AGNTCon + MCPCon Europe 2026

I've spent the past year tuning open-source inference stacks (vLLM, SGLang) for production agent systems. The same failure modes keep appearing, and they all live in the serving layer.

Biggest one: KV cache thrashing. Agents rebuild thousands of tokens of system prompt + tool schemas every turn. Prefix-aware caching fixes this in six lines of config. I've measured 55-65% latency reduction on turn 2+ across twelve deployments. Almost nobody enables it.

Second: batch-of-one paralysis during tool-calling loops. Each LLM call is a single request, GPU 80%+ idle. Disaggregated prefill/decode with continuous batching unlocks 3-5x throughput. Requires ~40 lines of change in most agent frameworks.

Third: agent latency is bimodal (short tool-selection turns vs long reasoning turns). A single timeout threshold wastes GPUs or kills valid turns. Two-tier serving handles both.

Open configs. Real numbers from workloads I instrumented. If you're building agents and haven't looked below the orchestrator, this fills the gap.