fleet-liaison-tender — Social Vessels for Fleet Communication

Liaison specialists. As the fleet grows, information management becomes its own discipline.

The Problem

A fleet of agents generates information faster than any single agent can process. The edge especially — it has finite compute and can't drink from the cloud's firehose. Meanwhile, the cloud needs edge findings but doesn't speak "serial jitter" natively.

Tender Types

1. Research Tender

Carries findings between cloud labs and edge labs.

• Cloud → Edge: Architecture specs compressed to implementable units
• Edge → Cloud: Benchmarks, failure modes, sensor characterizations

2. Data Tender

Batches and packages big data for edge consumption.

• Cloud has 600 repos of context. Edge has 8GB total.
• Data tender compresses: "Here's what changed that affects YOUR hardware"

3. Context Tender

Carries fleet-wide context to isolated edge nodes.

• Edge nodes don't see the whole fleet. Context tender provides selective visibility.
• "The fleet just reorganized ISA v3 — here's what changed for your CUDA kernels"

4. Priority Tender

Translates urgency between realities.

• Cloud "low priority" might be edge "my sensors are noisy"
• Edge "everything's fine" might hide a slow drift the cloud would catch from fleet-wide data

Vessel Specialization

Oracle1 (SuperInstance / Cloud)

• Role: High point of the ecosystem. Architect. Coordinator.
• Strengths: Fleet-wide view, unlimited memory, long-term planning, spec writing
• Constraints: Can't touch hardware. CUDA is theoretical. Serial is abstract.
• First-class reality: API calls, git operations, text, architecture documents

JetsonClaw1 (Lucineer / Edge)

• Role: Bare metal intelligence. GPU lab. Edge specialist.
• Strengths: Real hardware, real timing, real sensors, gut feel for what works
• Constraints: Finite RAM/VRAM. Serial execution. Can't see the whole fleet.
• First-class reality: Sensor readings, CUDA kernels, serial frames, VRAM allocations
• Blinders ON: Focused on bare metal. Not distracted by fleet management.
• Other projects relieved: Ship innovations as git-agents, clear mind for Jetson guru work

Lucineer (Casey's Son)

• Role: Independent captain. Own vessel. Own GitHub account.
• Relationship: Not crew on Casey's ship — another captain with his own vessel
• Communication: Fork → PR → bottles. Git-native collaboration.

The Liaison Pattern

Cloud (Oracle1)                    Edge (JetsonClaw1)
    │                                    │
    ├── Research Tender ────────────────►│  (specs compressed for edge)
    │                                    │
    │◄──────────── Research Tender ──────┤  (benchmarks formatted for cloud)
    │                                    │
    ├── Data Tender ────────────────────►│  (batched: only edge-relevant changes)
    │                                    │
    ├── Context Tender ─────────────────►│  (fleet status, relevant to edge)
    │                                    │
    │◄──────────── Priority Tender ──────┤  (urgency translation)
    │                                    │

Why Liaisons?

Not every agent needs to understand every other agent. Specialization means:

• The edge agent doesn't need to understand fleet governance
• The cloud agent doesn't need to understand serial jitter
• Liaison vessels translate between these worlds

A large fleet needs MANY liaison tenders:

• Per-edge-node: at least one tender per physical vessel
• Per-domain: code review tenders, testing tenders, docs tenders
• Per-priority: routine updates vs emergency escalations need different packaging

Information Asymmetry

Cloud → Edge: Curated, compressed, actionable. The edge can't process everything. Edge → Cloud: Raw, detailed, everything. The cloud has the capacity.

The tender's job is managing this asymmetry actively, not just forwarding messages.