An agent that turns a business scope into a deployed service
A production R&D system that takes a business scope and produces a deployed backend — generating agent graphs, tool configs, and an integration-ready API surface.
Challenge
Standing up a new AI backend is mostly undifferentiated work: wiring an agent graph, defining tools, exposing an API, and making the whole thing reproducible. Doing it by hand is slow and inconsistent, and the result is rarely observable enough to operate. The goal was a system that could take a business scope and produce a deployable service — without sacrificing structure or control.
Approach
We built an autonomous “AI Architect” that treats backend generation as a structured planning problem rather than a freeform prompt. The system plans in explicit steps, calls typed tools to assemble the service, and executes safely with reproducible outputs at every stage. Nothing happens that can’t be traced and repeated.
System design
- Structured planning that decomposes a scope into an agent graph and tool set
- Typed tool-calling layer with sandboxed, reproducible execution
- Code and configuration generation targeting a FastAPI service
- An integration-ready API surface produced as a first-class output
What we delivered
- An end-to-end system that converts a business scope into a deployed backend
- Generated agent graphs and tool configurations, versioned and reproducible
- A clean, integration-ready API surface for downstream teams
- Traceability across planning, generation, and execution
Why it mattered
The work compresses the path from idea to a running, integration-ready service while keeping the output structured and reproducible. It is a concrete example of treating agents as engineering: explicit control flow, typed tools, and safe execution — not a clever prompt hoping to hold together.
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