Decagon builds an autoresearch loop to optimize every inference endpoint
blelbach · x · 2026-10-29
Decagon shared how it approaches inference optimization: every endpoint presents a different optimization problem shaped by model architecture, traffic patterns, hardware, and latency constraints.
- The team built an "autoresearch loop" that tackles these problems automatically and carries lessons from one investigation into the next, so the system gets better at research as it accumulates evidence.
- Experimentation runs on Modal. Commenters note inference is becoming increasingly bespoke — continuously optimized per model×hardware×workload — with agents making that level of specialization economically viable.
Related event: Decagon Builds Self-Improving Auto-Research Loop for Inference Optimization(2 posts)→
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