scbe-fleet-deploy

name	scbe-fleet-deploy
description	Deploy trained models across the AI fleet — federated artifact fusion, quality gate checks, version promotion, multi-cloud orchestration, and rollback. Use when deploying models, promoting artifacts, checking deployment health, or managing the fleet release cycle.

SCBE Fleet Deploy

Manage the deployment lifecycle from trained artifacts to running fleet agents.

Deployment Pipeline

Trained Artifacts (HF, GCP, AWS)
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    v
Federated Fusion (training/federated_orchestrator.py)
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    v
Quality Gates (safety >= 0.95, quality >= 0.80)
    |
    v
Version Promotion (staging -> production)
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    v
Fleet Rollout (rolling update across agents)
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    v
Health Monitoring (coherence, latency, safety scores)

Operations

1. Collect Artifacts

Gather training outputs from all providers:

# Each provider produces a manifest JSON
# HF: textgen models (LoRA adapters, full weights)
# GCP: embedding models (Vertex AI)
# AWS: runtime models (SageMaker endpoints)

ls training/manifests/
# hf_manifest.json  gcp_manifest.json  aws_manifest.json

2. Run Federated Fusion

python training/federated_orchestrator.py \
    --manifests training/manifests/*.json \
    --output training/fused_release.json \
    --min-quality 0.80 \
    --min-safety 0.95

3. Quality Gate Check

Every artifact must pass all gates before promotion:

Gate	Threshold	Metric
Quality	>= 0.80	Task accuracy / BLEU / F1
Safety	>= 0.95	Governance compliance rate
Latency	<= 200ms p95	Inference latency
Cost	<= $1.00/1K tokens	Compute cost

Failed artifacts are rejected with detailed failure reports.

4. Version Promotion

staging -> canary (10% traffic) -> production (100%)

Each stage requires:

• Governance vote (BFT consensus from validator agents)
• Safety re-check at new scale
• Rollback plan documented

5. Fleet Rollout

Rolling update across the flock:

1. Select first batch (1 agent per specialty)
2. Deploy new model version
3. Monitor for 5 minutes (coherence, error rate)
4. If healthy: continue to next batch
5. If degraded: auto-rollback to previous version

6. Rollback

# Immediate rollback to last known good
python training/federated_orchestrator.py \
    --rollback \
    --target-version v2.1.0

7. Health Monitoring Post-Deploy

Track these metrics after deployment:

# Per-agent metrics
agent.coherence       # Should stay > 0.7
agent.error_rate      # Should stay < 0.05
agent.response_time   # Should stay < target

# Fleet-wide metrics
fleet.consensus_rate  # BFT agreement percentage
fleet.safety_score    # Governance compliance
fleet.task_throughput # Tasks completed per minute

Multi-Cloud Provider Map

Provider	Role	Artifact Type	Region
HuggingFace	textgen	LoRA adapters, full weights	Global CDN
GCP Vertex	embed	Embedding models	us-central1
AWS SageMaker	runtime	Inference endpoints	us-east-1

Key Files

File	Purpose
training/federated_orchestrator.py	Multi-cloud artifact fusion + gates
training/train_node_fleet_three_specialty.py	3-head specialty training
hydra/spine.py	Fleet coordination backbone
hydra/swarm_governance.py	BFT consensus for promotions
agents/browser/fleet_coordinator.py	Browser fleet management

Sacred Tongue Deploy Mapping

Stage	Tongue	Meaning
Build	CA (Cascade)	Breaking down, compiling
Test	RU (Runethic)	Binding, validating rules
Stage	UM (Umbroth)	Hidden, not yet revealed
Deploy	KO (Kor'aelin)	Asserting into production
Monitor	AV (Avali)	Listening, watching
Rollback	DR (Draumric)	Structured retreat