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21Stack Overflow · practice
Taking a look under your agent’s hood
This post discusses the challenges and methods of applying observability to AI agents, which combine traditional distributed systems telemetry with new requirements for monitoring LLMs and multi-step tool calls. The discussion highlights how non-deterministic agent behavior and increased speed of execution make continuous production feedback loops essential for upstream development. Ultimately, integrating observability data into automated testing and development loops helps teams iterate on agent performance.
DEPTH ■■■■■25 min read2 weeks ago
Machine Learning Observability
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22Stack Overflow · practice
Professional skepticism is a dev’s best skill
This podcast episode discusses how professional skepticism and QA testing principles are increasingly vital for software engineering, particularly when developing with AI agents. The guest explores the integration of testing into agent-driven development workflows and highlights the ongoing need for human oversight to catch regressions and prevent fragile code. The discussion emphasizes that critical thinking and careful verification remain essential despite advances in automated tooling.
DEPTH ■■■■■26 min read4 weeks ago
Machine Learning
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