How Together AI Uses AI Agents to Automate Complex Engineering Tasks: Lessons from Developing Efficient LLM Inference Systems

Building AI agents to automate complex and long-running engineering tasks demands a distinctive approach compared to typical AI applications, as evidenced by a case study on using agents to enhance LLM inference through speculative decoding. The blog outlines key patterns for developing effective agents, emphasizing the importance of infrastructure and behavioral patterns. Infrastructure patterns focus on creating a stable environment with well-abstracted tools and comprehensive documentation, while behavioral patterns guide agents on managing tasks like parallel sessions, wait times, and progress monitoring. A real-world example illustrates the automation of a speculator training pipeline, highlighting the reduction of manual oversight and increased efficiency. Despite challenges such as context management, novel failure modes, and resource optimization, the potential for expanding AI agent usage into various domains remains vast, promising increased reliability and enhanced human-LLM collaboration.

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