High-performance agents on a horizontally-scalable control plane.

Describe the worker, not the workflow. An agent is a reviewable directory: its model, its boundaries, and its skills, procedures it loads only when the moment calls for them. All of it diffs, signs, and rolls back like any other code you ship.

Agents never hardcode a vendor. A curated registry abstracts every provider behind one interface, sets a fleet-wide default, and lets any agent override it, from frontier models to open weights on your own hardware. Swapping is one line of configuration.

The agent ecosystem is converging on one protocol, and AXL speaks it in every direction: consume any server, be a server, and render tool results as living interfaces. Connect once, and every future MCP tool is already compatible.

Workflows are authored in AXG, a small but powerful language built for orchestrating agents. Cost controls, retries, parallelism, multi-agent handoff and durability are built into the core.

Software front doors are becoming conversations. One agent, one memory, and one set of controls, present in every room your users already occupy, including a phone call. Connecting a channel is configuration, and the agent is in the room.

Recurring work is configuration: the morning brief, the nightly ingest, the weekly report. Schedules fire exactly once across the whole fleet, no matter how many instances are running, and the run history is there when you ask what happened.

Agents that cite their sources, and sources you can prove. Every ingested chunk carries its hash and signature, so an answer traces to a trusted author or does not get made. Ingest document sets, version them, and search them at question time.

The tenth conversation should not feel like the first. Preferences, decisions, and facts follow the person, scoped deliberately: what an agent learns mid-task never leaks past it, and what a user teaches it lasts.

Agents that receive real files and hand real files back. Attachments flow in from chat or the API, artifacts come out as downloadable results, and both live in durable storage you control.

Middleware wraps the agent loop in layers you choose: response caches, write caps, approval gates, model routing, dry runs. Policy lives in the platform, so it holds even when the model improvises.

The shield scrubs secrets and personal information before they reach the model and fences untrusted content on the way in. Stored history holds placeholders, people with access see the restored text, and critical secrets block the response outright.

Point agents at your repositories and let them build. Each task runs in an isolated code sandbox on the cloud provider of your choice, with the test suite as the definition of done. Agents write, test, and iterate until everything passes, then hand you a pull request. Software that writes software, with a person holding the merge button.

The next integration surface is other people agents. Publish yours with a discovery card, call theirs the same way, and let the open protocols make the introductions. Interop is configuration on the agents you already run.

Every run leaves traces, tool audit entries, latency, and cost. The diagnostics console shows what a deployment is doing right now, and schedules run recurring work across the fleet without firing twice.

The diagnostics console is a live view of a running deployment: every agent, every run, every stream, updating as it happens. When something goes wrong, you follow it from symptom to cause instead of reconstructing it from logs.

Everything AXL does is a command away: mint credentials, chat with agents, ingest documents, submit workflows, and watch a run as it executes. Profiles switch between deployments, and it all scripts, so your pipeline drives the same surface you do.