Impact Engine — System

Science that stays in notebooks doesn't scale. The architecture behind Impact Engine turns methodology into infrastructure — independently deployable, config-driven, and built through agent-driven development.

How is it wired? — Pipeline Design

Pipeline flow showing parallel fan-out per initiative, fan-in at Allocate, and fan-out for selected initiatives

Fan out per initiative, fan in at the decision boundary, fan back out for the winners.

Initiatives are independent until the allocation decision. The pipeline exploits that — measuring and scoring in parallel, then synchronizing only where a joint decision requires it. One YAML config connects four independent packages into a single run: fan out per initiative, synchronize at the allocation boundary, fan back out for scaled deployment. Components communicate through plain dict boundaries, so each package is independently deployable and testable without the full pipeline.

How does it extend? — Extension Points

Adding a new causal model, decision rule, or scoring function requires no changes to the package itself. Each component exports its registry and interface — register an adapter from your own code, and the pipeline picks it up. One new class, zero pipeline changes, no fork required.

Measure component extension groups — Data Sources, Impact Engine core, Model Adapters, Data Storage — each with concrete implementations and a Custom slot

The Measure component illustrates the pattern: four registries, each with a Custom slot. Evaluate and Allocate follow the same structure.

How is it built? — Agentic Support

A system that requires its builder to enforce every convention creates a dependency. The entire ecosystem is built through agent-driven development — agents read the codebase conventions before writing a single line, operate within explicit interface contracts, and validate every change against the same lint, tests, and pre-commit hooks a human would face. Every repo carries a hierarchical CLAUDE.md with dependency graph, naming rules, and interface contracts, read as runtime instructions before any file is touched. Whether a human or an AI made the change, the pipeline can't tell — and that's the point.


Further Reading

Software Design

R.C. Martin — Clean Code: A Handbook of Agile Software Craftsmanship (2008)

J.K. Ousterhout — A Philosophy of Software Design (2nd ed., 2021)

Agentic Support

Anthropic — Building Effective Agents (Anthropic Blog, 2024)

R. Jansen — Agent-Driven Development: The Next Paradigm Shift in Software Engineering (DEV Community, 2025)

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