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Multi-turn conversations represent one of the most challenging aspects of building reliable AI agents. While large language models demonstrate impressive capabilities in isolated interactions, maintaining consistency across extended dialogues remains a critical challenge for AI engineers. Research shows that leading LLMs exhibit significantly lower performance in multi-turn conversations than single-turn

TL;DR Production AI agents face critical reliability challenges, with over 40% of projects expected to be canceled by 2027. The seven key warning signs include inconsistent response quality, frequent hallucinations, security vulnerabilities, performance degradation, poor tool orchestration, memory loss in extended sessions, and rising error rates. Each failure mode

TLDR: Debugging multi-agent systems requires specialized approaches beyond traditional single-agent methods. This guide covers five essential techniques: implementing comprehensive distributed tracing to capture complete execution flows, applying systematic failure classification using the MAST framework, leveraging span-level root cause analysis for granular debugging, enabling real-time production monitoring with intelligent alerts, and

TLDR LLM costs and latency are critical challenges for production AI applications. This guide presents five proven optimization strategies: (1) intelligent model routing to match query complexity with appropriate models, (2) prompt optimization for token efficiency, (3) semantic caching to reuse similar responses, (4) streaming responses to reduce perceived latency,