Omni by xpander

You tell Omni what process you want handled, and it turns that into an agent project. It picks the connectors, writes the instructions, sets up memory, and runs the agent through a review before launch, handling the wiring that would normally take an engineer a few days of reading API docs. No infrastructure. No setup project on your side. The whole thing starts as one sentence in a chat window.

Omni reads past tasks and traces to see where an agent succeeded or failed, then rewrites its instructions to improve it, which means the agent that handled your tickets last month is measurably better at them this month without you touching a single setting. That means the agent gets better as it runs, instead of drifting once nobody's watching. Most enterprise AI agents never get this second pass.

When an API or connector shifts, Omni finds every affected agent and updates them before they fail quietly, which saves you from discovering a broken pipeline days later when a report comes back empty. This matters on integration-heavy setups, where one changed endpoint can quietly break a whole chain of agents that depend on it.

Agents built locally in Claude or Codex can be brought onto xpander and made to run consistently in the cloud. The work moves with its skills and MCP connectors, so a teammate's machine isn't the only place it exists. This is AI agent deployment without a migration project. No rebuilding from scratch.

Omni runs the same task across frontier and open models so you can see which performs best, then switch without rebuilding the agent, which means a model upgrade becomes a setting you change rather than a project you plan. Model choice becomes a test you run, not a guess you commit to. Pick one, run it, compare the traces.

Agents move from dev to prod with the config differences handled safely, and each one gets documented. Combined with per-user permissions and audit trails on the platform underneath, this is what makes the jump to production manageable for IT teams that need to sign off on what runs. It's the boring part. It's also the part that decides whether agents stay in production or die in a prototype folder.

Agents reach into the systems your business already uses, whether that's Slack, Teams, GitHub, or an internal API. MCP connectors and API calls run through a governed path instead of a workaround, so credentials stay out of the model's context. That separation is the point. Security teams care about it more than any feature list.