Known sources and topics
Keep current state for organizations, markets, research areas, media sources, policy domains, or saved coverage scopes.
Synorb is a web search alternative when an AI agent needs repeatable, source-grounded coverage of known topics, entities, or Source Channels. Instead of running the same cold search again and again, the agent listens to Streams and receives Manifests as watched sources change.
It is not a replacement for every search. Agents should still search the open web for unknown sources and open-ended discovery. The practical pattern is listen first, search for gaps.
Watched Source Channels · Streams of Manifests · Source URLs and stable IDs
When an agent repeatedly asks search to rediscover the same sources, it spends context, latency, and tool calls rebuilding state it could have been maintaining. That is especially painful for monitoring workflows where the coverage area is already known.
| Repeated web search | Synorb Streams |
|---|---|
| Starts after the user asks, returns links or snippets, and may require the agent to re-check sources it has already seen. | Starts from watched Source Channels, routes updates into Streams, and returns Manifests with source URLs and provenance. |
| Useful for unknown sources, broad exploration, and novel questions. | Useful for known coverage areas, monitoring, citations, stable IDs, and production workflows. |
Synorb fits agents that monitor companies, industries, policy areas, research topics, current events, data releases, filings, or source watchlists. The agent benefits because each update arrives already connected to source metadata and Stream routing.
Keep current state for organizations, markets, research areas, media sources, policy domains, or saved coverage scopes.
Carry source URLs, source names, dates, Source Channels, stable IDs, and lineage into model outputs and UI cards.
Use REST, webhooks, and S3 when updates should trigger alerts, enrichment, retrieval, review, or storage.
The agent receives Manifests from Streams. Each Manifest is structured for machine use and human audit: Briefs for readable context, Signals for atomic claims, Records for durable source state, and provenance for citations.
{
"manifest_id": "1777525429698648000",
"stream_names": ["ai-infrastructure", "company-monitoring"],
"source": {
"name": "Observed Source Channel",
"url": "https://source.example/update",
"published_date": "2026-06-17"
},
"brief": {
"title": "What changed"
},
"signals": [
{
"claim": "Claim grounded in the source event",
"confidence": 0.91
}
],
"delivery": {
"interfaces": ["MCP", "REST", "webhook", "S3"]
}
}
A practical agent workflow uses Synorb for known coverage, stores Manifests in memory or application state, cites source URLs in outputs, and falls back to web search only when the question asks for something outside watched Streams.
If an agent does not know the source universe, it should search. If it needs to keep known coverage current with citations and stable IDs, it should listen to a feed. Synorb is honest infrastructure for the second case.
No. Synorb is an alternative for repeatable, watched coverage areas. Agents should still use web search for unknown sources, broad discovery, and questions outside configured Streams.
Use Synorb when the agent needs current, source-grounded updates from known topics, entities, or Source Channels, especially when provenance and stable IDs matter.
Synorb lets the agent listen to Streams and keep Manifests in state, so it does not need to run the same cold search just to rediscover known source changes.
Use web search for open-ended discovery, unfamiliar sources, broad background research, and user questions that fall outside watched Source Channels or configured Streams.
The agent receives Manifests with Briefs, Signals, Records, source URLs, stable IDs, Stream routing, provenance, and delivery metadata over MCP, REST, webhooks, or S3.
Start with keys, then connect a compatible agent with MCP or route Manifests into your backend with REST, webhooks, or S3.