Multi-Agent Systems | Stefano Blando

Multi-Agent Orchestration

Wed, 18 Mar 2026 00:00:00 +0000

This project can be read as a system for real-time economic coordination under changing phases, partial information, and hard timing constraints.

The system is structured as an event-driven multi-agent orchestration layer. It listens to the game through SSE events, keeps a runtime representation of the environment, and switches strategy depending on the current phase of play: policy update, procurement auction, inventory reconciliation, and fulfillment.

What makes the project interesting is not just the use of multiple agents, but the way orchestration is tied to operational robustness. Different capability sets are activated at different stages, while local persistence, metrics, and replay analysis help make decisions more stable under noisy runtime conditions.

In practice, it is a good example of a project where agentic design had to be grounded in execution reliability rather than in generic prompting alone. The deeper pattern is auction-based coordination with inventory constraints and demand handling.

Interactive Explorer

The interactive app focuses on the engineering core of the project: the live event loop, shared runtime state, stylized agent interaction, phase-specific agents, capability partitioning, bounded memory, and KPI replay.

It is intentionally presented as a runtime architecture explorer, not as a fake benchmark dashboard. The goal is to make the orchestration logic legible in the same visual language used for the Island Model and PFSE apps, with emphasis on coordination, auctions, and fulfillment.

RiskSentinel - Agentic Systemic Risk Simulator

Sun, 15 Mar 2026 00:00:00 +0000

RiskSentinel is an agentic systemic risk simulator built around a question I find both practical and conceptually interesting: if a large financial node is hit by a shock, how can we make the propagation of that stress visible, comparable, and explainable in real time?

Built for the Microsoft AI Dev Days Hackathon 2026, the project combines three contagion models, topology-aware analytics, and a bounded multi-agent workflow on top of research-grade market network data. The purpose is not only to simulate cascades, but to make them easier to inspect through natural-language interaction, interactive visualization, and model comparison.

What makes the project work is the combination of several layers that are often separated: a proper network simulation engine, an interface that makes the results explorable, and an agentic layer that helps interpret what is happening without pretending to replace the underlying quantitative machinery.

The result is a practical prototype for systemic risk monitoring, sitting between quantitative finance, complex systems, and AI-assisted decision support.

You can explore the project here:

RiskSentinel for Microsoft AI Dev Days 2026

Sun, 15 Mar 2026 00:00:00 +0000

RiskSentinel is the project I built for the Microsoft AI Dev Days Hackathon 2026.

The core idea was to create a system that could make systemic risk more explorable and more tangible: instead of treating contagion as an abstract output in a paper or a notebook, I wanted an interface where shocks could be launched, propagated, compared, and interpreted in real time.

The project combines network science, contagion modeling, and agentic AI on top of research-grade financial network data covering 210 S&P 500 stocks and 3,081 daily snapshots. Under the hood, it integrates three propagation models, interactive network analytics with Streamlit and Plotly, and an agentic workflow built with Microsoft Agent Framework and Azure OpenAI.

What I like most about this project is that it sits exactly at the boundary between my research interests and practical prototyping: financial networks, complex systems, decision support, and AI agents all in the same tool.

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GitHub:
App: