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.

H Company is the leading agentic AI startup in Europe, based in Paris, specialized in computer use.

The AI landscape is shifting rapidly from passive text generation to active execution. The frontier belongs to Agentic AI, systems that don't just chat, but autonomously navigate digital environments to complete complex workflows.

At H Company, we bridge this gap between frontier research and production-grade deployment. We will explore how H Company built Holo3, our state-of-the-art model family designed specifically for "Computer Use" (GUI perception, planning, and OS navigation). Moving from theory to practice, we will demonstrate how these compact, high-efficiency models power our latest enterprise tools—including HoloTab, our autonomous AI browser companion. Finally, we will unpack the unified infrastructure model required to train, deploy, and scale these agents securely across multi-cloud environments.