AI BriefingDecagon's AutoResearch Agent Automates Inference Stack Optimization, Achieving Up to 2.5x Throughput Gains
·2026.10.29 03:28
Key point
The system uses high-fidelity production replay and parallel sub-agents to diagnose bottlenecks and test speculative decoding, quantization, and scheduling changes.
Details
- Decagon introduced AutoResearch, an agent system that automates the diagnosis, experimentation, and measurement of inference stack bottlenecks to improve performance.
- The system achieved throughput gains of up to 2.50x requests/s/GPU using FP8 weight quantization combined with NGRAM speculative decoding, with other optimizations ranging from 1.05x to 1.99x.
- It employs high-fidelity replay environments that reconstruct production traffic (removing PII) to measure latency-throughput curves and validate improvements against a noise floor.
- Parallel sub-agents investigate competing hypotheses (e.g., scheduling, speculative decoding, kernel optimization) while a shared evaluation harness determines if results are Better, Same, Worse, or Invalid.
- A shared memory system stores version-controlled findings to prevent repeated work and transfer insights across different model endpoints and hardware configurations.
- Future iterations aim to shift from human-initiated to agent-initiated optimization, triggered by model updates or workload shifts, contributing to Decagon's Intelligence Factory.
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