In this episode of GydeBites, host Prasanna Vaidya speaks with Shir Meir Lador, AI Leader at Google, about what it takes to build AI agents that perform reliably in production. Shir explains why context engineering, MCP, memory, and automated evaluations have become the foundation of modern AI systems.
The conversation explores multi-agent orchestration, context management, evaluation pipelines, and practical strategies to improve reliability in complex enterprise environments.
Whether you're a developer, architect, or enterprise leader, this episode offers actionable insights for designing AI agents that are dependable, efficient, and production-ready.
Resources:
• Agent Factory video podcast
• Blog from NEXT talk
• Lab for building a multi-agent system with a quality loop
• Lab for evaluating a multi-agent system
• Lab for securing a multi-agent system
• Evaluation blog
• Demo illustrating Agent skills on Antigravity
Questions covered in this episode:
What is context, and how do context and grounding become the foundation of a successful AI agent?
How does MCP connect agents to enterprise systems?
What is multi-agent orchestration, and at what scale should it be used?
What is agent memory, and how does persistent memory improve agent performance?
What is automated evaluation, and how does it improve agent reliability?
