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AI Agents
AI Agents that hold up in production
From “it kinda works” to “it reliably ships value”: AI agents built for production.
What an agent actually is
Context, tools & workflows
An agent is usually an LLM with three things: context (what it knows), tools (what it can do), and a workflow (in what order). That's it.The difference from a chatbot: an agent can act. It doesn't just reply, it writes to your database, opens a ticket, books a meeting. And it does it without you needing to be in the room. (We do keep a human in the loop where necessary, of course.)
What we actually build
Production-Grade Agents
We’re building agents in Mastra. A a code-first framework for production-grade agents. Not no-code workflows. Real architecture, version-controlled, deployed on infrastructure that scales. Internally, we're using agents to:
- Connect Notion, Slack, Linear, and GitHub so context stops living in silos
- Prep client issues for developers without manual handoff
- Automates the boring parts in our salesflow.
The part some people skip
Quality, evals & observability
Building the agent is the ‘easy’ part. Knowing whether it's working at full capacity is harder. First we set up evals, structured tests that measure output quality, so you know when your agent is producing good results and when it isn't.
We build in observability when we go live so you can see what broke, when it happened and why it did. We make sure everything is repeatable at scale, not just in a demo. An agent you can't measure is just automation you trust blindly.
We're building agents for ourselves and for our clients.