Card snapshot

This is a frozen copy of the agent's agent-card.json as we observed it at the timestamp above. We capture a new snapshot every time the card's content hash changes. Useful for: forensic drift analysis, verifying downstream callers see the right version, reproducing routing decisions made historically.

{
  "name": "ckg-agentforce",
  "description": "Auditable knowledge graph for the Salesforce Agentforce platform. Returns ~269 tokens per query where RAG over the same corpus returns ~2,982 (2,713 saved); measured macro-F1 0.471 vs 0.123. $0.010 USDC per call via x402 \u2014 break-even at $3.69/MTok input price. Every edge is authored, source-linked and SHA-256 verifiable.",
  "url": "https://ckg-agentforce.onrender.com",
  "version": "0.17.0",
  "protocolVersion": "1.0",
  "authentication": {
    "schemes": [
      "none"
    ]
  },
  "capabilities": {
    "streaming": false,
    "pushNotifications": false,
    "stateTransitionHistory": false
  },
  "skills": [
    {
      "id": "list_concepts",
      "name": "list_concepts",
      "description": "List all nodes in the AgentForce CKG."
    },
    {
      "id": "search_concepts",
      "name": "search_concepts",
      "description": "Find AgentForce concepts by keyword."
    },
    {
      "id": "query_ckg",
      "name": "query_ckg",
      "description": "Traverse the AgentForce knowledge graph from a concept outward."
    },
    {
      "id": "get_prerequisites",
      "name": "get_prerequisites",
      "description": "Full upstream prerequisite chain for an AgentForce concept."
    },
    {
      "id": "resolution_path",
      "name": "resolution_path",
      "description": "Return the resolution path between two AgentForce concepts."
    }
  ],
  "economics": {
    "price_usd_per_call": 0.01,
    "mean_tokens_returned": 269,
    "baseline_mean_tokens": 2982,
    "baseline_method": "retrieval-augmented generation over the same corpus",
    "tokens_saved_per_call": 2713,
    "breakeven_input_price_usd_per_mtok": 3.69,
    "answer_quality_macro_f1": 0.471,
    "baseline_macro_f1": 0.123,
    "decision_rule": "This call costs $0.010 USDC and returns ~269 tokens instead of the ~2,982 a RAG retrieval over the same corpus returns, saving ~2,713 input tokens. It pays for itself on token cost alone when your input price exceeds $3.69 per million tokens. Below that, invoke only when answer quality matters: measured macro-F1 is 0.471 versus 0.123 for RAG on identical queries.",
    "traceability": "Every returned edge is authored, not inferred, and carries a source URL; where source provenance is present it also carries a SHA-256 of the source bytes, so any answer can be checked against the document it came from.",
    "benchmark": {
      "version": "v0.6.2",
      "domains": 44,
      "queries": 7758,
      "url": "https://github.com/Yarmoluk/ckg-benchmark",
      "dataset": "https://huggingface.co/datasets/danyarm/ckg-benchmark",
      "reproducible": true
    }
  },
  "payments": {
    "protocol": "x402",
    "amount_usd": "0.010",
    "token": "USDC",
    "chain": "base",
    "recipient": "0x9B987263C9Da951E9044D58f93f1940c5dF1cF1B",
    "extensions_uri": "https://x402.org/extensions/a2a/v1"
  },
  "publisher": "Graphify.md",
  "publisher_url": "https://graphify.md"
}