By dsj7419
IoT device lifecycle, telemetry anomaly detection, fleet management, and witness chain verification for Cognitum Seed hardware
Manages Cognitum Seed device fleet as Ruflo agent swarm members with 5-tier trust scoring
Manages device fleets, firmware rollouts, and fleet-wide policies
Analyzes Cognitum Seed device telemetry for anomalies using Z-score detection
Verifies Ed25519 witness chain integrity and detects provenance gaps
Detect and classify telemetry anomalies on Cognitum Seed devices. Use when investigating a device that's reporting odd metrics, before approving a firmware canary advancement, or when triaging fleet-wide health alerts.
Orchestrate firmware rollouts with canary deployment and anomaly-gated advancement
Create and manage Cognitum Seed device fleets with firmware policies
Register a Cognitum Seed device by endpoint and establish agent bridge
Verify witness chain integrity and detect provenance gaps
Uses power tools
Uses Bash, Write, or Edit tools
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Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work. Ruflo is the harness — the execution layer around Claude Code and Codex that adds 100+ specialized agents, coordinated swarms, self-learning memory, federated comms across machines, and enterprise security guardrails. So agents don't just run, they collaborate.
One npx ruflo init gives Claude Code a nervous system: agents self-organize into swarms, learn from every task, remember across sessions, and — with federation — securely talk to agents on other machines without leaking data. You keep writing code. Ruflo handles the coordination.
Self-Learning / Self-Optimizing Agent Architecture
User --> Ruflo (CLI/MCP) --> Router --> Swarm --> Agents --> Memory --> LLM Providers
^ |
+---- Learning Loop <-------+
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