am. Andrea Migliavacca
Andrea Migliavacca / PortfolioAI ENGINEERING · SOFTWARE

My interest
in AI.

I want to understand how AI applications work, especially the architecture around the model: how it uses documents, how agents coordinate and how answers are verified. In my projects I experiment with these choices and document their results and limits.

Computer Engineering student · Politecnico di Milano

01Projects

Problem, contribution and result.

Inbox Triage lab: a lead classification and actions awaiting approval
AI engineering · Go Fig take-home · online demo

Inbox Triage

Automatically sorts a shared inbox, selects actions and drafts replies. Uncertain cases go to a person; sending and external writes require confirmation.

System, design and demo →
Decision lab comparing choices in the RAG project
RAG · Agent Research · WikiLLM

RAG — design decisions

I measured retrieval, reranking and generation choices on financial questions. A decision lab compares quality, time and cost; the page also introduces agentic search and advance synthesis.

Decisions and results →
80.5%
Agents · experimental evaluation

τ²-bench

Six prompt versions for a support agent, with the model held fixed: an 80.5% average across four runs, compared with 68% for the default agent in one run. I examined errors in Langfuse traces and reported a benchmark defect.

Method and results →
Screenshot of the online Mesos board game's GUI
Java · team · 30/30

Mesos online

In a four-person team, I built the Controller and Socket/RMI server paths and contributed to the model UML. Different types of clients can join the same match.

The product and my role →
Illustration of routes on a hexagonal map
C · individual project · 30/30 with honours

Shortest paths

Movhex: minimum costs on a changing hexagonal map. Computed land neighbours save memory; Dijkstra with a min-heap and stopping at the destination limit search work. Individual C project, 30/30 with honours.

Problem and solution →
Review interface: request, quote and automated verdict above, reviewer judgment below
Agents · human review · online demo

Quote review interface connected to Langfuse

An agent prepares quotes and messages, an automated judge evaluates them, and a nontechnical reviewer checks them before sending. The human and automated judgments appear together in Langfuse.

How it works and demo →

02Skills

Python · FastAPI · LangGraph · PostgreSQL · Azure

AI applications

Document retrieval, model decisions and human approval, with explicit validation rules.

See the workflow ↗
pytest · Langfuse · LangSmith · CI

Testing and observability

Repeatable experiments, automated checks and traces to investigate errors.

See the evaluation ↗
Java · C · UML · design pattern

Software engineering

Server design, modular interfaces and algorithms mindful of time and memory.

See the team project ↗
How I design and evaluate agents →

Contact

I'm looking for opportunities in AI engineering.

Interested in graduate roles and internships involving agents, evaluation and AI applications.