Gianluca Mazza — AI Systems Engineering

Designing production AI systems with agents, tools, memory, retrieval, evals, durable runtime state, and recoverable execution paths.

Systems

Multi-agent runtime: delegation, coordination, state, and typed execution.
Memory layer: episodic memory, retrieval, compression, persistence, and replay.
Workflow engine: durable execution, retries, checkpoints, and human gates.
Evaluation systems: regression suites, trace review, cost tracking, and evidence.

Projects

emotional-memory — Research-grade memory layer for LLM systems. Python, PyPI, DOI 10.5281/zenodo.19640250, MIT.
orka — Agent orchestration in Rust. MCP/A2A protocols, multi-channel routing (Telegram/Discord/Slack/WhatsApp/HTTP), WASM plugins.
msg2agent — Agent-to-agent communication. X25519/Ed25519 E2E encryption, W3C DID identity, relay hub with P2P fallback. Go.
OpenFatture — Electronic invoicing (FatturaPA/SDI) + Lightning Network payments + local-first AI. Python, v1.1.0, MIT.

FAQ

How do you scope a new technical engagement?

I start with a written brief: problem, current stack, known failure modes, and success criteria. If scope is unclear, I propose a short discovery sprint with a working prototype and an eval plan.

How do you take LangGraph workflows to production and prevent regressions?

Production LangGraph needs durable state, explicit routing, typed tool failures, retries, and trace-level observability. I ship it with an eval harness that covers the failure modes that matter.

How do you build RAG systems that work in production?

I start with diagnostics, not a new vector database. Chunking, hybrid retrieval, re-ranking, metadata filters, and a frozen eval set usually matter more than changing embeddings.

Contact

info@gianlucamazza.it

github.com/gianlucamazza

linkedin.com/in/gianlucamazza

Infrastruttura Retrieval

Ricerca ibrida, pipeline embeddings, strategia metadati, reranking e valutazione per sistemi RAG in produzione.

Design della pipeline

Il retrieval in produzione è una pipeline: ingestion, chunking, metadati, embeddings, ricerca lessicale, ricerca vettoriale, reranking e valutazione della risposta.

Diagnostica

Prima di cambiare modelli, ispezionare miss, chunk ambigui, documenti obsoleti, filtri e intento query.

Infrastruttura Retrieval

Pipeline embeddings, ricerca ibrida, filtri metadati, reranking ed eval set stabili per sistemi RAG.

Sistemi di Valutazione

Suite di regressione, casi avversariali, review trace, tracking costi ed evidenze di production-readiness.