared memory works. While anyone can give an agent feedback on a task, only admins and editors can commit feedback to permanent memory, as well as undo, or delete from that memory. For everyone else, the feedback they provide applies only to the current task. The split is intentional, Arnab says. Asana's communications team hold the pen on the company's voice and tone, so they would be the editors and admins of an agent that writes. Arnab can draft with it, but he can't modify its behavior. And most people never need to touch the machinery at all: "Not everybody on the team needs to understand these concepts, like skills and behavior and memory. There are one or two people on the team who become experts, they set it up correctly, and all the other human beings on the team get the same benefits going forward." How to put this into practice: Decide who trains each agent, and who works with it. Identify subject matter experts inside your business who can help build the agents that the team uses. Everyone else can provide feedback on tasks, but cannot rewrite the agent. Match editors to expertise. The team that owns the standard the agent applies, like brand voice or planning conventions, owns (or administers) the agent. Build the memory as you work. Feedback can improve the agent with time. Ask the agent to remember decisions worth keeping, or delete ones that are no longer relevant or true. Keep the agent’s work where everyone can see it Similar to how agents in Slack post transparently in channels , when a task is assigned to an AI teammate everyone can see that an agent is doing it, and what it does. The agent posts activity, including its research plan and the steps it took, so everyone with access to that task can read what it did, comment, and steer it toward the result they want. When Asana’s communications team asked Arnab to review a briefing document for a speaking engagement, he @-mentioned the agent on the task and asked it to also factor in his talk track from an earlier talk. The message was brief because he had used that agent many times and the material he referenced was already in the Work Graph. A colleague on the communications team could see his request and the agent’s response, and go back and forth with the agent at the same time. "You could probably get great quality responses from a one-on-one AI agent if you are highly AI fluent, get that document out, and post it back into Slack or into Asana," Arnab says.