Why your AI Agents need an Agent Development Lifecycle
Date to be announced · Time to be announced
Your agent demo works. It's fast, it's cheap, it's magic in the sandbox. Then you deploy it and everything falls apart. No one agrees on how to test it, monitor it, or improve it, and the token bill doesn't care that it's "still just a pilot." We solved this exact problem for code decades ago with the SDLC. We solved it again for APIs once the sprawl got out of hand. Now it's agentic AI's turn, and teams are trying to skip the step where you learn from history. In this talk, I'll break down the Agent Development Lifecycle (ADLC): what it borrows from software and API management, and what to actually consider when designing an agentic system: cost, governance, security, and repeatability. Organizer timezone/event-window conflict: normalized date/time deliberately null. Published local schedule: 10:30–11:00. Timezone requires verification.
Session status: scheduled
Room: MPR A
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In person · Microsoft Toronto, Toronto, Ontario, Canada
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