Verifiable Knowledge Graph for Salesforce AgentForce
A solution providing source traceability for AI assistants.
1Problem Statement
Every failed resolution in Salesforce AgentForce costs $2 and impacts customer satisfaction. Traditional methods lead to expensive wrong inferences. This project aims to solve this by providing deterministic answers.
2Technical Approach
The system transforms the AgentForce stack into a knowledge graph with 40 nodes and 52 edges. Each edge is based on SHA-256 verified source documents. Uses pre-structured relationships instead of inference.
3Source Provenance
Every node has a source URL and content hash. Developers can verify source integrity using curl commands. The system automatically reprocesses when hash mismatches occur.
4Use Cases
1) $2 retry problem: Agents failing to identify correct resolution paths. 2) Policy blocks: Graph shows which capabilities Trust Layer restricts. 3) Grounding issues: Automatically displays prerequisite chains.
5Edge Types
REQUIRES: Hard prerequisite, ENABLES: Capability unlock, IMPLEMENTS: Concrete instantiation, RELATES_TO: Conceptual proximity. Each edge type carries different confidence signals.
6RAG Comparison
While traditional RAG systems return probabilistic results, this graph only shows relationships declared in sources. For Einstein Trust Layer queries, RAG returns 6 separate docs while the graph answers in one hop.
7Performance Data
Tests showed 121% improvement in billing path queries and 158% in trust layer queries. Runs cost-effectively on CPUs with local models.
8Developer Experience
Available as Python package installable via pip or uvx. Developers can access benchmarks and documentation at graphifymd.com.
9Industry Impact
Enhances reliability of AI solutions in Salesforce ecosystem. Particularly valuable for high-risk sectors like finance and healthcare. Source traceability facilitates regulatory compliance.
Original article: Hacker News: Show HN — Salesforce AgentForce için Doğrulanabilir Bilgi Grafiği