3 production patterns for AI agents and how to evaluate each one

In a presentation at Arize Observe 2026, Sam Bhagwat, CEO of Mastra, emphasized the importance of understanding the type of production AI agent being developed, rather than simply focusing on whether or not an agent is being built. He outlined three primary production patterns for AI agents: customer-facing, internal enterprise, and developer platform, each with distinct characteristics and challenges. Customer-facing agents need to be highly context-aware to understand user-specific data and workflows, while internal enterprise agents must navigate fragmented data systems and organizational friction. Developer platform agents prioritize standardizing primitives to enhance efficiency for other developers. Bhagwat stressed the importance of context engineering over model selection and highlighted the essential roles of evaluation and observability in ensuring the agents' success in production environments. Properly implementing evaluation mechanisms is critical to addressing cost, accuracy challenges, and determining the appropriate changes needed when agents underperform.

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