Date & time
17:00
Register for the panel discussion
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Most engineering teams have no way to track their AI agents’ output. Budgets per engineer are tightening, but leaders still can’t say which models are worth the cost, whether their agents are actually making them faster, or how usage compares to other teams.
The problem gets bigger the longer an agent runs, as what used to be a one-line suggestion becomes a multi-step black box: planning, writing, testing, and opening the PR with no efficiency tracker.
This workshop puts that instrument in your hands. Working in a live GitKraken Insights sandbox loaded with real-world data, you’ll learn to read your own agentic workflow across adoption, autonomy, activity, cost, and outcomes. You will also be able to benchmark against your own organization.
Participants will learn:
- How to measure the adoption, autonomy, activity, cost, and outcomes of your own agentic workflow and know exactly what to improve next
- Which models and skills are worth their cost for different kinds of work, and where you’re likely overspending
- How to scale your agent work without scaling the bill
