Researcher - Lynx

Researcher - 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 Researcher - Lynx Does

Where Researcher - Lynx is built for targeted, answerable research questions, Researcher - 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

Researcher - Lynx vs Researcher - Lynx

Assigning Work to Researcher - Lynx

// Create a deep-research task
const researchTask = await task({
  statement: "How are mid-market 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

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