Using AI to Accelerate Hiring and Productivity at Zapier

Welcome to the latest issue of Engineering Enablement, a weekly newsletter sharing research and perspectives on developer productivity.

Announcements:

• 🗓️ Laura will host a year-in-review roundtable next week with developer productivity researchers from Microsoft, Google, and GitHub, covering this year’s biggest insights and what engineering leaders should expect in 2026.
• 📚 Abi just announced his book, Frictionless, co-authored with Nicole Forsgren, focusing on developer experience. Get a copy here.

Across engineering organizations, AI is often presented as a cost-cutting tool designed to automate work, reduce headcount, and foster leaner teams. However, as more companies move beyond initial pilots, a different pattern is emerging. When AI is applied to the right challenges, it doesn’t shrink engineering teams; instead, it significantly increases the return on every engineer you hire.

Zapier serves as a clear example of this paradigm shift. I recently had the opportunity to interview Andrew Kordampalos, who leads Zapier’s AI Agents transformation. Zapier has built an internal ecosystem of lightweight AI agents that automate thousands of operational and administrative tasks across engineering, product, and internal operations. Their experience points to a simple conclusion: as AI amplifies the value of each engineer, it becomes more rational to hire more engineers, not fewer.

This article examines how Zapier reached that conclusion and cultivated a culture that treats AI as a force multiplier for hiring and value creation, rather than solely a headcount-reduction strategy.

Using AI to Remove Friction Around Engineering Work

Zapier’s starting point was not, "How do we replace developers?" but rather, "Where does work slow developers down?" When Kordampalos joined, his team had grown out of a startup (acquired by Zapier) that initially built real-time meeting transcript agents. Their team quickly pivoted to a bigger question: Would it be more effective to automate the choreography surrounding engineering tasks rather than the core engineering work itself?

With this line of thinking, they formed a hypothesis: engineers would become far more productive if the friction around them dissolved through agentic automation. The goal was not to change what engineers do, but to increase the proportion of their time spent on work only they can perform.

Zapier’s AI Agents team introduced a network of lightweight internal agents that automate the coordination and overhead surrounding development work:

• Async Standup Agents: Agents that collect async standup updates and summarize them, allowing teams to replace five daily standups with two weekly sessions.
• Onboarding Agents: Agents that run onboarding steps, including generating email signatures and triggering access to tools, reducing onboarding time to roughly two weeks—significantly less than the industry standard 30–90 days.
• Workflow Agents: Agents that manage Slack workflows, approvals, and operational routines, cutting down on context switching and interruptions.

The underlying mindset is simple: “When you find you’re doing the same thing again and again, you build the automation.” Zapier already had a culture of “build the robot,” and AI agents gave that culture a larger surface area.

How AI Agents Changed Zapier’s Hiring Math

As Zapier deployed more internal agents, they observed a compounding effect. New hires reached productivity faster. Existing employees increased their throughput without extending their working hours. The organization started to experience AI not as a way to substitute for engineers, but as infrastructure that made every engineer more effective.

This is where the economics shift. If AI removes 10–15% of the administrative and coordination work from an engineer’s week, their effective output goes up. The cost of hiring remains the same, but the value per hire increases. In that scenario, reducing headcount undercuts your own leverage.

Zapier leaned into this logic. As Kordampalos puts it, “We are doubling down and hiring even more people because we want to boost our productivity by using AI.” AI didn’t replace engineers; it replaced the parts of engineering work that prevent engineers from doing their best engineering.

The Move from Summarizing Meetings to a Full Agent Ecosystem

Zapier’s path to a broad agent ecosystem began with a narrower use case. Through the acquisition of Vowel, Kordampalos had already been working with early AI models that transcribed and summarized meetings in real time. That work raised a broader ambition: to make automation conversational, accessible, and embedded in daily work, rather than something only specialists configured. “We realized that the future of creating automations wouldn’t be drag-and-drop interfaces,” he says. “It would be natural language; that’s the universal interface.”

Instead of relying solely on traditional workflows, teams at Zapier began building small agents using their own platform to handle repetitive internal tasks: managing approvals, posting updates, summarizing meetings, and even generating email signatures for new hires. Over time, “There are more bots than humans at Zapier” moved from a joke to a fair description of reality.

For most engineers, the biggest drag on productivity isn’t code; it’s meetings, as I further explore in this article about the true opportunities for productivity improvement with AI. When Kordampalos’s team grew, he noticed they were spending too much time on daily standups. So they built a pair of agents: one that pinged each team member for their async updates, and another that summarized them for everyone. Almost overnight, the daily standup became a twice-a-week sync. “It’s not always one magic automation that replaces your work,” he explained. “It’s the orchestration of smaller agents and bots that you have.”

The insight that multiple small agents can work together like a team (referred to at Zapier as a “pod”) has guided Zapier’s approach. They use Slack as the coordination layer. Agents communicate there, respond to emoji reactions, post updates, and even hand tasks to one another. This ecosystem evolved organically, with employees encouraged to test new agents in sandbox channels before rolling them into production.

On average, Kordampalos says, a new idea takes days, not weeks, to become a working agent.

What Platform and DevEx Leaders Can Learn from Zapier’s Model

Zapier’s experience offers a playbook for any platform or DevEx team looking to introduce AI-driven automation inside their organization:

1. Start with the real bottlenecks. Automate daily stand-ups, status reporting, onboarding steps, and the tasks that steal cognitive energy from engineers.
2. Keep the feedback loops tight. Give teams space to experiment safely. Launch agents in controlled channels, observe the signal-to-noise ratio, and scale what works.
3. Build observability and governance early. Create a single dashboard or inventory of agents to manage ownership, access, and performance.
4. Tie automation to outcomes, not novelty. Focus on measurable gains: reduced meeting hours, faster onboarding, or improved throughput per engineer.

Conclusion: Replace or Augment?

Zapier effectively tested both paths. They could have treated AI as a way to justify a smaller engineering organization. Instead, by focusing on friction reduction, they turned AI into infrastructure that increased the value of every engineer and made additional hiring more attractive.

Their lesson: AI shouldn’t replace engineers. It should replace the friction around engineers, freeing organizations to hire confidently and get more value from each individual contributor. In a world where AI is reshaping how software gets built, the companies that win will be the ones that use AI to augment their teams and accelerate headcount investment, not the ones that try to do the same work with fewer people.

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Who’s Hiring Right Now

This week’s featured DevProd job openings. See more open roles here.

• American Express: Sr. Manager, Digital Product Management - DevProd | Hybrid - London, UK
• Capital One: Product Manager - Developer Experience | Plano, TX; McLean, VA; Richmond, VA
• Plaid: Software Engineer - Platform | New York, NY
• Reddit: Staff Software Engineer - Developer Experience | Remote - United States
• Tesco: Senior Product Manager - Infrastructure | Hybrid - Tesco UK, Welwyn Garden City
• Whatnot: Software Engineer - Platform | San Francisco, Los Angeles, Seattle, NYC

That’s it for this week. Thanks for reading.

A guest post by Justin Reock Deputy CTO of DX (getdx.com)