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We map the workflow you want to automate step by step, including the systems it touches and the decisions it requires. Together we define what success looks like and which steps must stay under human control.
We choose between a single agent and a multi-agent design, and select models by comparing accuracy, latency, and cost on your actual tasks. Tool interfaces, memory strategy, and guardrails are specified before development starts.
We build the agent with typed tool integrations, structured outputs, and recovery logic for failed steps. Sensitive actions are wired through approval gates so a human signs off before anything irreversible happens.
We test the agent against a benchmark set of real cases and measure task completion, accuracy, and cost per run. Edge cases that break the agent become regression tests before launch.
The agent first runs in shadow mode or on a limited scope while your team reviews its decisions. We expand its autonomy gradually as the metrics support it.
In production, every run is logged with full traces, token costs, and outcomes. We review failures, refine prompts and tools, and feed learnings back so the agent improves month over month.
We believe in radical transparency. You'll always know where your project stands and what comes next.
Progress reports every week
Communicate with your team
Clear deliverable checkpoints
Complete technical handoff
Let's begin with a conversation about your project goals.