Record
Human View is the term as people read it. Machine View is exactly what an AI agent receives.
This is the page you're already on. The Human View toggle above shows it in full.
This is exactly what an AI agent receives. The same record, as JSON-LD, below.
Use this record in your app or agent
di get-glossary --slug signals
GET https://api.decisioninsights.ai/api/public/v1/glossary/aff98d9f-57fd-4aa1-90e8-5e4a8dc37416
curl -X POST https://mcp.decisioninsights.ai/api/mcp-public \
-H "content-type: application/json" \
-H "accept: application/json, text/event-stream" \
-d '{"id":1,"jsonrpc":"2.0","method":"tools/call","params":{"arguments":{"slug":"signals"},"name":"di_get_term"}}'
The CLI command and the REST call for this record need a free account: run di login, or send your OAuth token. Over MCP it is free with none.
A green key on the record, left, is one Decision Insights added. It doesn't exist on the company's site or in a wire feed. The same key labels its row on the right, so you can match them directly. More on how DI enriches a record.
This is what the machine sees: the exact JSON-LD an agent receives for this listing.
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"@type": "Organization",
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"name": "Aviz Networks"
},
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Graph
A glossary entry defines a term and stops there. It doesn't show where the term is actually being used or written about. We track and link every article that covers it.
The person who sent this agent gets an answer they can act on
Somebody dispatched this agent to evaluate Record for a real decision: a shortlist, a vendor comparison, a purchase. "Record makes X" isn't something they can act on; they need to know the claim is real and where it came from. Follow one enriched link from Act 2 and see what the agent can actually hand back to them:
- The record DefinedTerm Record
- One of its 12 linked records BlogPosting ONES 2.1 updates network observability and management capabilities https://decisioninsights.ai/ones-2-1-updates-network-observability-and-management-capabilities/
- The primary source CreativeWork aviznetworks.com https://aviznetworks.com/resources/blogs/ones-2-1-advancements-in-network-visibility-with-multi-nos-telemetry-ai-fabric-anomaly-detection-and-data-lake-integration Original author: Tarun Kumar Polanki
Our summary is written by Decision Insights Coverage. The source keeps its own byline, credited above. Both are in the JSON.