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This article argues that failures in AI agent systems can be understood through the lens of distributed systems, drawing parallels between agent behavior and classic distributed system problems.
Discusses workarounds for tool-call failures and looping issues in Qwen3.6-27B model.
An analysis of multi-agent LLM pipeline failures, citing the Berkeley MAST paper which attributes most failures to coordination issues (specification, inter-agent misalignment) rather than model capability, and suggests dedicated verifier agents as a fix.
Discusses five common observability gaps in production voice AI stacks, including blending infrastructure and conversation failures, lack of VAD visibility, inadequate sampling, noisy auto-generated evals, and evaluating at the wrong level.