We built an intent middleware for AI agents — early pilots showing 30% fewer failures on multi-step workflows

Hey all, building XeroML, and wanted to share what we've been working on.

The problem: AI agents drift. They start strong but by step 10-15 in a complex workflow, they lose track of the original goal. Outputs go off-rails, you burn tokens retrying, and reliability tanks.

What we built: An intent layer that sits between the user and the agent. Before the agent acts, XeroML infers and structures the user's intent — then keeps the agent goal-aware at every step. Think of it as a persistent "north star" the agent checks against throughout execution.

How it works:

• Model-agnostic — works with any LLM
• API + MCP server ready
• Plugs in within minutes, no major refactor needed

Early results: In our pilots, we're seeing ~30% improvement over base models on multi-step task completion. Fewer failures, less drift, more predictable outputs.

We're opening 3 pilot spots — free integration, no strings. We just want teams building real agents to stress-test this and give us honest feedback.

If you're working on AI agents or dev infra and want to try it: xeroml.com or DM me.

Happy to answer any technical questions here.