LLM Agent Performance Is a Distributed Systems Problem
FixBugs's team shows how token math, parallel chunking, and queues cut LLM agent latency: file analysis dropped from 10 minutes to 40 seconds.
Building FixBugs, an AI debugging agent that reads bug reports, logs, code and traces, the team found that making LLM agents fast requires thinking like a distributed-systems engineer. The piece separates time-to-first-token from overall throughput: input tokens hit the prefill phase and delay the first response, while output tokens are bound by decode speed — a distinction that matters because debugging agents tend to be output-heavy.
The biggest early bottleneck was asking one giant prompt which files were relevant to a bug, which took roughly 10 minutes for a 50-file repo. Splitting files into independent chunks and running 16 concurrent relevance calls cut that stage to about 40 seconds, essentially applying MapReduce to LLM workflows. The harder part, though, is the 'reduce' step — merging log snippets by time, span, or service — which is messier than simple chunking.
Streaming dramatically improves perceived latency (TTFT drops from 13s to 2.4s) but can reduce raw throughput (486 vs 244 tok/s), so FixBugs streams user-facing stages while optimizing background workers for total completion. The team also found concurrency has diminishing returns: throughput plateaus while individual TTFT worsens beyond a point. For multi-stage pipelines (analysis, reproduction, root cause, fix, validation), event queues that decouple stages proved far more resilient than one long synchronous request chain.