AI: orkestrasyon
Pinagdurugtong ng AI orchestration ang tatlo: tinutukoy ng engine ang sandali sa user state, pinipili ang cohort, at pinipili ang aksiyon, kaya tumatakbo ang loop nang hindi mo kinakailangang i-wire nang manual ang bawat hakbang.
At a glance
- GraphQL
aiquery: summarise, explain, suggest- MCP
- Every
prodantix.*tool, callable by an agent - MCP host
https://eu.mcp.prodantix.com- Auth
- Access token
- Tinutukoy ang makabuluhang sandali sa live state
- Pinipili ang tamang cohort at tamang aksiyon
- Isinasara ang loop: ang aksiyon ay nagiging susunod na event na pinag-aaralan ng engine
Paano ito gumagana
Every answer is grounded in rows the caller could already read. Citations are resolved server-side from a built candidate list rather than taken from the model’s reply, so an answer can only point at something that exists and the caller is entitled to see.
Saan ito maaabot
| Surface | Operation |
|---|---|
| GraphQL | ai query: summarise, explain, suggest |
| MCP | every prodantix.* tool, callable by an agent |
Halimbawa
GraphQL
query Explain($projectId: String!) {
ai {
explainMetric(
projectId: $projectId
metric: "order.completed"
window: "P7D"
) {
summary
citations { kind id }
}
}
}Pointing an agent at prodantix
The MCP surface exposes the same capabilities to an autonomous agent. See MCP for the tool list and protocol details.
JSON
{
"mcpServers": {
"prodantix": {
"url": "https://eu.mcp.prodantix.com/mcp",
"headers": { "Authorization": "Bearer <access token>" }
}
}
}