Enzo Lombardi's latest article in the 'Building AI Agents in Rust' series addresses a common scalability pain point: as the number of agent skills grows, the traditional match-statement dispatcher becomes a tangled mess of clones, retries, and error handling. The piece proposes a structured alternative built on Rust's trait system.
At its core, the new approach defines each skill as its own trait implementation, complete with associated input types, runtime metadata (read-only vs destructive, concurrency safety), and standardized error handling. This allows the system to reason intelligently about failures. A central registry takes over dispatch duties, automatically generating JSON Schema from Rust structs to keep the model and code in perfect sync. The registry also enables safe parallel execution of multiple tool calls.
Beyond the dispatcher redesign, the article covers resilience techniques like retry helpers for transient errors and cross-cutting behavior via pre/post hooks—enabling permissions checks, logging, sensitive data redaction, and caching. Scaling strategies are addressed through search hints, aliases, and on-demand loading for large tool sets.
Lombardi distills three guiding rules: atomic skills, result-based outputs, and safety-first input handling. The article concludes with a demonstration showing how these principles lead to a cleaner, more maintainable, and production-ready agent loop.
For Rust developers building AI agents, this series provides a practical path from prototype to robust system. The focus on leveraging Rust's type system for architectural clarity is a notable contribution to the emerging field of agentic AI development.