Baymax

When a heatwave strikes, a disease outbreak surges, or a mass casualty event unfolds, hospitals are suddenly competing for the same scarce supplies, and the phone-call coordination that's supposed to fix it is far too slow.

Baymax is a network of hospital AI agents built on Fetch.ai that sees crises coming by fusing live weather, disease, and inventory signals. Using Claude for reasoning, it autonomously negotiates and settles inter-facility transfers on-chain before shortages peak. Every decision is traced through Arize Phoenix, allowing the system to learn from each event and improve future responses.

Baymax doesn't just respond to emergencies - it learns to anticipate them.

At a Glance

Baymax predicts shortages before they happen, negotiates inventory transfers between hospitals, settles them on-chain, and learns from every crisis it handles.

Inspiration

The supply crisis starts long before a hospital runs out.

It starts hours earlier, when a weather forecast converges with rising illness signals and a facility that's already running lean. By the time a supply manager notices the pattern—let alone makes phone calls to neighboring facilities, negotiates terms, and arranges a transfer—the shortage has already peaked.

The perception problem (counting what's on a shelf) is largely solved. The harder problem is autonomous coordination under surge conditions: seeing a crisis before it happens, negotiating transfers across institutions under real constraints, settling them quickly, and improving the system after every event.

That's a multi-agent systems problem.

Fetch.ai provides the agent network. Claude provides the reasoning layer. Arize Phoenix closes the learning loop. Baymax is what happens when all three work together.

What it Does

Baymax runs one agent per hospital. Each agent continuously combines:

Live inventory from edge vision systems
Weather forecasts and disease-burden signals
Historical surge patterns and outcomes

When a shortage is predicted:

Claude detects a surge risk before inventory reaches critical levels.
The requesting hospital broadcasts a transfer request through the Fetch.ai agent network.
Nearby hospitals respond with constrained offers.
Claude evaluates quantity, urgency, ETA, distance, and expiry dates.
If no single offer satisfies demand, Baymax automatically creates a split transfer plan.
The transfer is settled through the Fetch Payment Protocol on Dorado testnet.
Arize Phoenix traces the entire decision process and outcome.

The result is a system that can identify shortages earlier, coordinate faster, and continuously improve over time.

How We Built It

Baymax has two user-facing surfaces:

ASI:One for natural-language interaction and approvals
A live Flask dashboard for real-time monitoring and visualization

Both are powered by the same Fetch.ai agent network and synchronized through Redis.

Core Stack

Component	Technology
Agent Network	Fetch.ai uAgents
Agent Discovery & Chat	Agentverse + ASI:One
Settlement	Fetch Payment Protocol
Reasoning	Claude Haiku + Claude Sonnet
Vision	Claude Vision
State Management	Redis Stack
Forecasting Signals	Open-Meteo + disease.sh
Observability	Arize Phoenix
Dashboard	Flask

Agent Network

Agent	Role
`baymax_front`	Requester, ASI:One interface, settlement coordinator
`baymax_hospital_b`	Surplus inventory provider
`baymax_hospital_c`	Secondary surplus inventory provider

Negotiation Flow

Signal Detection
    ↓
Shortfall Predicted
    ↓
Request Broadcast
    ↓
Offer Collection
    ↓
Offer Ranking
    ↓
Transfer Planning
    ↓
On-Chain Settlement
    ↓
Outcome Logging

Challenges We Ran Into

ASI:One Echo Loops

ASI:One occasionally interpreted our own agent narration as new user intent. We built filtering, cooldowns, and heuristics to prevent agents from responding to themselves.

Payment Card Rendering

The Fetch payment card depended on very specific metadata fields. Missing metadata caused silent failures with no visible error messages.

Python 3.14 Event Loop Changes

uAgents still relied on behavior removed in Python 3.14. We had to carefully manage initialization order to ensure a valid event loop existed before agent creation.

Long-Running On-Chain Verification

Transaction verification could stall agent execution for up to 20 seconds. We moved verification into background threads using asyncio.to_thread().

Accomplishments We're Proud Of

Live End-to-End Settlement

We successfully demonstrated:

Natural-language intent → surge prediction → multi-hospital negotiation → on-chain payment → confirmed transfer

Real Predictive Signals

Baymax combines:

Live weather data
Real illness data
Current inventory levels

to generate structured shortage forecasts before a shortage is declared.

Constraint-Aware Negotiation

Baymax doesn't simply match one requester to one provider. It can automatically compose split transfers when multiple facilities are required to satisfy demand.

Full Decision Traceability

Every prediction, ranking decision, transfer proposal, and outcome is captured in Arize Phoenix.

Parallel Development

The forecasting, vision, dashboard, observability, and agent teams all built independently against a shared Redis schema and integrated successfully.

What We Learned

The defensible technology isn't the camera.

The real value comes from creating a network that can detect, negotiate, and settle across institutional boundaries before a crisis peaks.

We also learned that observability is essential for autonomous systems. The ability to trace every decision made by the system transforms Baymax from a coordination tool into a platform that can improve with experience.

Finally, we learned that fail-closed design makes live demos possible. Every major dependency has deterministic fallbacks, allowing the system to remain reliable even when external services are unavailable.

What's Next

Predictive Pre-Positioning

Move inventory before shortages occur when confidence in an incoming surge becomes sufficiently high.

Disaster Response Coordination

Extend the network to support regional responses for mass-casualty incidents and natural disasters.

Ambulance & Patient Routing

Apply the same negotiation framework to patient transfers and hospital capacity balancing.

Blood & Biologics Logistics

Support temperature-sensitive and blood-type-constrained transfers.

Cross-System Federation

Allow independent health systems to participate in a regional mutual-aid network without exposing internal inventory systems.

Supplier-Side Agents

Bring distributors into the negotiation process so Baymax can choose the globally optimal resolution between purchasing and transfer options.

Baymax sits on top of existing hospital workflows. It doesn't replace clinical decision-making—it helps ensure critical supplies arrive where they're needed before shortages impact patient care.

## Built with

fetch.ai · uagents · agentverse · asi:one · chat-protocol · payment-protocol · claude · claude-code · anthropic · redis · arize-phoenix · opencv · open-meteo · disease.sh · flask · python