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AI Native DevCon Day 2: From Agent Demos to Operating Models

Rohan Sharma 2026年06月03日 14:40 4 次阅读 来源:Dev.to

TL;DR Day 2 of AI Native DevCon shifted from agent capability to operating discipline. The strongest sessions focused on how teams can run AI-native delivery with clearer context pipelines, measurable agent behavior, safer execution boundaries, and better organizational ownership. The scale showed up in the numbers too. Across the two days, DevCon brought together 650+ in-person registrations, around 2,000 online registrations, and a packed mix of sessions, workshops, hallway conversations, and practical lessons. Day 2 leaned into workshops. That shift mattered because the second day was less about proving agents can do useful work and more about showing how teams can make that work repeatable. Hey there, welcome back. Rohan Sharma here again continuing the devcon series. Day 1 gave us the framing, including Guy Podjarny ’s core point that skills should be treated like real software assets. Day 2 picked up from there and moved into the operating details. Once agents are inside daily engineering work, platform and product teams need to decide what changes first, who owns those changes, and how the results are measured. Talks that shaped Day 2 Harness engineering beyond code Marc Sloan from Tessl focused on the next gap many teams are hitting. Code context is increasingly structured, but product and design context still lives in external systems such as Figma, Notion, and Linear. Pulling that context live can reduce staleness, but it introduces drift in evals, versioning, and reproducibility. The practical lesson was to stop treating external product and design context as random reference material. Teams need a defined layer between the repository and those external systems, with clear versioning so evaluations can be replayed against known context snapshots. Without that, agents can produce work that looks technically correct while missing the product constraint that actually mattered. That is a very expensive kind of almost-right. From vibes to metrics Simon Obstbau

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