Inference for the agentic workforce.
Why model serving has to be rebuilt around long-running AI agents.
Agents are not chat users.
Chat asks a question, waits for an answer, and ends. An agent takes a goal, runs for hours, calls tools, checks its work, and keeps moving while nobody is watching.
That difference changes the traffic. Context grows. Streams stay open. A single run can cross models and protocols while making thousands of inference calls.
Serving built for short conversations does not become agent inference just because an agent is pointed at it.
Chat-shaped serving breaks under agent load.
A fast first token is useful. It is not enough. Agents need streams that stay alive, tool calls that keep their shape, and predictable serving deep into a long trajectory.
They also need model choice without integration work. The best model for planning may not be the best one for code, vision, or a cheap repetitive turn.
The inference layer has to absorb that complexity so the agent loop does not.
We rebuilt inference around the loop.
Layer X1 is direct model inference for AI agents: every flagship model behind one endpoint, reachable with one key through the SDK dialect an agent already speaks.
The engine is shaped for runs that last hours rather than questions that last seconds. It measures quality, routes around degradation, and keeps the stream in the agent’s hand moving.
The machinery is ours. The simple endpoint is yours.
“Agent inference is not chat at scale.”
Every model should sit behind one door.
Changing models should be a string change, not a migration. New model families should appear behind the endpoint an agent already uses, under the key and bill a team already understands.
OpenAI-compatible and Anthropic-compatible traffic belong at the same door. The harness should not have to care which provider sits behind a turn.
Choice belongs in the model call, not in a new contract, SDK, credential, and integration every time.
Inference should disappear into the work.
The best inference layer is the one an agent does not have to think about. It stays available, keeps protocol promises, and serves the right model without interrupting the run.
Teams should spend their time deciding what an agent can do, not babysitting provider adapters and fallback lists.
That is the Layer X1 product: inference for AI agents, built for the loop rather than the demo.
Your agent stays the same.
Its inference gets better.
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