Building Faster, Better AI Agent Systems
Reliable AI systems need continuous production feedback, rigorous evaluation and workload-specific inference optimization.
Summary
Agent quality improves when production traces, offline datasets and evaluations form a continuous loop, with humans setting goals and reviewing data while AI proposes fixes and maintains parts of the process. Evaluation platforms must support both pre-production testing and post-production observability, including large semi-structured traces that turn failures into new test cases. Speculative decoding improves decode speed without changing output quality, but its value depends on spare VRAM, concurrency, acceptance rates and the balance between prompt length and generated output. Langfuse and Braintrust emphasize feedback infrastructure, while the vLLM profiling shows how model-serving architecture determines practical latency gains.