ALAKAZAM // AGENTIC
ESTIMATION ENGINE

Project Alakazam was born out of a critical inefficiency in modern product development: the overhead of manual ticket estimation. Drawing inspiration from the Agentic Kudan framework, the goal was to build a system that doesn't just predict numbers, but reasons through requirements.

The engine utilizes a multi-agent approach where specialized LLM nodes debate complexity, identify technical risks, and cross-reference historical velocity to generate highly accurate story point estimations.

Scaling multi-agent systems introduces unique challenges in state management and deterministic behavior. We implemented Domain-Driven Design (DDD) to strictly separate agent reasoning from core business logic, ensuring the system remains maintainable as it grows.

Maintaining a “High-Density” code quality was non-negotiable. We leveraged a modern Python toolchain with strict pre-commit hooks and type enforcement to prevent the typical decay found in fast-moving AI prototypes.