An agent is only an agent if it can take an action. We build LLM-powered systems that plan a sequence of steps, call the tools they need (your APIs, your databases, your services), and recover when something fails. Built with Claude or OpenAI as the reasoning layer, MCP for tool exposure, and a tight feedback loop so the agent can correct course mid-task.
What we won't do: ship a "chatbot" and call it an agent. Build something that looks impressive in a controlled demo and breaks the first time it touches production data. Hide the agent's decisions behind a black box — you should be able to read the chain of tool calls and understand exactly what happened.
Use cases we've shipped or are actively building: internal automation for repetitive support workflows, agentic data enrichment over CRMs, code generation agents wired to repo tools, and research agents that read documents and synthesize structured answers.
What you get
- Agents that complete real tasks, not just answer questions
- Tool calls exposed and inspectable — no black boxes
- Recovery loops that handle tool failures gracefully
- Works with your existing APIs and systems
- Model-agnostic: Claude, GPT, or open models
- Built for production load, not demos