Salesforce Agentforce
Integrations
- Salesforce Data Cloud 3.0
- MuleSoft Anypoint AI
- Slack AI (Native Integration)
- GPT-5.3 (via Trust Layer)
- Claude 4.5
- Google Vertex AI
Pricing Details
- Transitioning to billing per successful 'Outcome'.
- Baseline usage included in UE+ and Einstein 1 editions via Agentforce Credits.
Features
- Neural-Symbolic Orchestration Engine
- Data Cloud 3.0 Active Context Fabric
- Autonomous Shield Threat Detection
- Outcome-Based Performance Billing
- Cross-Cloud Session Persistence
- Hyperforce Vector Engine v2
Description
Agentforce 2.0: Neural-Symbolic Architecture Audit
As of January 2026, Salesforce has standardized on Neural-Symbolic Orchestration. This hybrid approach combines the creative potential of LLMs with the rigid execution of symbolic logic, ensuring 99.9% reliability in complex multi-object CRM updates 📑.
Advanced Reasoning & Context Fabric
The Atlas 2.0 Reasoning Engine now utilizes 'OODA-Next' cycles, grounded in the Data Cloud 3.0 Context Fabric. This architecture enables agents to maintain state across disparate sessions and clouds 📑.
- Vector Engine v2: RAG is now powered by a high-performance, native vector substrate with built-in graph traversal, allowing agents to understand complex account relationships beyond flat text 📑.
- Autonomous Shield: A real-time governance layer within the Einstein Trust Layer that performs continuous heuristic analysis of agent thought-chains to prevent autonomous loop-hijacking 📑.
- Metadata-as-Logic: CRM Schema is now exposed as semantic graphs, enabling agents to self-correct navigation errors within the Salesforce Object Query Language (SOQL) scope 📑.
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Trust, Transparency & Outcome Pricing
Salesforce has moved to Outcome-Based Billing for high-volume agents. This is supported by immutable execution logs stored in Hyperforce Security Vaults, providing full forensic transparency 📑.
- Secure Tool-Calling: All Apex and Flow invocations are wrapped in 'Safe-Execution Containers,' ensuring that agent actions cannot exceed the user-context permissions (RBAC) even in fully autonomous modes 📑.
- Zero Data Retention (ZDR) 2.0: Includes cryptographic proofs of payload deletion from third-party model providers (OpenAI GPT-5.3, Anthropic Claude 4.5) 📑.
Evaluation Guidance
Architects should benchmark MTTR (Mean Time To Result) rather than simple token latency. Ensure all custom Apex tools are annotated with the new @AuraEnabled(Agentic=true) decorator for optimal discovery. Organizations must validate their Data Cloud Data Stream frequency to ensure the Vector Engine v2 is not operating on stale cache 📑.
Tool Pros and Cons
Pros
- Rapid AI deployment
- Industry pre-training
- Task automation
- Efficiency gains
- Reduced agent workload
- Faster response times
Cons
- Limited customization
- Variable agent effectiveness
- Salesforce integration required