The Top 3 Data Quality Practices for Successful AI Application Development

For software engineering leaders, data availability and quality issues now represent the primary barrier to AI implementation. Organizations that lack automated quality controls embedded throughout the software development life cycle (SDLC) face escalating risks: poor data quality disrupts business operations with bugs, triggers compliance violations, and derails modernization projects. Software engineering leaders can avoid costly … continue reading

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Pinterest Deploys Production-Scale Model Context Protocol Ecosystem for AI Agent Workflows

Pinterest engineering teams have deployed a production-ready Model Context Protocol (MCP) ecosystem that allows AI agents to automate complex engineering tasks and integrate diverse internal tools. Domain-specific MCP servers, a central registry, and human-in-the-loop approval improve security, governance, and developer productivity while saving thousands of hours per month.

By Leela Kumili