Agentforce Prompt Reusability: Applying Separation of Concerns to AI Prompt Engineering

Overview

By now, it’s clear that Salesforce Agentforce is here to stay. As organizations rapidly adopt it to automate business processes, a shared concern begins to emerge: prompt proliferation. Without intentional design principles and with the rapid and sometimes frenetic adoption of Agentforce, teams will quickly accumulate dozens of prompt templates that perform similar functions with slight variations, setting the stage for a maintenance nightmare reminiscent of poorly adopted frameworks around Apex or even Flow.

Since this is uncharted territory, it’s too early to talk about best practices. However, applying fundamental software engineering principles to prompt design will allow us to treat prompts as reusable, modular components rather than one-off solutions. In this way, we can build scalable, maintainable AI systems that evolve with business needs without creating a scalability nightmare. This is where Separation of Concerns (SoC) comes into place. 

The Upcoming Problem: Prompt Proliferation

We all know by now that agents use and need context to be successful. They need grounding, they need clear tasks. However, they don’t need to have ALL of it hardcoded on the prompt to work. 

Let’s use an example to illustrate the point. Imagine your organization starts building Agentforce Prompts to read through uploaded files. Initially, the team creates a prompt to analyze and summarize a contract on an Opportunity. It works beautifully, Sales is really happy (wait, is that even possible?), and you call it a successful AI use case. Then, Finance wants an easier way to extract the payment terms from the same contract. Rather than reusing the existing prompt, the team creates a new one with nearly identical logic but slightly different language. After that, Delivery wants an Agent to help with reading SOWs and summarizing them. Soon, you will have three prompt templates. Think about it though, each prompt does essentially the same thing: 

1. Read a file
2. Extract key information
3. Produce a structured summary

This is the AI equivalent of copy-paste programming, and it’s equally problematic, especially in the AI space, where every organization is new and trying to learn as the days go by. 

Separation of Concerns: A Proven Design Principle

Separation of Concerns, introduced by computer scientist Edsger W. Dijkstra in 1974, is a design principle for separating a computer program into distinct sections, each addressing a separate concern. As stated in his seminal work “On the role of scientific thought“:

“It is what I sometimes have called ‘the separation of concerns’… it does not mean ignoring the other aspects, it is just doing justice to the fact that from this aspect’s point of view, the other is irrelevant.”

If we keep digging into software architecture principles, Robert C. Martin reinforced this concept through The Common Closure Principle & The Common Reuse Principle: a module should have one reason to change, meaning it should have a single, well-defined responsibility.

Under these rules, each component focuses on its specific concern, making systems easier to understand, test, and maintain.

(Fun fact: When I learned about these principles, my Minecraft-shaped brain felt really, really happy!)

How do we apply Separation of Concerns to Agentforce Prompts?

By now, you probably connected the dots. Let’s go back to our example. Instead of creating document-type-specific prompts, consider this generic prompt template (for illustration purposes only, not prod ready):

You are a file analysis assistant.

INPUT:
- File: {!$Input.FileContent}
- Task: {!$Input.TaskDescription}
- Context: {!$Input.AdditionalContext}

OBJECTIVE:
Analyze the provided file according to the specified task, considering any additional context provided.

INSTRUCTIONS:
1. Read and comprehend the file content thoroughly
2. Focus on aspects relevant to the specified task
3. Apply any context-specific requirements or constraints
4. Produce a summary that addresses the task requirements

OUTPUT:
Provide a structured summary that:
- Directly addresses the task requirements
- Highlights key information from the file
- Incorporates relevant contextual considerations
- Uses clear, concise language appropriate for the context

Now, analyze the file and complete the task.

How This Works in Practice

This single prompt can handle multiple scenarios by parameterizing the specifics, and letting, e.g., a Salesforce Flow pass those parameters depending on the use case:

Case 1: Contract Analysis

• File: Legal contract PDF uploaded to the Opportunity
• Task: Extract contract terms, obligations, payment schedule, termination clauses, and renewal dates. Identify any non-standard clauses or risk factors.
• Context: Use the Opportunity Summary field to gather information about this particular deal (let’s assume for this example that you have a summary field on the Opportunity!)

Case 2: Extract Payment Terms

• File: Legal contract PDF uploaded to the Opportunity
• Task: On this Contract file, find the contract terms
• Context: None!

Case 3: SOW Summary

• File: SOW uploaded to the Project
• Task: Summarize the SOW. Identify the key deliverables, items in scope, items out of scope, and team composition. Include a responsibility matrix. Identify proposed technologies, integration points, and potential risks.
• Context: Use the status reports on active projects for the same customer to identify potential risks not included in this SOW but with potential impact on this project.  

The prompt doesn’t change. Only the inputs change, and they are controlled in separate Salesforce Flows. This is Separation of Concerns in action.

Benefits of Reusable Prompt Design

Easier to maintain: When you need to change anything on the vore logic( in our example, in the file processing) or add new capabilities, you update one prompt template instead of dozens. Bug fixes propagate instantly across all use cases.

Consistency: All similar processing follows the same structural logic, making outputs predictable and less prone to error. 

Scalability: New AI use cases that fit in your existing prompt don’t require new prompts. You simply work on a Flow that plugs to an existing prompt, reducing development time from hours to minutes.

Governance and Compliance: When prompts are reusable components, it’s easier to implement security reviews, data handling policies, and compliance controls centrally.

Implementation Strategy for Agentforce

Will all Agentforce prompts fall in this “generalized” category? NO. Absolutely not. However, it is important to identify the use cases that could be generalized in a single prompt to prevent the proliferation early on.  

1. Identify Core Capabilities: Map out the fundamental tasks your AI agents need to perform, e.g. file analysis and summarization, comparison across files, risk assessment and flagging, etc. 
2. Design Generic Prompt Templates: For each core capability, create a template that accepts parameterized inputs rather than hardcoding specific business logic.
3. Create Configuration Layers: Build a system (using Salesforce Flows or even custom metadata) that stores the specific Task and Context definitions for each business scenario.
4. Always Establish Naming Conventions: Use clear, capability-based names.  
5. Document, document, document: Clearly document required input variables and their formats, supported file types if files are involved, expected output structure, limitations or constraints.
6. Generalization doesn’t mean ignoring edge cases: Ensure your prompt handles unexpected inputs, in our example, that could be corrupted files, unsupported formats, etc. Always include error handling instructions in your template.
7. Context Control: Sometimes context IS important for proper interpretation. Design your prompts to respect context parameters when they matter, but don’t hardcode assumptions. Pass them as an input instead.

SoC Limitations in Prompt Design

While Separation of Concerns is a powerful principle for building scalable Agentforce solutions, it’s not a universal solution for every prompt. Highly specialized prompts with deeply domain-specific logic may not benefit from generalization. For example, a prompt that performs complex financial compliance checks against specific regulatory frameworks (like SOX or GDPR) might require so much hardcoded expertise that attempting to make it generic would either dilute its effectiveness or create an overly complex parameterization system. 

Similarly, prompts that require intricate multi-step reasoning with domain-specific decision trees may be better served as specialized, standalone prompts. 

The key question to ask is: “Will parameterizing this make it more maintainable, or just more complicated?” If adding flexibility requires dozens of conditional parameters or transforms a clear 50-line prompt into a confusing 200-line template with nested logic, you’ve likely crossed the line from helpful abstraction to over-engineering. Start with specialized prompts when you’re exploring new use cases, then refactor toward reusability once you identify patterns. 

Remember, premature generalization can be just as problematic as prompt proliferation. The goal of this approach is to find the right balance between reusability and clarity. 

Conclusion: Building for Scalability

As AI becomes embedded in every business process, the quality of our prompt engineering will determine whether we build sustainable systems or technical debt traps. By applying Separation of Concerns principle when possible, a principle proven over 50 years of software engineering, we can create Agentforce solutions that scale gracefully.

The next time you’re tempted to create a new prompt that’s “almost the same” as an existing one, pause. Ask yourself: “Can I make my existing prompt more generic and pass the specific requirements as parameters?” More often than not, the answer is yes.

Your future self and your entire organization will thank you for thinking architecturally about prompt design today.