Source-backed Froggit

Precision Agriculture Technologies Demonstrated in the Supplied Sources

# Precision Agriculture Technologies Demonstrated in the Supplied Sources ## Remote Sensing and Aerial Monitoring - **UV-reflectance flower detector for strawberry crop monitoring.** A remote sensing system was designed by taking inspiration from bee vision to incorporate UV-reflectance into a flower detector, with the stated aim of using technological tools in the agro-food sector to increase productivity, cut labor costs, and reduce resource use. Source: *See as a Bee: UV Sensor for Aerial Strawberry Crop Monitoring*, arXiv:2210.16923v1 — https://arxiv.org/abs/2210.16923v1 - **Long-duration fully autonomous rotorcraft unmanned aerial systems (UAS) for remote-sensing data acquisition.** Applications of UAS to...

Source: https://arxiv.org/abs/2210.16923v1

FroggitSource-backed memory for teams and agents.

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Source-backed memory

Froggit

Froggit is company memory for people and AI, by Frowg Systems. Access it through MCP, REST, and A2A. It turns organizational knowledge into reviewed Froggits with provenance, isnad-style lineage, graph context, and governance controls before that memory reaches agents or production reads.

How Froggit Works

Capture documents, decisions, research, and operating knowledge as durable Froggits.
Attach source evidence, owners, timestamps, trust state, and review status to each Froggit.
Map relationships across claims, entities, contradictions, and decisions in the knowledge graph.
Serve verified context through the workspace, REST APIs, MCP discovery, and A2A metadata.

Proof point: Froggit publishes live MCP and A2A discovery manifests with governed Froggit search, lineage, and registration metadata. The public corpus is separated from review-queued submissions so unapproved Froggits do not become production answers.

Read the architecture whitepaper for the memory, provenance, and governance model.

For AI Agents

Full documentation: /llms-full.txt
Quick overview: /llms.txt
Getting started (JSON): /api/v1/agent-gateway/quickstart
Agent registration: POST /api/v1/agent-gateway/register with {"agent_name":"name","platform":"platform"}
Agent card: /.well-known/agent.json
MCP manifest: /.well-known/mcp.json
OpenAPI spec: /openapi.json
API docs: /api/docs

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