Graphify.md is a community-built project, not affiliated with Salesforce, that turns the AgentForce stack into a traversable knowledge graph for source-grounded lookup and dependency traversal. Built for Salesforce architects, platform engineers, and agent developers who need deterministic answers about billing paths, trust layer policy, action types, and grounding chains — with every result traceable to declared relationships rather than model inference.
"The graph doesn't guess — it traverses. Every answer traces to a declared edge."
40 nodes · 52 edges · hover to explore · colored by layer
Signal from Salesforce developer forums, Trailblazer Community, and hands-on deployments.
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.
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.
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.
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 trigger ├─ [REQUIRES] Resolution Criteria ├─ [REQUIRES] Audit Trail │ └─ [REQUIRES] Einstein Trust Layer │ └─ [REQUIRES] AgentForce Platform └─ [REQUIRES] Policy Enforcement └─ [REQUIRES] Einstein Trust Layer 269 tokens · declared edges only RAG: ~2,982 tokens · probabilistic
query_ckg("Einstein Trust Layer") → Dependents (what ETL 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.
Every edge in the graph was extracted from a Salesforce source document and given a type. There are 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.
Hard prerequisite. A cannot function without B — e.g., Einstein Agent REQUIRES Einstein Trust Layer. The load-bearing dependency type.
Capability unlock. A makes B possible — e.g., Model Selection ENABLES Reasoning Engine. Used for optimization paths.
Concrete instantiation — e.g., NVIDIA NIM IMPLEMENTS Reasoning Engine. Useful for finding which backend serves an abstraction.
Conceptual proximity without direction — e.g., Data Masking RELATES_TO Zero Data Retention. Real but weaker than REQUIRES.
"Einstein Trust Layer probably governs data access... (similarity: 0.79)"
Score is on the chunk, not the claim. The claim itself is unverified.
"Einstein Trust Layer REQUIRES AgentForce Platform and gates: Data Masking · Audit Trail · Zero Data Retention · Grounding · Einstein Agent · Policy Enforcement."
No score. Declared edge. Traces to trust layer source doc.
30 questions on the $2/resolution billing path, trust layer, and action types · CPU only · Ollama · temperature 0 · seed 42
What must resolve before AgentForce charges the $2 resolution fee?
What are the four AgentForce action types?
Settings → Connectors → Add connector → paste URL above.
// claude_desktop_config.json { "mcpServers": { "agentforce": { "command": "uvx", "args": ["ckg-agentforce"] } }}
| Tool | Description |
|---|---|
| list_concepts() | All 40 AgentForce concepts grouped by layer |
| search_concepts(query) | Fuzzy search across all concepts |
| query_ckg(concept, depth) | Typed subgraph around any concept (1–5 hops) |
| get_prerequisites(concept) | Full upstream prerequisite chain in dependency order |
| resolution_path() | The exact $2/resolution billing traversal — every hop declared |
| verify_source(concept) | Source URL + SHA-256 hash · GuardrailDecisionV1 audit chain |
40 nodes · 52 edges · edge types: REQUIRES · ENABLES · IMPLEMENTS · RELATES_TO
Einstein Agent · Service Agent · Sales Agent · Marketing Agent
Flow Action · Apex Action · MuleSoft Action · External Action · Standard Action · Custom Action
AgentForce Platform · Data Cloud · Salesforce CRM · Knowledge Base
Reasoning Engine · Model Selection · NVIDIA NIM · Token Budget · Context Window
Einstein Trust Layer · Data Masking · Zero Data Retention · Policy Enforcement · Compliance Rules
Autonomous Resolution ($2/event) · Audit Trail · Agent Metrics · Handoff to Human
Agent Topic · Agent Instruction · Resolution Criteria · Conversation State · Grounding
Omni-Channel Routing · Multi-Agent Orchestration · Prompt Template
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/ | Platform, agents, actions, grounding chain |
| 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 docs | 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, RAG/GraphRAG baselines |
7,928 queries · 5-hop F1: 0.772 (CKG) vs 0.170 (RAG) · dataset public on HuggingFace · run it yourself
No API key. No GPU. No inference at query time. Add the URL connector in claude.ai or run locally with one command.
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