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Learn how Zapier transformed their support team's ability to handle thousands of integration fix tickets by building AI-powered tools that enable support staff to ship code directly. Discover the evolution from failed API playground tools to successful MCP IDE integrations, culminating in Scout Agent - an autonomous system that reads support tickets, gathers context, generates fixes with tests, and submits merge requests ready for review. Explore the critical lessons about context gathering, trust-building through transparent AI reasoning, and the importance of embedding tools directly in existing workflows like GitLab to prevent context switching. Understand how to measure AI tool effectiveness through three distinct failure modes: categorization accuracy, fixability assessment, and solution quality, and see how this approach now drives 40% of Zapier's integration fixes while maintaining quality and engineer trust.
