AI Deep Dive (Virtual) - Building Self-Improving Agents
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Join Snowflake to learn how to build self-improving agents
Tech Talk: Your Agent Should Fix Itself: Building Self-Improving Agents
Speaker: Elliot Botwick, Principal AI/ML Architect, Snowflake
Abstract: In this talk, we will share how coding agents help developers build high quality agents faster.
A key insight from building agents in production is that high quality agents operate with their goals, plans and actions aligned. We introduce the Agent Goal-Plan-Action (Agent GPA) framework to capture this insight, which achieved state of the art benchmarks on TRAIL/GAIA with 95% error coverage and 86% error localization.
The Agent GPA framework assesses the full agent's process:
- Was the goal achieved efficiently?
- Did the plan make sense?
- Were the right tools used?
- Did the agent follow through?
Without visibility into these steps, teams risk deploying agents that look reliable but create hidden costs in production. Inaccuracies can waste compute, inflate latency and lead to the wrong business decisions, all of which erode trust at scale.
We will show how to use coding agents to automate the process of measuring and improving your agent's GPA by using optimization skills that take advantage of the GPA evaluation framework. By the end, you’ll be able to use coding agents and the GPA framework to identify common agent failures, improve their agent and make it ready for production.
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