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LLM inference for AI agents
AI agents are among the most demanding inference workloads: they make many sequential model calls, carry long context, and often run concurrently for many users. That makes time-to-first-token, streaming speed, long-context handling, and behavior under concurrency decisive. Netra Runtime serves these workloads through an OpenAI-compatible API and, in a published long-context benchmark, streamed tokens about 5.5x faster than vLLM under load.
Why agents stress inference
An agent rarely makes a single model call. It plans, calls tools, reads results, and decides what to do next — often dozens of calls per task. Latency at each step compounds across the loop, the context keeps growing as the agent accumulates documents, tool outputs, and history, and many agents typically run at the same time. That combination of long context and high concurrency is the hardest case for an inference engine.
What agentic serving needs
| Need | Why it matters for agents |
|---|---|
| Low TTFT | Each step waits on the first token; the delay compounds across a loop. |
| Fast streaming | Faster per-token output shortens every step of the agent. |
| Long context | Agents carry documents, tool results, and conversation history. |
| Concurrency | Many agents and users run at the same time. |
| OpenAI-compatible API | Drop into existing agent frameworks without rewrites. |
How Netra fits
Netra Runtime exposes an OpenAI-compatible API, so agent frameworks and SDKs that already target OpenAI can use Netra by changing the base URL and key. Its published benchmark used a 32K-in, 16K-out long-context workload — the shape of agentic, document-analysis, and coding tasks — and Netra led the field from 1 to 512 concurrent users, streaming about 5.5x faster than vLLM and finishing full answers roughly 3.4x sooner at load.
Getting started
To serve an agent on Netra Runtime, point your existing OpenAI-style client at Netra's base URL and key, then deploy. Learn what Netra Runtime is or see pricing.
Frequently asked questions
What makes AI agent workloads hard for inference engines?
Agents make many sequential model calls with long, growing context, so latency compounds and memory pressure rises. Engines must keep time-to-first-token and per-token latency low even as context length and concurrency increase.
Can I use Netra Runtime with my existing agent framework?
Yes. Netra Runtime exposes an OpenAI-compatible API, so agent frameworks and SDKs that target OpenAI can use Netra by changing the base URL and API key.
Does Netra Runtime handle long-context agent workloads?
Yes. Netra's published benchmark used a 32K-token input and 16K-token output — the shape of agentic and document-analysis workloads — and Netra led on throughput and latency from 1 to 512 concurrent users.