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Evaluation Engineering (Evals)

All digests tagged Evaluation Engineering (Evals)

Inside Clay's Eval Stack: 300M Agent Runs, One LangSmith Pipeline thumbnail

· 13:01

Inside Clay's Eval Stack: 300M Agent Runs, One LangSmith Pipeline

Clay detailed its agentic stack, featuring Claygent (go-to-market research) and Sculptor (go-to-market engineering workflow orchestration). The discussion emphasized that scaling agents to millions of runs requires a robust evaluation philosophy. Key architectural shifts include adopting a data lake for unified first/third-party data access, implementing comprehensive multi-stage evaluations (from local dev to CI), and leveraging large context models (like Fable) to enable complex, self-iterating agent loops.

Key takeaways

  1. Agentic Systems are Becoming Interfaces

    Clay is evolving into an 'agent interface,' making all parts of the product available via CLI and public API. This creates a flywheel where internal and external agents share the same tools, allowing failures in tool invocation to provide user signal for continuous improvement.

  2. Evals are Non-Negotiable for Scale 3:23

    With billions of runs, comprehensive evaluation suites are critical. Clay's philosophy mandates different levels of testing: low-lift local dev checks, and staging/CI environments that mirror production harnesses.

  3. Data Lake Foundation for Agents

    To handle disparate data sources (LangChain traces, Snowflake, Postgres, ClickHouse), Clay is moving to a data lake architecture. This allows agents to run wild on unified first-party and third-party data while enabling safe shadow builds.

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Datadog Deleted All Its AI Context. It Worked. thumbnail

· 1:01:23

Datadog Deleted All Its AI Context. It Worked.

Datadog detailed its journey scaling AI coding agents across 4,000 engineers, highlighting that performance improvements were achieved by deleting years of accumulated context files (context rot). The discussion emphasizes the critical role of building dedicated evaluation (evals) platforms to make data-driven decisions about model selection and agent capabilities. Key findings include using evals to replay historical PRs for code review guardrails and adapting hiring practices away from traditional LeetCode interviews toward real-world, large codebase tasks.

Key takeaways

  1. Context Rot: Deleting Context Improved Performance 2:49

    The team found that deleting old, accumulated AI context files (written prior to models like Sonnet 3.5) led to better evaluation scores, demonstrating 'context rot'—where historical information becomes irrelevant or harmful to the agent's performance.

  2. Evals for Code Review and Regression Testing 5:49

    The first concrete application of evals was building a platform that replays historical PRs known to have caused incidents, allowing agents to act as a last guardrail before production deployment.

  3. Shift from Productivity to Ambition 59:02

    The core lesson learned is that the goal of AI adoption should not solely be increasing productivity, but rather 'increasing ambition'—enabling teams to attempt and validate more complex ideas.

  4. AI-Driven Interviewing 53:25

    The process of hiring is evolving away from low-signal LeetCode interviews toward real-world scenarios that require AI to navigate and understand large, complex codebases.

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