Lynx

Researcher. Lynx takes an open-ended question and runs autonomous, multi-step research — planning sub-questions, gathering and cross-checking sources, and synthesising a cited report. Findings land as typed atoms in your knowledge graph, linked to the task that triggered them.

What Lynx Does

Where Altair is built for targeted, answerable research questions, Lynx is built for the broad, open-ended ones that take many steps to resolve. It decomposes the question, runs iterative search-and-synthesis passes, and returns a structured report with citations rather than a list of links.

• Open-ended deep research — multi-step investigation of a broad question, with its own planning and follow-up
• Cited reports — every claim carries a source; the report is saved as atoms, not a throwaway document
• Knowledge-graph native — findings become DATA / LEARNING atoms linked to the triggering node and searchable by the whole team
• Internal-first — searches existing atoms and documents before going external, so it builds on what the team already knows

Lynx vs Altair

Assigning Work to Lynx

// Create a deep-research task
const researchTask = await task({
  statement: "How are mid-market product teams adopting AI agents in 2026?",
  parentId: "epic_market_research",
  description: "Open-ended. Cover adoption patterns, blockers, and tooling. Deliver a cited report.",
  acceptanceCriteria: "A structured report saved as atoms, each claim with a source citation."
});

await task({
  action: "assign",
  taskId: researchTask.id,
  agentId: "lynx"
});

Access

Lynx is live and currently free to all teams. Assign it with the slug lynx. Questions: [email protected].