# Five sessions and a hackathon: How we turned skeptics into agent builders

DevFeed: [Five sessions and a hackathon: How we turned skeptics into agent builders](<https://devfeed.tech/articles/five-sessions-and-a-hackathon-how-we-turned-skeptics-into-agent-builders-32259.md>)

Original publisher: [Read original article](<https://medium.com/data-science-at-microsoft/five-sessions-and-a-hackathon-how-we-turned-skeptics-into-agent-builders-4320f3eb1af1?source=rss----a6e43238cdaf---4>)

Author: Jay Garg

Published: 2026-06-23T07:16:01Z

Content type: opinion

Language: en

Sources: [Data Science at Microsoft](<https://devfeed.tech/sources/data-science-at-microsoft.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Learning](<https://devfeed.tech/topics/learning.md>)

Tags: [adoption](<https://devfeed.tech/tags/adoption.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [building](<https://devfeed.tech/tags/building.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [engineering-management](<https://devfeed.tech/tags/engineering-management.md>), [hackathon](<https://devfeed.tech/tags/hackathon.md>), [leadership](<https://devfeed.tech/tags/leadership.md>), [productivity](<https://devfeed.tech/tags/productivity.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>)

## AI overview

This article presents lessons from a five-week AI enablement series and a two-hour hackathon for an infrastructure engineering team. It argues that low AI adoption was caused less by access or time than by a lack of practical mental models and opportunities to re-engage with AI, culminating in participants building working agents.

## Source excerpt

A field-tested playbook for teaching an engineering team to actually work with AI -- not just have access to itIllustration of team members collaborating around a table with laptops and notebooks, discussing holographic charts of network nodes and hexagonal shapes representing AI agents being assembled. Image generated with Microsoft Designer. Three sessions in on teaching a class whose curriculum I'd designed around building with AI, I asked the room a question I'd been avoiding. What's the thing about AI you don't say out loud? The answers came slowly at first. "I don't know where to start." "I'm worried it'll replace me." "I tried it once and it gave me garbage, so I stopped." That last one came up the most. People had given AI a shot a couple of years back, when the first wave hit. The output didn't meet the bar they hold their own work to, so they quietly filed it under "fun toy, not a serious tool" and moved on. That was the moment the curriculum I'd written stopped being a curriculum and became a conversation. This is what we learned running a five-week, thirty-minute-a-week AI enablement series for a high-performing infrastructure engineering team -- and the two-hour hackathon at the end where everyone, including managers and PMs, shipped a working agent. The paradox we started with Picture a senior engineering team. Already shipping. Already busy. Tools available, leadership encouraging use, no policy blockers. And yet -- adoption was low and shallow. A handful of folks were using AI daily. The rest were using it for autocomplete and stopping there or not touching it at all. The same people who would happily spend a weekend learning a new distributed systems primitive weren't spending 15 minutes learning how to make an agent do their service support. That's a paradox worth sitting with for a moment. The barrier wasn't access. It wasn't even time, not really. It was that everybody had tried AI in its bad season, and nobody had been given a reason to try it agai