Meet Spark · Gluesync AI Hub
Spark is the AI agent layer inside Gluesync. It reasons with your model, acts through governed Core Hub tools, reads MOLO17's own documentation and knowledge tools, and extends across sites with Gluesync Connect.
One agent layer · Three tool sources
Instead of a model guessing about your environment, Spark calls tools that already exist — in your Core Hub, in MOLO17's published knowledge, and across the sites Connect will manage.
Spark reaches pipelines, agents, entities, connections, metrics, notifications, Chronos, and Query Studio SQL through the 107-tool embedded catalog. Each agent carries an explicit allow-list, and every call runs with the signed-in user's Core Hub permissions.
Spark can also consult MOLO17's own hosted MCP tools for product documentation and knowledge, so an answer about Gluesync behavior cites published material instead of improvising from model memory.
Connect's hosted assistant is designed to run Spark across many registered sites for fleet-wide questions and incident summaries. Connect is coming soon, planned for Q4 2026, and available today through the waitlist.
Where Spark shows up
Spark agents answer questions about the estate, explain notifications in plain language, and send severity-aware reports through configured webhooks and email.
Explore → Published runsFreeze an agent into an immutable version, then start asynchronous runs with idempotency, cancellation, and an ordered event timeline.
Explore → Query Forge SQLName an agent with spark_agent in a gluesync.ai query and read its answer, optional SQL draft, citations, and conversation ID as columns.
Explore → ChronosChronos invokes a published agent through ai_agent_run from a cron schedule, a chained pipeline event, or an inbound webhook — with prompt templates, a payload allow-list, and loop guards.
Explore →Why operators allow it
Spark inherits the controls your team already trusts, so a helpful agent never becomes an unreviewable one.
Read the AI Studio documentation →Spark forwards the signed-in identity through existing Core Hub authorization. There is no privileged AI service account behind the conversation.
SQL through Spark follows Query Studio controls and stays read-only by default. The gluesync.ai path only ever offers read-only tools.
A pipeline mutation never runs straight from the model. The first call is held as a pending action bound to owner, conversation, and pipeline until someone confirms it.
Every turn shows which tools ran and how they finished. Provider keys, connection credentials, and raw result rows stay out of the trace.
Your infrastructure · your models · your permissions
