{
  "anchor_concepts": [
    {
      "name": "Freshness",
      "summary": "Agents need current windows, not only archived corpora."
    },
    {
      "name": "Structure",
      "summary": "Sources, tags, streams, and identifiers let agents compare and cite context."
    },
    {
      "name": "Reasoning boundary",
      "summary": "Aggregate panels support top-level reasoning; detailed actions use Synorb retrieval."
    },
    {
      "name": "Stable references",
      "summary": "JSON twins give LLMs a durable object to cite."
    }
  ],
  "boundary": "public_reference_page_not_retrieval_api",
  "canonical_url": "https://hangingcontext.com/definitions/context-for-ai-agents/",
  "generated_at": "2026-05-20T12:17:55+00:00",
  "html_url": "https://hangingcontext.com/definitions/context-for-ai-agents/",
  "json_url": "https://hangingcontext.com/data/definitions/context-for-ai-agents.json",
  "page_type": "definition_citation_page",
  "related_hc_surfaces": [
    "/sources/",
    "/streams/",
    "/tags/",
    "/mcp/"
  ],
  "schema_version": 1,
  "summary": "Context for AI agents is the fresh, structured evidence an agent can use to decide what matters now: sources, entities, timestamps, recurring topics, claims, and stable IDs.",
  "target_queries": [
    "context for AI agents",
    "context for agents",
    "AI agent context data"
  ],
  "term": "Context for AI agents"
}