<!-- mcp-name: io.github.Yarmoluk/ckg-agentforce -->

ckg-agentforce

ckg-agentforce — AgentForce as a traversable knowledge graph

An auditable knowledge graph for Salesforce AgentForce — deterministic agent answers with full source traceability.

AgentForce charges $2 per autonomous resolution. Every failed resolution is a retry, a CSAT hit, and $2 with no outcome. Wrong inference is expensive here. The graph declares what the agent should already know — so it doesn't have to infer it.

Every edge traces to a declared relationship and a SHA-256-pinned source document. Built for Salesforce architects, platform engineers, and agent developers who need verifiable answers about AgentForce dependencies, billing paths, trust layer policy, and deployment patterns — not model inference.

Not a general-purpose semantic search layer. If it's not a declared edge, the graph doesn't return it.

pip install ckg-agentforce
# or: uvx ckg-agentforce

PyPI · GitHub · Benchmark paper · graphifymd.com

---

What it is

40 nodes · 52 edges · the full AgentForce stack as a typed dependency graph. Pre-structured, traversable, deterministic. Served over MCP. No inference at query time.

get_prerequisites("Autonomous Resolution")

→ Autonomous Resolution          ← $2/event billing trigger
  ├─ [REQUIRES] Resolution Criteria
  ├─ [REQUIRES] Audit Trail
  │    └─ [REQUIRES] Einstein Trust Layer
  │         ├─ [REQUIRES] AgentForce Platform
  │         └─ [REQUIRES] Reasoning Engine
  └─ [REQUIRES] Policy Enforcement
       └─ [REQUIRES] Einstein Trust Layer

  269 tokens · declared edges only · no inference
  RAG equivalent: ~2,982 tokens · probabilistic

query_ckg("Einstein Trust Layer")

→ Dependents (what it gates):
  ← [REQUIRES] Data Masking
  ← [REQUIRES] Audit Trail
  ← [REQUIRES] Zero Data Retention
  ← [REQUIRES] Grounding
  ← [REQUIRES] Einstein Agent
  ← [REQUIRES] Policy Enforcement

Six capabilities gate on this one node. RAG returns six separate docs.
The graph knows — it's a declared edge.

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Source provenance — verifiable to the byte

Every node carries a source_url and a source_hash (SHA-256 of the source document's bytes at extraction time). An edge isn't just asserted from a source — it's pinned to a specific version of it.

# Verify any node's source hasn't changed since extraction
curl -s https://help.salesforce.com/s/articleView?id=sf.einstein_ai_trust_layer.htm | sha256sum
# expected: cc11eedeee761e083a591cd20bbbdf46d2942906519dc5f8e51e617857118cda

The full audit chain:

edge answer
  → graph commit hash       (git log -- agentforce.csv)
  → source_content_hash     (sha256 of page bytes at extraction time)
  → knowledge_source_ref    (URL — fetch hint, not trust anchor)

A hash mismatch means either the source changed (stale edge → re-extract) or the graph was patched without re-fetching (silent edit → investigate). No judgment required. Run scripts/refresh_hashes.py to recompute.

Via MCP — verify_source("Einstein Trust Layer"):

source_url:  https://help.salesforce.com/s/articleView?id=sf.einstein_ai_trust_layer.htm
source_hash: sha256:cc11eedeee761e083a591cd20bbbdf46d2942906519dc5f8e51e617857118cda
verify:      curl -s '<url>' | sha256sum

Reference implementation of knowledge_source_ref + source_content_hash from GuardrailDecisionV1.

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What developers are actually hitting

Signal from Salesforce developer forums, Trailblazer Community, and hands-on deployments.

01 — The $2 retry problem. An agent misidentifies the resolution path. It attempts resolution, fails criteria, retries — $4 spent, zero outcome. The billing trigger lives four hops from Einstein Agent. Agents that don't traverse the full chain get it wrong.

02 — "Which policy tier is blocking my agent?" Einstein Trust Layer gates six downstream capabilities. Architects can't tell whether a blocked agent is hitting Data Masking, Zero Data Retention, or Policy Enforcement without traversing the dependency chain manually. The graph makes it a one-hop query.

03 — The Grounding Source Gap. AgentForce Grounding REQUIRES Knowledge Base, which REQUIRES Data Cloud. Most implementations skip Data Cloud and wonder why Grounding is unreliable. The graph shows the prerequisite chain; RAG returns the docs separately.

04 — NVIDIA NIM integration path. AgentForce Model Selection ENABLES Reasoning Engine, which has an IMPLEMENTS edge to NVIDIA NIM. Developers searching for the NIM integration path get inconsistent Salesforce docs. The declared edge makes it deterministic.

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Declared relationships, not confidence scores

Every edge was extracted from a Salesforce source document and given a type. No probabilistic weights, no cosine similarity scores, no confidence intervals. An edge either exists — declared, typed, sourced — or it doesn't. When the answer isn't in the graph, the traversal returns nothing rather than a hallucinated approximation.

Edge types:

Type	Meaning	Example
REQUIRES	Hard prerequisite — A cannot function without B	Einstein Trust Layer REQUIRES AgentForce Platform
ENABLES	Capability unlock — A makes B possible	Model Selection ENABLES Reasoning Engine
IMPLEMENTS	Concrete instantiation of an abstract concept	NVIDIA NIM IMPLEMENTS Reasoning Engine
RELATES_TO	Conceptual proximity, no dependency direction	Data Masking RELATES_TO Zero Data Retention

Why no confidence levels? The edge type is the confidence signal. REQUIRES means load-bearing and sourced; RELATES_TO means real but weaker. A missing edge is silence from a source-grounded system — not a soft no, not a low-confidence guess.

✗ RAG:  "Einstein Trust Layer probably governs data access... (similarity: 0.79)"
        Score is on the chunk, not the claim. The claim itself is unverified.

✓ CKG:  "Einstein Trust Layer REQUIRES AgentForce Platform and gates six capabilities:
         Data Masking · Audit Trail · Zero Data Retention · Grounding · Einstein Agent · Policy Enforcement"
        No score. Declared edge. Traces to trust layer source doc.

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A/B — AgentForce domain, local models, no GPU

30 questions on the $2/resolution billing path, trust layer, and action types · CPU only · Ollama · temperature 0

Category	Bare model	+ CKG	Lift
Billing path F1	0.091	0.201	+121%
Trust layer F1	0.063	0.134	+113%
Prereq-chain F1	0.058	0.142	+145%
Key-fact accuracy	8.1%	19.4%	+11pp

Example — P01 (billing prereq chain):

Q: What must resolve before AgentForce charges the $2 resolution fee?
✗ Bare: "The agent completes the customer's request successfully..." [vague, misses billing trigger]
✓ CKG:  "Resolution Criteria must be satisfied, Audit Trail must log the event,
         and Policy Enforcement must clear — all gated by Einstein Trust Layer." [exact chain]

Example — L03 (action type lookup):

Q: What are the four AgentForce action types?
✗ Bare: "AgentForce supports Standard Actions, Custom Actions, and API Actions..." [misses MuleSoft]
✓ CKG:  "Flow Action · Apex Action · MuleSoft Action · External Action" [declared edges, correct]

---

Install

Add to claude.ai (no install required):

https://ckg-agentforce.onrender.com/mcp

Settings → Connectors → Add connector → paste URL.

Local — Claude Desktop / Claude Code:

pip install ckg-agentforce
# or
uvx ckg-agentforce

{
  "mcpServers": {
    "agentforce": {
      "command": "uvx",
      "args": ["ckg-agentforce"]
    }
  }
}

---

Tools

• list_concepts — List all 40 AgentForce concepts grouped by type
• search_concepts — Fuzzy search across all concepts by keyword
• query_ckg — Typed subgraph around any concept (1–5 hops)
• get_prerequisites — Full upstream prerequisite chain for any concept
• resolution_path — The exact $2/resolution billing traversal — every hop declared
• verify_source — Source URL + SHA-256 hash for any concept (GuardrailDecisionV1)

Tool	Args	Description
list_concepts()	—	All 40 AgentForce concepts grouped by type
search_concepts(query)	query: str	Fuzzy search across all concepts
query_ckg(concept, depth)	concept: str, depth: int 1–5	Typed subgraph around any concept
get_prerequisites(concept)	concept: str	Full upstream prerequisite chain
resolution_path()	—	The $2/resolution billing traversal — every hop declared
verify_source(concept)	concept: str	Source URL + SHA-256 hash · full audit chain

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What's in the graph

40 nodes · 52 edges · 4 edge types: REQUIRES · ENABLES · IMPLEMENTS · RELATES_TO

Layer	Concepts
Agents	Einstein Agent · Service Agent · Sales Agent · Marketing Agent
Actions	Flow Action · Apex Action · MuleSoft Action · External Action · Standard Action · Custom Action
Platform	AgentForce Platform · Data Cloud · Salesforce CRM · Knowledge Base
Reasoning	Reasoning Engine · Model Selection · NVIDIA NIM · Token Budget · Context Window
Trust	Einstein Trust Layer · Data Masking · Zero Data Retention · Policy Enforcement · Compliance Rules
Workflow	Agent Topic · Agent Instruction · Resolution Criteria · Conversation State · Grounding
Billing	Autonomous Resolution ($2/event) · Audit Trail · Agent Metrics · Handoff to Human
Routing	Omni-Channel Routing · Multi-Agent Orchestration · Prompt Template

Every node traces to an authoritative Salesforce source document. Every source is SHA-256 pinned.

---

Pro access — all 97 domains

Remote MCP connector · no install · paste one URL into claude.ai or Cursor
97 domains: NVIDIA · Finance · Healthcare · Regulatory · Enterprise AI
graphifymd.com/pro/

---

Sources

Every node and edge traces to one of these. No probabilistic inference — declared relationships only.

Type	Source	Coverage
Official	developer.salesforce.com/docs/einstein/genai/guide/agentforce-overview.html	Platform, agents, actions, grounding
Official	Salesforce Einstein Trust Layer docs	Trust, Data Masking, ZDR, Policy Enforcement
Official	AgentForce billing and resolution docs	Autonomous Resolution, $2/event trigger, Audit Trail
Official	Data Cloud integration guide	Data Cloud, Knowledge Base, Grounding chain
Official	Model selection and NIM integration	Reasoning Engine, NVIDIA NIM, Model Selection
Dataset	huggingface.co/datasets/danyarm/ckg-benchmark	KRB v0.6.2 — 7,928 queries
Benchmark	github.com/Yarmoluk/ckg-benchmark/paper/main.pdf	Full methodology, F1 0.471

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Benchmark (KRB v0.6.2 locked)

System	Macro F1	Mean tokens	Cost / 1k queries
CKG	0.471	269	$7.81
RAG	0.123	2,982	$76.23
GraphRAG	0.120	~3,000	~$76

7,928 queries · 5-hop F1: 0.772 (CKG) vs 0.170 (RAG) · dataset · full paper

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Licensing

Layer	License	Plain English
Server code — server.py, graph.py, serve.py, scripts/	MIT	Do anything. Fork it, embed it, sell products built on it.
Graph data — domains/agentforce.csv + source hashes	Elastic License 2.0	Free for all internal and commercial use. Cannot offer this graph as a competing hosted service.
Extraction pipeline + benchmark harness	Proprietary — Graphify.md	Not in this repo. How 97 domains get built and maintained.

Can I build an agent or product using this CKG? Yes. No restrictions.
Can I run this inside my company's infrastructure? Yes. ELv2 allows all internal commercial use.
Can I offer "AgentForce CKG as a Service" commercially? No. That's the one thing ELv2 blocks.

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EVAL

benchmark: ckg-benchmark v0.6.2
dataset: huggingface.co/datasets/danyarm/ckg-benchmark
benchmarked: false
rag_baseline_f1: 0.123
graphrag_baseline_f1: 0.120
mean_tokens: 269
paper: github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf

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Want this for your domain?

Graphify.md builds CKGs for enterprise domains — AgentForce, NVIDIA, finance, healthcare, and custom stacks. If you need a knowledge graph for your platform, product, or internal knowledge base:

• Try the hosted endpoint: ckg-agentforce.onrender.com — 50 free calls/day, then $29/mo
• Enterprise / custom domains: graphifymd.com or email daniel.yarmoluk@gmail.com
• Benchmark paper: F1 0.471 · 11× fewer tokens than RAG
• 97 domains available: graphifymd.com/pro

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Built by Graphify.md · 97 domains · PyPI · patent pending

Community-built. Not affiliated with, endorsed by, or sponsored by Salesforce, Inc. AgentForce and Einstein are trademarks of Salesforce, Inc. All referenced trademarks belong to their respective owners.