Unlocking the True Value of Agentforce: Why We Need a Generic Agent Orchestration Framework

The evolution of AI agents—such as Salesforce Agentforce—has been remarkable. However, completing a practical, end-to-end business process using only a single prompt or a simple flow remains extremely difficult in real-world enterprise environments.

I am currently building a Generic Agent Orchestration Framework. In this article, I will explain why such a framework is necessary, using concrete use cases and architectural perspectives.

1. From “Single AI Tasks” to “End-to-End Business Processes”

When integrating AI agents into business operations, the biggest challenges are state management and control of complex dependencies.

Take a seemingly simple task like business card scanning. It does not end with converting an image into text. In reality, the business flow involves multiple interconnected steps:

1. AI processing: Analyze the image and extract text
2. Web search: Enrich company information and build a profile
3. Database matching: Check for duplicates against existing Salesforce records
4. Human judgment: Let the user decide whether to create a new Lead or update an existing record
5. Data update: Save the final record and write audit logs

Rather than connecting these steps with isolated scripts, a mechanism is required to control them under consistent and centralized state management.

2. Why Is This Framework Necessary? Three Technical Imperatives

① Metadata-Driven Design to Eliminate Hardcoding

Modifying Apex code every time business logic changes does not scale. This framework manages workflows as configuration using custom metadata, such as:

• OrchestrationDefinition__mdt: Defines global workflow settings such as timeouts and logging
• StageDefinition__mdt: Defines step order, execution conditions, and input/output mappings
• AgentDefinition__mdt: Defines AI models, Apex classes, and action types

This approach enables flexible addition and modification of workflows without changing code.

② Unified Support for Multiple Execution Models (Sync & Async)

AI execution time is unpredictable, and external integrations make synchronous processing prone to governor limits and timeouts. This framework seamlessly combines multiple execution models:

• Queueable: Ideal for complex chained processes and data operations
• Future: Used for simple external callouts
• Batch: Handles large-scale data analysis and processing
• Scheduled Batch: Executes periodic reports and checks
• Platform Events: Enables scalable, event-driven integrations

③ Full Visibility and Auditability

In enterprise environments, it is essential to answer the question: “Why did the AI make this decision?” This framework fully records execution logs, error logs, and performance metrics, and includes a state management system that supports retries and recovery.

3. Practical Example: How the Business Card Scanning Agent Works

As a concrete application built on this framework, consider a Business Card Scanning Agent, which orchestrates multiple AI capabilities with different characteristics:

4. Conclusion: Turning AI into a “Usable System”

Agentforce is a powerful engine. But to run it on the complex roads of enterprise business processes, it needs a solid chassis.

The Generic Agent Orchestration Framework I am building serves exactly this purpose. With metadata-driven flexibility, robust asynchronous processing, and complete audit logging, AI agents can finally leave the laboratory and take on a central role in real business operations.