# Why AI gains are unevenly distributed in your engineering team | Asana’s Arnab Bose | Dev Interrupted Powered by LinearB

> Asana Chief Product Officer Arnab Bose explains why enterprise AI gains remain unevenly distributed across engineering teams. Discover how moving from isolated chatbots to agentic work management turns AI into a transparent digital teammate with shared memory, role-based controls, and clear ROI metrics.

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Why AI gains are unevenly distributed in your engineering team | Asana’s Arnab Bose

# Why AI gains are unevenly distributed in your engineering team | Asana’s Arnab Bose

By Arnab Bose

|

August 4, 2026

![Blog_Comprehensive_DORA_Guide_2400x1256_72_a71bae404c](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Blog_Comprehensive_DORA_Guide_2400x1256_72_a71bae404c?_a=BAVMn6ID0)

Why are 75% of knowledge workers using AI, yet only 5% of companies seeing meaningful productivity gains? This week on Dev Interrupted, Asana Chief Product Officer Arnab Bose explains why scaling enterprise AI means shifting from isolated chatbots to fully integrated agentic work management. He breaks down how Asana is turning AI from a tool into a transparent digital teammate with shared memory, full audit trails, and role-based access controls. The conversation closes on Asana's acquisition of Stack AI, the upcoming Command product for R&D teams, and which metrics prove AI ROI.

### Show Notes

* Asana:Explore the work management platform and learn more about Agentic Work Management at[asana.com](https://asana.com/).
* Stack AI:Read about Asana's acquisition of the no-code AI workflow automation platform on the[Asana Blog](https://asana.com/press/releases/pr/asana-acquires-stackai-adding-cross-system-execution-for-human-agent-teams/e7c73b97-ae8c-4e51-b927-189ccb184146).
* Connect with Arnab:[LinkedIn](https://www.linkedin.com/in/abosesf/)

### Transcript 

_(Disclaimer: may contain unintentionally confusing, inaccurate and/or amusing transcription errors)_

\[00:00:00\] **Andrew Zigler:** Welcome back to Dev Interrupted, brought to you by LinearB. My guest today is Arnab Bose, the chief product officer at Asana, and this one's a treat for me because Dev Interrupted's production runs on Asana. Three-quarters of knowledge workers use AI on the job now, but few companies can claim distributed productivity gains from it, and Arnab has a theory about why individuals get faster while the company simply doesn't.

\[00:00:26\] **Andrew Zigler:** And we get into all of that and more, and how Asana measures its own AI success. Now, here's my conversation with Arnab. I'm really excited about today's episode because we're diving deep into the next evolution of workplace productivity with a tool that we all know and love, I especially know and love, uh, and that's Asana. And joining us today is Arnab Bose, the Chief Product Officer at Asana. And it's amazing to have the opportunity to chat with you because here on Dev Interrupted, uh, we run on Asana. In fact, you know, I've \[00:01:00\] ended up building our whole podcast production pipeline, uh, our content calendars, and the cross-team dependencies for how LinearB interacts with our content all using Asana.

\[00:01:11\] **Andrew Zigler:** It's kind of like the central nervous system for how we

\[00:01:14\] **Andrew Zigler:** keep this show running and communicating. So, you know, I'm really deeply invested in, I guess, the, uh, Asana cinematic universe, you could say.

\[00:01:22\] **Arnab Bose:** Amazing

\[00:01:23\] **Andrew Zigler:** at, uh, some of the recent things that have been coming up for Asana, uh, like you...

\[00:01:27\] **Andrew Zigler:** There's been a recent, uh, acquisition, in fact, the first one in Asana's 18-year history, which we're gonna talk about, um, as well as announcements around agentic work management. Uh, it's really clear how your team is, is building towards a future that's going beyond just t- typical and traditional task tracking and management, and kind of helping us navigate this agentic maze of ephemeral and real tasks, and owners and non-owners and, and all of the real messy reality of how we do knowledge work now.

\[00:01:58\] **Andrew Zigler:** And Asana is really \[00:02:00\] the... gonna become one of the forefront places where that develops. So Arnab, I'm really excited to have you here. Welcome to Dev Interrupted.

\[00:02:07\] **Arnab Bose:** Thank you for having me, Andrew, and, uh, amazing to hear how you're using Asana today and how Dev Interrupted, the podcast, is, like, built on Asana. Amazing

\[00:02:15\] **Andrew Zigler:** Yeah. We're gonna dive into it a little bit more too, and so as we kinda crawl along on some of the latest developments with Asana, and, um, maybe you can kinda help clue me in as well, just as, like, a developer, someone developing-minded interest, uh, with the platform of, like, what are the ways that I can be thinking of plugging in in the future? But, you know,

\[00:02:34\] **Andrew Zigler:** I wanna start at the top, which is a data point from BCG about a really frustrating reality for knowledge workers. And, and that's that 75% of them are using AI in some capacity on the job at this point. We're talking about knowledge workers who, you know, are typically in, like, a, a more, uh, like, a white collar industry, where they're working with information likely on a computer.

\[00:02:54\] **Andrew Zigler:** And, you know, uh, most folks at this stage are using AI to do that. But \[00:03:00\] only about 5% of companies on an aggregate overall are seeing meaningful productivity gains. And that stat was really interesting to me because, um, we talk about the same kinds of problems that are happening right now in just even inside of those orgs, the engineering orgs themselves, of how you get all of these really, like, spread out, uh, success in a- agentic engineering.

\[00:03:24\] **Andrew Zigler:** You get the 10X engineer. You get the, the mythical 1000X engineer, and everyone's trying to figure out how they're working, and we're all trying to identify these players. Um, but it- the reality is, is that, yeah, sure, you got these, like, really crazy, uh, like, empowered in, uh, devs. But on the aggregate, are these engineering orgs delivering more and higher quality engineering products?

\[00:03:46\] **Andrew Zigler:** Are they getting more across the finish line

\[00:03:48\] **Arnab Bose:** Yeah

\[00:03:48\] **Andrew Zigler:** need a refactor or cause an incident? Those questions are still really fuzzy, and that's what we say as, like, a productivity gain.

\[00:03:55\] **Arnab Bose:** Mm-hmm.

\[00:03:56\] **Andrew Zigler:** do you think that, like, there is such a big gap \[00:04:00\] between picking up and using the tools to actually getting, like, on an org level, meaningful gains?

\[00:04:07\] **Arnab Bose:** So, uh, a gr-great question. Uh, I think the, uh, the research from BCG is, is quite, um, uh, precise in terms of highlighting that the individual within companies is now able to produce, uh, more work and probably higher quality work. But overall, when you take a look at a workflow that a group of knowledge workers have to, uh, build out end to end, those workflows are not actually being accelerated by AI as yet, and that's why companies are not seeing true productivity gains on the aggregate.

\[00:04:42\] **Arnab Bose:** And the thesis that I have, and this is the reason why I was drawn to Asana, is, uh, AI has been really easy to use on a one-on-one basis, where, uh, if you're using, um, you know, a chat-based LLM product and, um, you \[00:05:00\] have a thought or an idea or a document you want to write or a document you want to refine or, um, you know, an image or a graphic you want to create, uh, that interaction pattern is something that, um, you know, grew like wildfire three and a half years ago and has constantly gotten better as the quality of the models and the reasoning capabilities of the models have improved.

\[00:05:22\] **Arnab Bose:** But that is not the way in which work actually gets done end to end in a business, whether it's a small business or a large enterprise. In any of these cases, it is typically, a workflow which requires multiple human beings to get on the same page, sign off on the work, uh, and then agree when something is, um, is reaching a quality bar that indicates that it's done.

\[00:05:46\] **Arnab Bose:** And so while, uh, AI tools are capable of taking actions inside of shared workflows across teams and projects, this is not the way in which most companies have been able to deploy AI. \[00:06:00\] What our thesis at Asana is, is, you know, as you were calling out yourself and you, you probably see on a day-to-day basis, Asana is a very interesting canvas where you can define who does what by when and how, and leveraging the, the work graph, which is a data structure and a data model we've been investing in for over 18 years now.

\[00:06:22\] **Arnab Bose:** Uh, you're not just creating tasks, you're creating tasks that are connected to teams of people, that are connected to projects, that are connected to portfolios and goals. And so, uh, a larger group of people can align on strategic outcomes, exactly how, uh, the production of the podcast should be done, set up approval workflows and things like that.

\[00:06:45\] **Arnab Bose:** And then if you can add in enterprise-grade AI agents that have the context of how these, uh, workflows were completed by human beings historically, that have the checkpoints where multiple human beings can \[00:07:00\] provide these AI agents with feedback, um, and the-- this clarity around how, uh, an artifact, uh, was created, then you can actually achieve end-to-end, outcomes at the workflow level that truly move your company forward.

\[00:07:16\] **Arnab Bose:** So I, I'm not yet answering your question about the engineering, uh, workflows, but if you just simply take the knowledge worker workflows of, uh, aligning on a brief of a document or signing off on, uh, on the production values of a, of a video, um, if the AI agent is not able to work with the context of your company inside the shared workflows across teams and projects, that's what results in the, uh, in that stat from BCG that 95% of companies are not seeing real productivity gains

\[00:07:47\] **Andrew Zigler:** Yeah, absolutely. I think there's... You've, you've rightly called out that there's a big difference in being individually enabled with AI and o- being on an organizational level enabled \[00:08:00\] with AI, and those skills don't directly transfer. Instead, it's more like an accumulation of those individual AI abilities and those workflows and the things that they need, and then figuring out how to harden them into states that are durable for the long term, and that's where you get that long-term productivity gains.

\[00:08:20\] **Andrew Zigler:** Especially the larger the org, the more hardened and the more secure and the more, uh, specified, uh, that kind of deployment needs to be. And frankly, there's a lot of really large and sensitive information orgs that build and are powered by things on Asana. So when you think about building in that problem space and, you know, interacting with that, like, messy context, you have to think about how to make it safe for everyone at all the different levels of the work, um, that they do.

\[00:08:46\] **Andrew Zigler:** Which is a, which is a, a taller order, uh, than even most orgs have of trying to figure out how to just be productive with AI for ourselves, period.

\[00:08:54\] **Arnab Bose:** Correct

\[00:08:55\] **Andrew Zigler:** and so, you know, you've also called out that, like, Asana is, becomes this \[00:09:00\] place where of the traditional history of how work was done and where the identities of the humans and the identity, and the identities of the agents come together to queue up and pick work and give feedback to each other.

\[00:09:14\] **Andrew Zigler:** It becomes this, like, context, I guess, plane, right, in which these two things are communicating with each other. And really what that starts to get to is it'll help us figure out, like, what's the new way of working.

\[00:09:26\] **Arnab Bose:** Mm-hmm.

\[00:09:27\] **Andrew Zigler:** lot of us are trying to figure out how, what are the new loops, what are the new ways that we kind of, like, operate together as a team.

\[00:09:33\] **Andrew Zigler:** And so, you know, you've recently unveiled agentic work management, which is kind of like an, almost like an operating system level answer to this question. It's, like, it's acknowledging that computing up until now has been writing code to create things, to push things through pipes in and out through different destinations for different results, and now we can do that with intelligence and with natural language inputs and outputs.

\[00:09:57\] **Andrew Zigler:** And so now knowledge work itself \[00:10:00\] becomes something we can build with pipes and,

\[00:10:02\] **Arnab Bose:** That's right

\[00:10:03\] **Andrew Zigler:** so it becomes like an operating system. Like, how do you think about that fr- in your product role, um, at Asana in, in, in the stuff that you see?

\[00:10:11\] **Arnab Bose:** we've built agentic work management on a premise that came directly from our history. Uh, and you know, you've been an Asana user for a long time. You can see that for, you know, over 18 years now, Asana has been building this work graph, which is a structured representation of who is doing what by when, towards what goal, and in coordination with whom.

\[00:10:31\] **Arnab Bose:** And there are some principles, uh, behind the work graph, uh, for human beings around shared visibility, clear ownership, permission of our access, structured communication that can also be applied to AI agents. And the way in which we are bringing AI agents into this agentic work management product is we think of AI agents as true teammates, as actors within the system that stand alone.

\[00:10:58\] **Arnab Bose:** And those actors \[00:11:00\] have the same kinds of shared visibility, clear ownership, permission of our access and structured communication as human beings do. When you combine these things, what, what ends up happening is you get some significant value that is directly plugged in to that, uh, that shared workflow.

\[00:11:16\] **Arnab Bose:** So the first thing is, and I spoke about it a little bit already, because you're adding this, uh, AI agent as an actor into a part of the work graph, and you're giving it access to it, it can see the activities that have happened in the past, uh, that human beings have accomplished. The second thing is, um, because there are-- there's a permissioning, uh, system we can build based on which human being is interacting with that AI agent and in what part of the work graph, what we can do is instead of an AI agent being like Arnab's AI agent or Andrew's AI agent and only learning from feedback from me, we've built in a con-- a concept called shared memory.

\[00:11:55\] **Arnab Bose:** And what shared memory means is, um, if you have, uh, an \[00:12:00\] AI teammate that is a launch planner or a podcast production specialist, uh, and it's getting feedback on the work that it's doing from you, Andrew, or somebody else on your team, when a third person comes ahead and uses that AI agent, it will remember all of the nudges and the feedback, uh, that it's received from everybody, uh, and filter down based on the, on the particular project or task it's working on, and it can accomplish that task that much better.

\[00:12:29\] **Arnab Bose:** It's like onboarding a human teammate onto your team and then having multiple team members mentor and coach the person, and then the person remembers how, uh, the Dev Interrupted posca- podcast is produced and the nuances and the quality bar that you're achieving. So I'm just layering on the concept.

\[00:12:47\] **Arnab Bose:** So the first concept is the ability to access that enterprise work graph to see how historical work has been completed. The second is the ability to get to shared memory so that multiple people can \[00:13:00\] train the AI agent, and it constantly gets better with use and not just for one person. Uh, and the third thing is, uh, having a full audit trail of the activity that it's done in a way which is shared and visible by, uh, by anybody who's an administrator or a project lead.

\[00:13:17\] **Arnab Bose:** And what this, uh, achieves, this last part is, again, if you're accustomed to using, uh, AI agents as like your private personal, uh, chatbot, uh, actions are happening in a private thread or behind interfaces where the AI's involvement is invisible. You know, like all of the prompts that I put into, uh, leveraging a personal chatbot to create a presentation or to generate a video, that back and forth is something that is invisible if I simply download the presentation or video and put it back onto, a file share system, right?

\[00:13:53\] **Arnab Bose:** Like the file share system doesn't have a way to like represent the interactions. Whereas if you have \[00:14:00\] that same interaction with the AI teammate inside Asana, all of that is visible to your team. And if like your manager or somebody else who's an editor on the production team disagrees with something, they can also nudge the AI agent to, um, achieve the best outcome for Dev Interrupted.

\[00:14:18\] **Arnab Bose:** Um, and it'll be fully transparent to everybody on the team.

\[00:14:21\] **Andrew Zigler:** Right. the way that you've broken this down into three concepts is actually really profound. We've talked about a- how these are so game-changing for large organizations with a lot of other really smart folks here on the show. Like, the second thing that you called out there in particular, like beyond the first thing, which is the context graph, like yes, you- we have this like nice interconnected graph of who, what, when, where, why, uh, within like the world of work, right?

\[00:14:50\] **Andrew Zigler:** And then the second part, uh, this shared memory is so critical. We learned this too from, uh, we had Karthik Ramgopal, he's a distinguished engineer at LinkedIn,\[00:15:00\]

\[00:15:00\] **Andrew Zigler:** talking about how they transformed their engineering org, um, and about how they were working with AI, and it all started with unlocking shared memory for workflows across folks that were using, uh, these tools, allowing the gains to be aggregated, um, as a team, not just like on an individual basis.

\[00:15:19\] **Andrew Zigler:** And then also too, this, um, ability that you've very smartly called out for, admins, project leads, folks that are on the, uh, leading side of like building and deploying these agents to achieve certain outcomes for the business. They need to have that visibility too into how they're used and what that data looks like to the point where, you know, that whole creation, curation, learning process needs to happen in one closed space so that even the continuous conversations and training of it are, it's all together in one thing.

\[00:15:54\] **Andrew Zigler:** Like, you can go to an agent and you know the sessions and all the traces that resulted in this agent \[00:16:00\] being the way it is and what, and producing what it is. And honestly, I think that even goes back to like what I think the future of what, um, like code store is gonna look like for engineering teams.

\[00:16:10\] **Andrew Zigler:** Checking in and opening a PR and, and checking in your code is one thing. We need to also be checking in the session transcripts that resulted in the code. What did you prompt? What was your harness? What were your skills? Did you use an MCP server? Was this reviewed? Like, you know, there's so a- so much that's actually missing.

\[00:16:26\] **Andrew Zigler:** And then in an iterative process, uh, which engineering is, you know, people wanna

\[00:16:31\] **Arnab Bose:** Yep

\[00:16:31\] **Andrew Zigler:** that PR, and they wanna work on it and improve it, and now we can't because we don't have the work history of how we got there. We have to throw it away.

\[00:16:41\] **Andrew Zigler:** And the, the thing is, is that, you know, Asana can swerve and avoid this problem because you have the knowledge graph, you have the shared memory of everything in that work and how it got there, and if the AI agent that inter- is, is you know, created and interfaced all within Asana, then you can always pick back up where you left off in \[00:17:00\] terms of training or fixing or aligning it to outcomes, um, and it becomes a truly iterative process, which like knowledge work deeply is.

\[00:17:07\] **Andrew Zigler:** I

\[00:17:08\] **Arnab Bose:** Correct. Yeah

\[00:17:08\] **Andrew Zigler:** order like to really trust and, and, and fall in love with this kind of tool, right?

\[00:17:12\] **Arnab Bose:** So I wanna pick up from where you left off and, uh, and highlight a couple of things that we are working on on the product strategy side at Asana. So agentic work management is what we announced. It is-- It's available today where, you know, you've got these AI teammates that are pre-built based off of our existing knowledge worker personas, like people in marketing or operations or IT, and they're working side by side with human beings and they're, they're already deployed at, uh, at major companies like FedEx and COS, and people are-- COS is a European, um, clothing company, and actually interviewed their chief, uh, digital officer, at an event in London, and they've totally transformed how they go from a runway production to having those products available on their e-commerce site in a matter of hours, uh, \[00:18:00\] leveraging AI teammates in Asana and so that's, uh, that's the type of work that's already in production today and people are using us for.

\[00:18:07\] **Arnab Bose:** On the engineering side, we have a pretty interesting product we're working on called Command by Asana. Uh, it's in, uh, early access/design preview. We've got a couple of design partners who are deploying it, and most interestingly, our internal R&D team is using this to build, uh, Asana products. And it's built on this idea that you're calling out around iteration and compounding improvement.

\[00:18:30\] **Arnab Bose:** And what we were seeing was, um, in terms of leveraging the latest frontier model coding agents against, uh, our existing source code to build incremental features, even in the calendar year 2026, there's been a tremendous amount of improvement, and the ability to generate these PRs and, and, uh, um, code fast from a well-written ticket or spec has improved tremendously.

\[00:18:56\] **Arnab Bose:** But what was holding us back as a team \[00:19:00\] was actually going, uh, from the ideation process. Like you have a, a, a three-person whiteboarding session between the product manager, the engineering lead, and the designer. Going from that to well-written tickets in a nice iterative way, and then as you're calling out, tracking and logging all of the AI-powered coding sessions so that where-- if there's a voice of the customer feedback that comes up or there's, um, an issue we detect within our internal usage process, we can go back and automatically, uh, update the PRD, update the ticket with those learnings.

\[00:19:39\] **Arnab Bose:** So we build this self-learning loop end to end across all the knowledge work, the product work, the design work, and the engineering planning work, and, and not just focus on the code base. That's what we are lacking. And so Command is, uh, is all about that. It's about, uh, helping the, the product builder stay in flow where you \[00:20:00\] can have these cleanly built tickets based on all the context of meeting recordings and PRDs and historical tickets, plus your existing code base, generating that ticket so that your coding agent, needs less hand-holding and can, can get to a great outcome the first time.

\[00:20:17\] **Arnab Bose:** Uh, it's about ensuring that for, uh, product managers and engineering managers, uh, they can look through, um- Their entire Kanban board of like, uh, who is doing what by when. They can predictively find risks in the, in the process and automatically fi- figure out remediation. So somebody might be out on leave, there might be one person who's, uh, run into a significant issue because the PR previously generated has, uh, has, has some like infrastructure or scaling challenges, identifying all of that in a fully agentic way.

\[00:20:51\] **Arnab Bose:** And then the third thing is more for like executives like myself, where because all this work is tracked in Asana, I now have, uh, an \[00:21:00\] agentic interface where I can query the status of individual features all the way up to major product launches, and I'm getting like high quality results, uh, that I can trust out of the system versus only getting a particular slice of the pie, which is like, okay, what, what does my ticket status look like for coding?

\[00:21:18\] **Arnab Bose:** But not, not getting the insight into all of the planning and design milestones. So anyway, like th- this is what I wanted to come back to, which is I also believe that we are only in the early stages of improving the, the looping for, uh, the product building life cycle, just like we're in early stages for basic knowledge work.

\[00:21:38\] **Arnab Bose:** And a lot of improvements have been made in the coding agent, uh, slice of the work stream. But now I think everybody should open their aperture to what does it truly mean to go from idea to revenue when they're building a product, and then what is the tooling required to like manage that entire life cycle in a, in an agentic, um, \[00:22:00\] engineered way where you're con- constantly compounding benefits.

\[00:22:02\] **Andrew Zigler:** Yeah, I'm definitely subscribed to this narrative. I

\[00:22:05\] **Arnab Bose:** Yeah

\[00:22:06\] **Andrew Zigler:** something we're, we're really obsessed, especially with building at LinearB and understanding, like, developer productivity within an organization, but also tracking, like, AI's adoption and, and then its downstream impact. And, you know, tracking whether or not your AI adoption is, like, going well is really messy for most organizations because it's not just how many seats are getting used or how many tokens are getting consumed. It's about, you know, what are the tickets that are getting closed? How efficiently are we moving through, um, our epics? And when code is getting delivered, is it getting reworked later, right? Is it causing incidents or outages? Like, it, it becomes, like, a, a deeply, uh, complex problem to measure and understand, like, "Oh, we are being successful," or, "We are durably increasing our AI quality, in a sustainable way."

\[00:22:53\] **Andrew Zigler:** Like, I- I'm curious now just based on what you talked about, it sounds like that gets managed in Asana itself as, like, a data management \[00:23:00\] layer. You know, what are the things that y'all think about as measuring success in AI usage or using these kinds of tools and looking at that kind of data?

\[00:23:08\] **Ben Lloyd Pearson:** This episode is brought to you by LinearB, the engineering productivity platform. There's a widening gap between engineering teams that have turned up AI adoption into delivered work and the teams that haven't. It's real, it's measurable, and it's growing every month. We sat down with LinearB CTO, Yishai Beeri, to walk through new engineering benchmarks that are built upon two point seven million pull requests from over 250 engineering organizations.

\[00:23:35\] **Ben Lloyd Pearson:** These metrics cover why high AI usage correlates to a double increase in PR merge rate, and why more AI code doesn't automatically mean more shipped code, and the lowest effort win that's available right now, which is turning on AI code reviews. The full report is out. Get your copy over at linearb.io

\[00:23:55\] **Arnab Bose:** Some of the things that we're looking at are, what does the cycle time look \[00:24:00\] like? Because, I mean, uh, I'll tell you the challenge. Um, at a company like Asana based in San Francisco being like a technology company, there isn't any resistance, certainly within the R&D organization of like utilizing AI tools.

\[00:24:13\] **Arnab Bose:** In fact, people want to use everything all at once, right? Uh, so looking at adoption data is a bit of a red herring for us because that's also not helping us track true outcomes. And are we being efficient with our spend? Are we actually, driving real productivity or, are we ending up in a state where we are playing with a lot of tools, but we've actually, uh, not improved the craft?

\[00:24:35\] **Arnab Bose:** Um, and the same thing I think happens if you take a look at, uh, other, uh, lower-level metrics like, PR velocity and things like that. So we are trying to look at it, and again, we are in early days. I don't think we've like cracked the nut here, but we're looking at things like end-to-end cycle time for projects, right?

\[00:24:53\] **Arnab Bose:** Like does-- is the end-to-end project getting delivered faster quarter over quarter, month over month, \[00:25:00\] uh, for projects of similar size and complexity? So cycle time is interesting versus just pure PR velocity. And then there are some other gates that we're taking a look at as well for, okay, like you've built a particular update.

\[00:25:12\] **Arnab Bose:** Like let's say you were on the AI teammates team and you shipped AI teammates. Uh, are you seeing the level of adoption from a quantitative perspective and customer happiness from a qualitative perspective that you set out for? Because we also don't wanna be in a situation where everybody's patting themselves on the back because PR velocity is improved, and you can say you shipped 40 features this month versus 20 features last month.

\[00:25:37\] **Arnab Bose:** But 20 of those features are sitting on the shelf, or the ones that are actually being utilized are not meeting your quantitative or qualitative success metrics. So I think it's a combination of those three things, which is let's, let's not try to like, uh, degrade the analysis to simple things like are people u- just utilizing the tools or are you able to like check \[00:26:00\] in more things faster?

\[00:26:01\] **Arnab Bose:** Perhaps like looking at slightly longer term, a wider aperture around cycle time, uh, cycle time plus gates around adoption and quantitative, uh, qual- quantitative and qualitative success is, is a better metric. At least that's where like myself and my CTO counterpart have landed at. And I'm sure like as we, you know, iterate through this, we'll come up with like even more improvements going forward.

\[00:26:25\] **Andrew Zigler:** Yeah, absolutely. That's a really great glimpse into how y'all think about it. You know, the cycle time and understanding, you know, the ultimate impact of... I love how you said it from idea to revenue. That's, like, really what we're geared around as well, is understanding how do you go from, you know, even what you described it as, like, this three-person tiger team whiteboarding about, like, you know, they each come with their discipline, they each get it all laid out.

\[00:26:45\] **Andrew Zigler:** Like, how do we go from that, those people in a room with, you know, two pizzas to then having the, the revenue impacting delivery on the other end? And, uh, you know, the road to get there for organizations right now, it's, it's really rocky. Like, especially one \[00:27:00\] of the big rocks that, that orgs are navigating right now, especially in a world where tokens are, you know, relatively subsidized and compared to probably the future of what tokens will be for us, um, is, uh, people are getting, uh, big eyes for maybe building stuff instead of buying stuff.

\[00:27:15\] **Andrew Zigler:** You know,

\[00:27:15\] **Andrew Zigler:** Asana actually recently, uh, completed an acquisition of Stack AI, uh, this was to address cross-system execution. I'd love to learn more about that. So you were posed with the question of build versus buy. We're not gonna build a Stack AI. We're gonna buy it. You know,

\[00:27:30\] **Andrew Zigler:** why? What was the thought process there?

\[00:27:32\] **Andrew Zigler:** What does Stack AI bring, and, and what does that look like as, like, a durable moat for you?

\[00:27:36\] **Arnab Bose:** So, uh, I'll, I'll sort of start with the strategy first about why is that technology or functionality interesting to Asana, and then we could talk a bit about like build versus buy versus partner. So, uh, again, like having been a longer t- long-time Asana user yourself, you've seen the product, uh, evolving from being a system that tracks work and now with the power of AI and the, and the \[00:28:00\] fact that most systems that you're using in-- as a knowledge worker, uh, have great APIs, have MCP servers.

\[00:28:08\] **Arnab Bose:** You can go from the idea of tracking work and breaking down your projects into this sort of collaborative task, into the task actually completing themselves and the journey being fully completed. And so my vision is to sort of con- take Asana on this journey from being a collaborative work management product to agentic work management, where work is not just tracked, but AI is able to complete the work for you and the human beings are able to evaluate the work and constantly make it better in this compounding way uh, in terms of, uh, competencies and technology investments, the things that Asana's engineering team and product team is really good at is sort of defining these human experiences for, uh, looking at complex bodies of work, project management, project tracking, delivery dates, and the data models underneath the covers, uh, that power that context graph.

\[00:28:55\] **Arnab Bose:** We haven't historically invested a ton in building out complex multi-step \[00:29:00\] orchestrations and integrations. And again, like it is technically possible for us to go build it out, but before you invest in something truly new zero to one, it's a good idea to take a look around and be like, okay, um, is it worth it to take, you know, if you're doing a hundred dollar test, a hundred dollars of investment that you have today on, uh, R&D talent and carve out some of it to go build this other pretty complicated thing out, right?

\[00:29:25\] **Arnab Bose:** Like if you're trying to achieve enterprise-grade outcomes for regulated industries that is compliant and accurate, uh, these integrations are not one-shot fire-and-forget where you're posting "Hello, World!" into Slack. You're, like, reading data from BigQuery. You are, uh, doing some amount of extract, transform, load.

\[00:29:43\] **Arnab Bose:** There's a, a correctness you have to get right for a true know your customer, uh, onboarding flow for a financial services organization. So then we looked around and we're like, okay, which teams are doing great work here? And not only is their technology great, but they've found, \[00:30:00\] success for enterprise-grade workloads, uh, in regulated industries.

\[00:30:05\] **Arnab Bose:** Because if somebody has done that, then betting on that team, um, is like a no-brainer. You know, because then not only are you getting the acceleration of, the technology build, but you're also getting a customer base, you're also getting knowledge about these industry vertical specific workflows that because as Asana, we've historically been tracking the work, not doing it, we are not experts in.

\[00:30:28\] **Arnab Bose:** So it's a three-pronged benefit. It's the technology, it's the, uh, customer base/revenue stream within those, uh, industries that may not have been thinking about Asana as their top of mind. Uh, and it's also, uh, a certain amount of like industry vertical specific knowledge about those workflows. And the Stack team stood out to us for all those three reasons, right?

\[00:30:51\] **Arnab Bose:** They're a couple of like MIT PhD grads. They've got like a great tech team with them. They have amazing, uh, engineering velocity. They have \[00:31:00\] over a-- Like they've, they have a s- um, I don't know if I can say the exact number of customers, but like they have-- they had a significant number of customers even before we bought them.

\[00:31:08\] **Arnab Bose:** A lot of them were, a lot of them were in healthcare and life sciences or financial services industries. They were deployed live. Uh, they have, uh, a whole host of pre-built templates by industry vertical and use case that showcases like a depth of integration that's, that's not just a, a simple if this, then that style tool.

\[00:31:30\] **Arnab Bose:** So that-- those are the reasons why they were, they were a very attractive target. And it made sense for us to buy them because we are not just buying technology, and it's, it's more than, "Hey, you could vibe code your way into integrations." These are-- This is a full-blown product that has a revenue stream, that has the compliance certifications, that has the customer base.

\[00:31:52\] **Arnab Bose:** And so taking that was a great one plus one equal to three, right? Like if I have that capability where you can build out your, uh, AI \[00:32:00\] agent in a no-code way that can orchestrate, and that agent can now fit inside the Asana work graph and participate in a multiplayer shared memory work graph context conversation between all of us, now you're going to like executing the work, not just tracking it

\[00:32:16\] **Andrew Zigler:** Yeah, that sounds incredible. It sounds like a way, like you said, to follow through from i- task creation or ideation all the way to execution, and it's so smart to call out that, that, you know, it's more than the technology that you buy. It's the customer relationships, but then also too it's the domain expertise.

\[00:32:34\] **Andrew Zigler:** These are folks that are executing the workflows at scale that you've up until this point in, in your domain been managing. And so you're, like, marrying these two domains together, figuring out how to stitch them, uh, together and their different parts that make them super effective, like, as a user because now they can be closer than ever before.

\[00:32:53\] **Andrew Zigler:** And then like you said too, like, you know, obviously, um, buying this kind of technology, you buy the durability, you \[00:33:00\] buy all of the years of blood, sweat, and tears, and 2:00 AM incidents, and figuring out where all of the problems were so that you don't have to do that for the next few years so that you can be fully locked in, right,

\[00:33:11\] **Arnab Bose:** correct

\[00:33:11\] **Andrew Zigler:** on the vision.

\[00:33:12\] **Andrew Zigler:** And one of those visions is creating this, like, space where

\[00:33:15\] **Andrew Zigler:** work happens. It sounds like Asana is no longer a board, it's a place. And so, something I'm really curious about, I get excited about is, like, the multiplayer AI because up- you know, I'm a pretty agentically enabled engineer solo, and I use agents and orchestrate at scale in the terminal, you know, all the time. Uh, but I've found it very difficult to then merge and marry that up into, like, the knowledge working world, especially where it meets, uh, teammates and coworkers. You know, I've created my share of bots and widgets and proxies and all sorts of things that touch stuff like, uh, Asana and Slack and try to get things across the line from, like, what we're working on. And it's really tricky to do that in, like, a multiplayer \[00:34:00\] capacity. How do you think about, like, designing even just the user interface? How do you think it has to transform to, like, really show, uh, everyone in what they're doing? I'm curious.

\[00:34:10\] **Arnab Bose:** I think the, the end user experience aspects of getting multiplayer right were, uh, the most challenging things I've worked on in the last 10 months. Uh, I think, um, some of the investments that Asana has historically made in being this canvas where multiple people can interact with a body of work, and it's clear who it's assigned to, when it's due, uh, what parent project it's in, what-- where the common thread lies, were helpful for us to then go ahead and build the, the UI affordances that indicated, uh, what work was agentic, what wasn't.

\[00:34:46\] **Arnab Bose:** I think we learnt a lot in terms of what, what end users expect out of agentic work. You know, like a lot of work in Asana is asynchronous. You know, you assign a task to a person, and perhaps it's, uh, creating the show flow \[00:35:00\] for a podcast like this. You don't expect them to come back, uh, within a few seconds with the thing fully written out when it's a human being.

\[00:35:08\] **Arnab Bose:** But if you assign it to an AI agent, I think most people expect there to be some reaction instantaneously. Uh, there were some interesting experience challenges of like, okay, there is a quality versus, performance versus cost trade-off. Uh, because obviously we want to be able to leverage the rich data that we have in the work graph so that the agent is highly, uh, contexted and can produce a high-quality output.

\[00:35:32\] **Arnab Bose:** But if the, end user experience expectation is chat, then waiting for that kind of a deep research output is something that the, uh, end user is not accustomed to. So how do you give them a taste of like, okay, here is the work plan that the agent has come up with. Here are the places where you can actually give it feedback in near real-time if you disagree with its work plan.

\[00:35:56\] **Arnab Bose:** Here's a way to go take a look at the activity. Those are very interesting \[00:36:00\] experience challenges. So for example, the way in which we've landed for AI teammates is if you assign it a task, it will instantly give you like a reaction that's saying it's working. If you want to introspect what it's working on, you can click and open that up.

\[00:36:14\] **Arnab Bose:** And again, like we've seen initial users who are curious, like before they've had their aha moment, want to see those things. And over time, once you start trusting it, you're like, okay, it's doing its work. I don't, I don't really care about, you know, seeing its breakdown.

\[00:36:27\] **Andrew Zigler:** You have to earn that

\[00:36:29\] **Arnab Bose:** You have to earn that trust.

\[00:36:30\] **Andrew Zigler:** Ear- these

\[00:36:31\] **Arnab Bose:** Correct.

\[00:36:31\] **Andrew Zigler:** instrumented so we can peer in and earn that trust

\[00:36:34\] **Arnab Bose:** That's right. And so we, we had to come up with this UX affordance that would allow you to click into it and view activity. And then again, one of the first things we learned was we were, uh, uh, internally working on a way in which there would be a planning activity that would plan out the work, figure out its complexity, and break it down into sub-tasks.

\[00:36:52\] **Arnab Bose:** Uh, and we realized that from an experience perspective, we need to make all of that transparent and be tracked inside Asana. So we actually create \[00:37:00\] sub-tasks for the agent's plan, which are assigned to the agent. And we also worked a lot on the harness to ensure that if the human being disagreed with one of the sub-tasks or wanted to provide inline updates and said, "Okay, like you were planning on breaking down this competitive analysis by first looking at, uh, the top 20 vendors in the Gartner," I'm making that part up, right?

\[00:37:23\] **Arnab Bose:** Like, if it broke it up and said that, and you were like, "No, I actually don't care about the, uh, the analyst MQs, I want you to take a look at it based on revenue or something," you can interrupt the agent and tell it to change that particular part of its work plan, and we incorporate that into the run. So there's a bunch of things that we've done over there so that the interaction pattern for this multiplayer canvas makes sense and makes sense for agents, and that's slightly different than human beings.

\[00:37:55\] **Arnab Bose:** And then there are some things that we've learned from the 18 years of Asana human \[00:38:00\] experience that works really well in, in this, in this modality where because it's now a sub-task that you can see and you can see that Arnab's given this feedback, you know, if my boss, like if Dan, who's our CEO, is like, "No, I actually don't want you to do it that way," he can come and he'll-- he has the transparency to see that that's the way in which I nudged the agent, and he can change it.

\[00:38:18\] **Arnab Bose:** And all that's transparent, which is very different than the pure-play chat-based, uh, products out there, right? Because they don't have all of this canvas and, uh,

\[00:38:28\] **Andrew Zigler:** Right

\[00:38:29\] **Arnab Bose:** they don't have the data structures and the modalities to go support these things.

\[00:38:33\] **Andrew Zigler:** Yeah, there has to be like a level of transparency and openness. I need to be able to peer in and understand, like, you know, a company and its workflows and what it produces is inherently very complex.

\[00:38:44\] **Arnab Bose:** That's right

\[00:38:45\] **Andrew Zigler:** it's also entirely bespoke. This often isn't something where you go look up some docs or you ask your agent and figure it out.

\[00:38:51\] **Andrew Zigler:** Like, this isn't like you're picking up React. Like, you're trying to figure out how your specific company does this one very specific thing. And, you know, oftentimes, like what we've \[00:39:00\] learned with observability now in code is that source code is not really the source of truth about what your, what your code or product is.

\[00:39:06\] **Andrew Zigler:** It's observability and what's happening in production, what you're instrumenting and observing in the wilds. And so the same thing becomes true of knowledge working, but even like on a higher level of transparency needed. It's like a factory. You can see through all of it. And so, um, obviously you get these like diverging UXs where you have like, you need to ear- the ch- you get the, the agent has to earn your trust by showing you the work and its thought process.

\[00:39:32\] **Andrew Zigler:** You have to be able to look in and realign it, and you have to be able to do all of this in like a multiplayer kind of state. I'm curious too, like how do you think about interacting in that world with like levels of information security even within an organization? Because like obviously protecting the knowledge work from the outside world is critical, and that's what Asana is doing.

\[00:39:52\] **Andrew Zigler:** But even within the org, maybe there's certain levels of knowledge that's privy to certain folks working on certain things, and if we're talking about it all \[00:40:00\] just being in there as one big kind of slush pile, it gets really hard to audit who should know what or to prevent people from accidentally using someone else's privileges to know something they shouldn't. I'm just curious like how y'all think about that challenge.

\[00:40:13\] **Arnab Bose:** Yeah. So one of the things we've been working on, and it's part of AI agentic work management and how these AI agents show up is, um, because the agents are modeled as actors within the system, they actually have a profile page, and they have role-based access controls, uh, for themselves. This allows the agent to effectively have a manager or a set of managers that we call them, like editors for the agent,

\[00:40:37\] **Arnab Bose:** um, and then other people who can use them.

\[00:40:39\] **Arnab Bose:** Um, and then you, you can also have people who can't even use that agent. You know, for example, if, if it's something that, uh, is a secret M&A researcher that, uh, only the executives can use or whatever, you can lock it down. And I-I'm using this as a, as a fairly basic, like 1,000-foot level view, but hopefully it ex-explains some interesting concepts.

\[00:40:58\] **Arnab Bose:** So one is \[00:41:00\] all of the activity logs that I was talking about are written out in a way where they're auditable. So you can always go back and take a look at the specific steps an agent took before it produced work. The second thing is, because the agent has an ad-administrator or a manager, that administrator or manager can decide what parts of the work graph, what projects and portfolios the agent should be in and should have access to, and what projects and portfolios it shouldn't.

\[00:41:26\] **Arnab Bose:** Uh, the third thing is we also took the concept of shared memory and made it visible to the administrator. So as an administrator or set of admins, you can see what are the memories that have been recorded by the agent, and if you wanted to delete or forget something, it's a simple UI action, then there's a API for it as well.

\[00:41:44\] **Arnab Bose:** Then on top of that, uh, in that agent config page or profile page, it also showcases all of the integrations or third-party tools it has access to. Now, it invokes those tools, uh, on behalf and using the OAuth credentials of the, uh, \[00:42:00\] end user who triggers the task. So that way there is auditability of the action in the downstream system as well, because, you know, most of these downstream systems will have no idea what, you know, Arnab's teammate is, uh, but they'll know who I am, and it, it inhe- it inherits that permission set.

\[00:42:17\] **Arnab Bose:** But, uh, what we are doing is we are allowing attenuation where, uh, uh, end users can't just, like, uh, dynamically authorize new, uh, connections. There is a superset that the admin sets, uh, and they have full control over that, right? So you can say that, okay, these teammates should be able to read, write data from Google Drive.

\[00:42:37\] **Arnab Bose:** They should be able to post to Slack. Uh, they should be able to read data from Databricks Genie, but perhaps they shouldn't be able to do anything else beyond that. Now, once you've said that, the end users who have access to the agent then use their own OAuth credentials to do all these integrations. So all of the legacy, like auditability over the wire tracking, OAuth logs, et cetera, exist, uh, and you have that additional layer of \[00:43:00\] control, um, that, that sets what the superset is.

\[00:43:03\] **Arnab Bose:** So there's more, uh, that I can unpack over there, but hopefully that gives you like role-based access controls, having a real profile page, memory management, uh, connection management. These are all the concepts we thought through.

\[00:43:16\] **Andrew Zigler:** I know, it's like the secret ingredients of like

\[00:43:18\] **Andrew Zigler:** how you manage, uh, at scale. And what's been so fascinating for me in this conversation is, you know, uh, we often on this show break down these problems and put it and apply it to an engineering organization lens. And something that I've always experienced just as a go-to-market engineer, like an engineer who's embedded within a knowledge working organization, is that those worlds have never been more deeply related to each other, and the things that's working on one side are transferring to the other side with new levels of success that, you know, maybe haven't historically been possible.

\[00:43:49\] **Andrew Zigler:** So it's been exciting to discover the fun parallels that happen, you know, in the engineering side of understanding how do we unlock value from this? How do we create something durable? What are the \[00:44:00\] primitives that make this scale not at an individual level, but at an org level? Which is like where we started our conversation.

\[00:44:06\] **Andrew Zigler:** And so to learn that how in the, uh, in the knowledge working world in Asana, those same primitives prevail, and the last one that you hit on here, at least in our conversation, around role-based access and about profiles and levels of security, I think is really important, uh, for having almost like a scoped identity for the agent that is relegated or otherwise tied to a human operator or owner. Uh, we even too on the show have talked about the role of, uh, you know, so to say like guardian agents in

\[00:44:36\] **Arnab Bose:** Yeah

\[00:44:36\] **Andrew Zigler:** world, where you need, uh, real time or otherwise like only slightly lagging auditors of the work that's getting done by agents, flagging things in real time and realigning stuff. And this is like a more critical problem for people who are building and delivering AI agents that are like chatbots for like financial services, right?

\[00:44:54\] **Andrew Zigler:** You need to know in real time, like if you're deviating from what they need to do. So there's different \[00:45:00\] levels of stakes, but even in the knowledge working world, agents and the work that they do, they have to get tied back to people. They have to get audited. They have to get improved over time.

\[00:45:08\] **Andrew Zigler:** And that's the space that Asana is building for, for leaders to be able to kind of do, uh, with the knowledge work that they have every day. So it's been really exciting to follow, uh, the idea of where Asana is going. And I'm curious too, Arnab, just as we start to close things out here, is there anything else exciting top of mind that you wanna point folks to about where they can get oriented around the future of Asana or how they can think about unlocking some of this AI success for their teams?

\[00:45:34\] **Arnab Bose:** Yeah, I mean, uh, along with our, uh, work innovation summit, uh, announcement at London, we also refreshed the public asana.com site. So if you just go to asana.com, you can learn a lot more about agentic work management, what that means, um, customer success stories ar-around FedEx or COS or Acerbis, like there's Morningstar.

\[00:45:54\] **Arnab Bose:** There's a bunch of them who've been leveraging, uh, AI agents in the, uh, flow of knowledge \[00:46:00\] work. And these are, uh, companies that are in, you know, uh, existing sectors and, and s-- uh, and industries which have regulation, real world customers, uh, very, very real world use cases.

\[00:46:12\] **Andrew Zigler:** Yeah, the messy realities of agentic worlds.

\[00:46:15\] **Arnab Bose:** yeah. And so it's not, it's not the theore-theoretical, you know, Silicon Valley, I, I built a startup using this kind of workflow.

\[00:46:22\] **Arnab Bose:** These are like, you know, h-hardcore existing legacy businesses. So hopefully, like, uh, that will inspire, uh, some of your listeners and viewers to sort of see what they can do to l-leverage the technology themselves or what is their interpretation of it. Uh, and then, uh, my vision is truly building out this operating system for human agent teams.

\[00:46:45\] **Arnab Bose:** So I think there's, there's interesting nuances across all of that. So, uh, an operating system means there are multiple applications on it. So agentic work management is our first application on it. Uh, but we have many others in the hopper like Command for R&D \[00:47:00\] teams, uh, Asana Service Management for service help desks, Asana Client Management for teams that are, uh, working in professional services or agency capacities and they need a way to interact with their clients.

\[00:47:11\] **Arnab Bose:** And there'll be more to come. Uh, the other part of it is really honing in on human agent teams. You know, we believe that human teams are gonna get augmented and improved with AI agents joining them in the flow of work. And we want to make the human beings more productive, more im-- uh, focusing on taste-making and, uh, and higher quality work.

\[00:47:33\] **Arnab Bose:** And that is our, that is our goal and that is our focus. And so, uh, that also, I believe, will empower our customers and partners and stakeholders to, like, drive better outcomes versus simply focusing on, like, automating or, like, leveraging agents to, like, one-shot complete things. ' Cause I don't think that generates a world or a modality where your business is compounding and you're learning from it.

\[00:47:56\] **Arnab Bose:** Uh, so there's a philosophical aspect over here as well about empowering \[00:48:00\] the human being.

\[00:48:01\] **Andrew Zigler:** Yeah. And iterating in the loop, you know,

\[00:48:03\] **Arnab Bose:** Yeah

\[00:48:04\] **Andrew Zigler:** gonna become more iterative, not just like one-shot output. And so unlocking the place where that can happen with Synergy, I think is an exciting future to be building. And so we're gonna be following your story and what you're working on over at Asana. We'll also include these links in our show notes, you know, folks know where to find out about Asana so we can stay tuned with the story. And, um, you know, for those of you listening, if you made it this far, then you clearly loved what we talked about. Please be sure to come find us on Substack, LinkedIn, where we drop a newsletter accompanying this episode. Also be sure to give us like, you know, a thumbs up or whatever the case may be on wherever you're watching or listening to us. Please come find us on LinkedIn. Arnab and I are both on LinkedIn. Uh, we both would love to hear from you about what you thought about today's discussion.

\[00:48:44\] **Andrew Zigler:** And Arnab, thanks again for coming on Dev Interrupted. It was a pleasure to host you here, and I hope to talk to you again soon.

\[00:48:50\] **Arnab Bose:** Thanks so much for having me. Great conversation, Andrew

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  "description": "LinearB is the engineering productivity platform that helps engineering leaders prove AI is improving throughput without sacrificing delivery confidence, flow efficiency, or developer experience.",
  "sameAs": [
    "https://www.linkedin.com/company/linearb"
  ],
  "award": [
    {
      "@type": "Award",
      "name": "LinearB is a Leader in the 2026 Gartner® Magic Quadrant™ for Developer Productivity Insight Platforms",
      "dateAwarded": "2026",
      "awardedBy": {
        "@type": "Organization",
        "name": "Gartner®"
      }
    },
    {
      "@type": "Award",
      "name": "Great Place to Work Certification",
      "dateAwarded": "2025-2027",
      "awardedBy": {
        "@type": "Organization",
        "name": "Great Place to Work"
      }
    },
    {
      "@type": "Award",
      "name": "America's Best Startup Employers 2025",
      "dateAwarded": "2025",
      "awardedBy": {
        "@type": "Organization",
        "name": "Forbes Magazine"
      }
    }
  ],
  "hasCertification": [
    {
      "@type": "Certification",
      "name": "SOC 1 Type 2"
    },
    {
      "@type": "Certification",
      "name": "SOC 2 Type 2"
    },
    {
      "@type": "Certification",
      "name": "GDPR Compliance certification"
    },
    {
      "@type": "Certification",
      "name": "ISO 27001"
    }
  ]
}
```

## More on linearb.io

### Top navigation

- [Book a Demo](https://linearb.io/book-a-demo)
- [AI Code Reviews — Catch security risks, bugs, and spec mismatches](https://linearb.io/platform/ai-code-reviews)
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- [Measure AI Impact — Track AI adoption and tie it to delivery outcomes](https://linearb.io/use-case/measure-ai-impact)
- [MCP Server — Chat with your data to spot patterns and boost output](https://linearb.io/platform/mcp-server)
- [Resource Allocation — Cost initiatives and shape your investment strategy](https://linearb.io/platform/resource-allocation)
- [Cost Capitalization — Capitalize engineering costs with audit-ready reports](https://linearb.io/platform/cost-capitalization)
- [Dev Team Management — Set targets and tie throughput to business outcomes](https://linearb.io/platform/goals-and-reporting)
- [DevOps Workflow Automation — Policy-based PR routing, approvals, and tests](https://linearb.io/platform/ai-workflow-governance)
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- [Watch now](https://linearb.io/resources/engineering-productivity-gap)
- [Customers](https://linearb.io/customers)
- [Pricing](https://linearb.io/pricing)
- [Why choose LinearB — Explore your data. Measure performance. Act to improve it.](https://linearb.io/why-linearb)
- [APEX framework — The operating model for AI-era engineering teams](https://linearb.io/resources/apex-framework)
- [Anti-FAQ — The questions other vendors won't answer](https://linearb.io/why-linearb/anti-faq)
- [Security — Enterprise-grade compliance and zero code access](https://linearb.io/security)
- [Build vs. buy — The hidden cost of building it yourself](https://linearb.io/resources/build-vs-buy)
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- [Engineering glossary](https://linearb.io/library/engineering-glossary)
- [Developer productivity](https://linearb.io/library/developer-productivity)
- [AI in software development](https://linearb.io/library/ai-in-software-development)
- [Engineering management](https://linearb.io/library/engineering-management)
- [Developer experience](https://linearb.io/library/developer-experience)
- [DevOps](https://linearb.io/library/devops)
- [Engineering operations and the context layer](https://linearb.io/library/engineering-operations)
- [Engineering efficiency](https://linearb.io/library/engineering-efficiency)
- [Software delivery](https://linearb.io/library/software-delivery)
- [Research and data](https://linearb.io/library/engineering-benchmarks-and-research)
- [LinearB is a Leader in the 2026 Gartner® Magic Quadrant™ for Developer Productivity Insight Platforms](https://linearb.io/resources/gartner-magic-quadrant-dpi-platforms-2026)
- [Sign in](https://app.linearb.io/login)
- [Enterprise](https://linearb.io/solutions/enterprise)
- [Contact](https://linearb.io/contact-us)
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- [Service agreement](https://linearb.io/services-agreement)
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- [Substack](https://devinterrupted.substack.com/)

### Footer

_Additional links from the site footer, not repeated from the top navigation above._

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