Multi-agent discovery: how AI agents explore your data overnight
aiagents
A closer look at how multiple agents pursue different questions across a private graph, and why each pass builds on the last.
One of the least understood parts of Constella is its agent-driven review. Here is how it works in plain language.
The basic idea
Instead of waiting for a person to ask a question, several agents each take a different line of inquiry across your organization's graph. One may look for recurring themes, another for contradictions, another for links between unrelated teams.
Why multiple agents
A single query finds one answer. Several agents working in parallel widen the range of questions explored, which is how you move from answering questions to raising them.
How each pass builds on the last
- First review: agents explore the connections present in your captured material.
- Second review: they use what the first pass learned, so the second night builds on the first.
- Ongoing: the graph grows as new captures arrive.
What stays inside your boundary
Knowledge and the model that learned from it remain inside the boundary you set. This is central to responsible AI in practice.
Where humans fit
Agents suggest; people judge. Findings should be reviewed against sources before they drive decisions.
Related approaches
For task-oriented automation, browse the AI Agents Store. For the broader architecture, see the Silverberry AI Platform.
Results depend on your data and questions. Read the overview in Ask Constella for your AHA moment.