Kevin Haggard, Vice President of Engineering at Barracuda, joins The Tech Trek to talk about how AI is showing up across cybersecurity products, engineering workflows, team adoption, and software delivery culture. He shares how Barracuda is approaching AI with guardrails, why adoption varies across teams, and what happened when the company ran protected AI dev days for the engineering organization.

What to take from this episode

* AI adoption inside engineering teams will not be even. Some teams are already orchestrating agents from requirements to deployment, while others are still figuring out where AI fits into their day to day work.

* Guardrails matter more in security sensitive environments. Barracuda uses an AI gateway and control plane so teams can experiment without leaking data or letting agents take uncontrolled actions.

* Protected time changes behavior. Barracuda’s AI dev days gave teams three days with no meetings so they could work with the tools inside real projects instead of treating AI as a side experiment.

* The coding bottleneck may move. AI can create more code faster, but QA, testing, release safety, problem definition, and rollback mechanisms become even more visible.

* The engineer’s role is shifting from operator to orchestrator. Kevin argues that system design, review, context, and crisp instruction will become more valuable as agents take on more execution.

Key moments

00:28, What Barracuda does and how AI pairs with people in cybersecurity

03:35, Why AI adoption varies across engineering teams inside a larger organization

05:06, The need for AI guardrails, gateways, and control planes

06:20, How Barracuda ran AI dev days across the organization

08:42, The light bulb moments from product, design, and engineering teams

13:07, Why AI velocity makes existing delivery bottlenecks harder to ignore

17:00, How engineers may move from writing code to orchestrating agents and reviewing systems

One line that stuck:

“Their role is going to elevate more.”

Practical moves from Kevin’s experience

* Bring trusted partners in for training, but follow that with hands on sessions.

* Give teams protected time to use AI inside actual work, not just demos.

* Share what the advanced teams are learning so adoption does not stay isolated.

* Keep people in the loop, especially when agents are generating code, tests, or workflow changes.

* Work backward from release bottlenecks, not just coding speed.

Subscribe to The Tech Trek for more conversations on how technical teams are adapting around AI, data, platform, product, and engineering execution.

#agenticai #ai #techleadership #engineeringleadership #engineering