Agentforce and the Data Gap Challenge: A Unifying Data Layer?

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While Agentforce opens up unprecedented possibilities in Salesforce, missing data can limit workflows or lead to incorrect answers. This happens when Agentforce cannot retrieve relevant data stored in an external system, i. e. outside the Salesforce environment. If the AI agent hits such a data gap, it will make mistakes and deliver incorrect feedback, leading to confusion for users relying on Agentforce information.

When looking for ways to bridge this data gap, one potential solution besides middleware which we have already discussed is a unifying data layer. Generally speaking, the term refers to a centralized data architecture, often originating from the concept of Customer Data Platforms (CDPs), that brings together data from multiple sources, standardizes it, and makes it accessible in a unified format.  

How Does a Unifying Data Layer Work?

In the specific case of the data gap described, instead of making Agentforce query multiple external systems directly, a unifying data layer acts as an intermediary between Salesforce (including Agentforce AI) and external systems, ensuring access to external data. This is achieved by

• Consolidating data: The data layer accesses and harmonizes data from various sources (e.g.,  databases, on-premise systems, third-party apps).
• Storing or virtualizing data: It can either store a copy of the data (e.g., in Salesforce Data Cloud) or create a virtualized layer where data is accessed on demand without full ingestion
• Standardizing & preparing data: It helps transform, clean, and structure data .
• Providing a single access point: It enables interaction with the data layer instead of navigating multiple disconnected sources.  

While a unifying data layer can offer benefits in bridging data gaps between Salesforce's Agentforce and external systems, potential drawbacks should also be considered:

• ‍Complexity in Implementation: Establishing a unifying data layer often requires intricate integration processes, especially when dealing with diverse external systems. This complexity can lead to extended development times, and sometimes to an increased potential for errors.
• ‍High Costs: Implementing a unifying data layer, such as Salesforce Data Cloud, can be expensive, particularly for small and medium-sized businesses. Costs encompass licensing fees, implementation, customization, integration, and ongoing maintenance. Moreover, in many cases the amount to pay depends on the volume of records processed. This is especially tricky as one usually does not know beforehand how much data needs to be retrieved. So costs may rise in a hardly predictable manner.
• ‍Learning Curve: The extensive functionalities of platforms like Salesforce Data Cloud can result in a steep learning curve, necessitating substantial training for users to become proficient.  
• ‍Customization Limitations: Tailoring the unifying data layer to specific business needs often requires advanced conceptual and technical skills, especially if it’s a broad set of applications or systems with different kinds of functionalities. Not every field in one platform will cross-map to a relevant field in another, especially if it's a less popular tool. So, there are limits to what can be achieved without intricate custom development.  
• ‍Need of Mapping: With solutions like Salesforce Data Cloud, mapping is a must across different clouds and for external systems. A simple import from another Salesforce org may not need it, but when merging data from multiple orgs, like combining contacts from Sales Cloud and Marketing Cloud, proper mapping is required. The same goes for integrating imported data for analysis or making it usable in Agentforce.

Consider Keeping it Easy and Low-cost

These drawbacks may not apply to your particular requirements. Or you may have the time and budget to make the investment to overcome them. On the other hand, if you see that the friction in systematically introducing a unifying data layer will slow down your AI agent projects, you may want to consider an easier and low-cost alternative:  

conemis Synapse is the simple and effective way to fill in Agentforce data gaps in a flash. Based on your requirements, data access will be set up for you  – tooling, connectors, mapping, data flow, and all necessary setup services included in one turnkey solution. No project required. conemis will take care of everything at a flat rate.

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