June 29 – July 2, 2026 · San Francisco, CA · imported from ai.engineer's public schedule feed

AI Engineer World's Fair 2026 — unofficial import demo

Unofficial demo. This programme was imported from the AI Engineer World's Fair's own public schedule feed to show vibeboard at real conference scale. Not affiliated with, or endorsed by, the organisers.

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Context EngineeringSession

Build-Time vs. Run-Time: Why Your Dev Tools Will Fail in Production

Averi Kitsch, Prerna Kakkar

When
Wednesday, July 110:45 AM – 11:05 AM · 20 min
Where
Track 8San Francisco, CA · imported from ai.engineer's public schedule feed
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About this session

A dangerous pattern is evolving in the ecosystem: developers are deploying "Build-Time" tools into "Run-Time" environments. In this session, we will introduce a critical distinction for the MCP ecosystem: the difference between Build-Time Agents (Developer Assistants like Gemini Code Assist) and Run-Time Agents (End-user applications like a Customer Support bot). Drawing from our experience building the MCP Toolbox, we will demonstrate why the "Atomic" tools that make Build-Time agents powerful become catastrophic liabilities for Run-Time agents. We will provide a framework for transitioning your architecture across three key axes: Design: Moving from flexible, atomic primitives to "Composite Workflows" that encapsulate business logic. Security: Shifting from "Developer Identity" (trusted) to "Workload Identity" (zero-trust), where the agent is treated as an untrusted user. Reliability: Why production agents need "Agent-Readable" errors (natural language guidance) rather than the stack traces that developers rely on. Attendees will leave with a clear rubric for evaluating whether their tools are truly "Production Ready" or just "Prototype Ready."

Speakers (2)

Averi Kitsch
Averi Kitsch

Staff Software Engineer, Google

Averi Kitsch is a Staff Software Engineer at Google dedicated to bridging the gap between raw data and active intelligence. As the engineering lead for the MCP Toolbox, Averi empowers developers to build sophisticated, agentic applications directly on top of their Google Cloud databases. Drawing from a deep background in DevOps—with specific expertise in serverless runtimes and CI/CD—she brings a pragmatic, "builder-first" perspective to AI infrastructure. Her ultimate goal is to ensure the next generation of intelligent applications is as robust and scalable as it is smart.

Prerna Kakkar
Prerna Kakkar

Senior Software Engineer, Google

Prerna Kakkar is TL for Agentic Evaluation for Google Cloud Databases and is an active contributer to Evalbench and MCP Toolbox for Databases.

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