Part of the protocols.md network

EDGE.md

The Edge Intelligence Protocol for Autonomous Agents
THE OPPORTUNITY

Agents Learning at Light Speed

Every autonomous agent has untapped potential at the edge. When agents share discoveries locally, innovation accelerates. When intelligence stays at the edge, privacy and performance unite.

5ms local sharing = instant optimization
Hyperlocal context = perfect adaptation
Edge-native learning = privacy by design

Smart Factory Optimization

Manufacturing robots discover new efficient assembly patterns. Edge.md instantly shares these optimizations across the facility, improving throughput by 23% without cloud dependency.

PERFORMANCE: 5ms response

City-Wide Traffic Flow

Autonomous vehicles collaborate through edge nodes to create dynamic green corridors. Edge.md enables real-time route optimization based on traffic patterns.

EFFICIENCY: 40% less transit time

Precision Agriculture Network

Drone swarms share micro-climate data at field edges. Edge.md enables each drone to instantly adapt spray patterns based on neighboring discoveries, maximizing crop yield.

COLLECTIVE INTELLIGENCE: 50+ agents learning together
ONE PROTOCOL

Edge Instructions as a Service

// Agent encounters edge case
POST https://edge.md/instructions
{ "agent_id": "drone_ag_447",
"location": [37.7749, -122.4194],
"context": "unexpected_wind_pattern",
"local_conditions": { "wind_speed": 45,
"direction_variance": 120
} }

// Edge responds with local intelligence
{ "instructions": "formation_delta_3",
"confidence": 0.94,
"learned_from": "local_swarm_last_7d",
"ttl_ms": 5000
}

Discovery Layer

Agents find nearest edge nodes with relevant context. Geographic and semantic routing in <5ms.

Context Cache

Hyperlocal learnings stored at edge. Privacy-preserving collective intelligence without cloud exposure.

Instruction Synthesis

Real-time generation of context-specific guidance from local patterns and edge-native models.

THE PARADIGM SHIFT

From Cloud Intelligence to Edge Intelligence

Old Way

Train in cloud

Deploy static model

Fail on edge cases

New Way

Learn at edge

Adapt in real-time

Handle anything

<5ms
Response Time
100%
Local Processing
Edge Cases Handled
IMPLEMENTATION

Edge-Native Agent Architecture

Edge Mesh Network
NODE
NODE
NODE
Nodes share instructions across distributed mesh
// Register edge capability
PUT https://edge.md/nodes/register
{ "node_id": "edge_sf_001",
"capabilities": [
"vehicle_routing",
"construction_zones",
"pedestrian_patterns"
], "coverage_radius_m": 500,
"max_latency_ms": 5
}

// Share collective learning
POST https://edge.md/learn
{ "pattern": "construction_lane_shift",
"success_rate": 0.97,
"share_with": "local_mesh"
}
PROOF OF PROTOCOL
Specification Hash (SHA-256):
e7d4a2f89c3b5e6d1a0f9c8b7a6e5d4c3b2a1f0e9d8c7b6a5f4e3d2c1b0a9f8e7
Build Timestamp: 2025-09-01T23:45:00Z
Protocol Version: v0.1-alpha
Git Tag: edge-intelligence-preview

Verify this spec:
curl https://edge.md/spec.json | shasum -a 256

Why Edge.md Becomes Mission Critical

As compute moves from centralized clouds to distributed edges, agents need a standardized protocol for discovering, negotiating, and utilizing edge intelligence. Edge.md isn't just another API—it's the namespace for an entire computing paradigm.

Like DNS for the internet or NPM for JavaScript, Edge.md becomes the critical infrastructure that every autonomous system depends on. The domain that defines how machines think at the edge.