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.
Every decision is auditable: you can read the chain of tool calls and see exactly what the agent did and why. The system is built for production load — the same shape works whether it's running once a day or once a second — and tested against real data on a real workflow before it goes live.
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, multi-step tasks end to end
- Tool calls exposed and inspectable from first to last step
- 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 with real-world failure modes