Deploy AI Agents 16X Faster with Agentforce

Hello. I'm sitting down today with Rebecca Wedeman, CEO and Principal Analyst from Valoire to share her insights on Enterprise AgentIC AI. We're exploring how a do it yourself or build your own approach compares to an AI platform like AgentForce and what you need to know to drive success. It's all based on her extensive research and in depth customer interviews. Rebecca, what's the benefit of a platform approach to AI versus a DIY approach?

So we found a real acceleration in time to value for customers that were building Agent Force agents. Organizations using that platform that was already built, tuned and optimized for agent development like AgentForce could deliver autonomous AI agents an average of 16 times faster than the DIY approaches. Wow. Yeah. We also found a real big increase in accuracy with an average increase of 75% compared to DIY efforts.

What factors do you think were contributing to that significant acceleration and that accuracy when they started using a platform like AgentForce?

Well, you know Nicole, we analysts like to put things into buckets, right? So we looked at a couple of key areas where AgentForce provides a distinct advantage over a do it yourself strategy.

Okay.

We found really seven, seven areas that we looked at and we talked to customers for each area. The kind of time and effort that was involved with models set up, data and application integration, prompt engineering, AI guardrails for security, building out that user interface and tuning. So let's start with model setup with diy, organizations often spent months evaluating, prototyping and tuning large language models or building their own retrieval. Augmented generation RAG databases took a lot of expertise. Because agentforce comes with a pre tuned LLM and integrated RAG capabilities, they were able to largely eliminate this setup time. One customer for example, told us that they had developed a beta of agentforce within weeks, where building it in their own LLM would have taken months. Another big area that we looked at as I mentioned, was data integration, connecting and preparing data for AI. Because AgentForce leverages Salesforce's data cloud, which connects, unifies and harmonizes that customer data. Customers that already had their ARM data in Salesforce were able to see quick benefits. The pre built RAG capabilities in data cloud, which helped them bring in all that unstructured data, also really accelerated that data integration time. One customer I spoke to, for example, that had built their RAG and data cloud in just a few hours with the help of a Salesforce solution engineer where they had spent a ton of time trying to pull all of their product catalogs into their DIY approach and never really been successful with it. Another key area that we looked at, as I said, was prompt engineering. Because AgentForce has Prompt Builder, it allows organizations to easily build and reuse prompt templates, reduces the need for that deep prompt engineering expertise. And also those pre built prompts with out of the box skills in agentforce further accelerates that process of actually building out those workflows. One customer shared how a manual process that took them a year to work on in their DIY project was operationalized in AgentForce in just a month. But probably one of the biggest areas that we found that was a challenge for customers in the DIY area was the AI guardrails and security features. So the trust layer in Salesforce provides these essential features with zero data retention, toxicity detection, the masking of data going to the LLM, all of those capabilities that enable customers to take advantage of AI and generative AI while feeling comfortable in being sure that they're going to have data security. What's interesting is we found that a lot of folks had spent a ton of time trying to figure out how to build their own trust layer within their DIY projects. Some estimated that it would have been like 20 or 30 times the efforts that it took them to configure Trust layer in Salesforce and in agentforce. Wow.

So no one got there?

No. And some of them had been trying for more than a year. Another area, obviously the user application development, that interface layer and what we found with Agent Builder and the sort of drag and drop interface, the conversational instruction dialogues, automated a lot of that UI and workflow development process so they didn't have to think about how they were actually going to present it to the end users. Customers estimated that would take them at least six times longer to develop that customer facing UI and associated workflows without Agent Force. And I mean, we're talking like weeks for folks to get prototypes up and working where they might have spent a ton of time in development effort if they were trying to do it in DIY fashion. So what we find was the data security and guardrails, but also the tuning. Right. We talked about only 50 ish percent accuracy with these DIY projects. That was a real challenge. And what we found with AgentForce, with the Atlas reasoning engine and the testing center, along with the ability to tune agents with natural language instructions, allowed business users not prompt engineers or AI experts to be able to rapidly and iteratively improve accuracy as they saw how Agent Force actually worked. Customers said they had an increase in accuracy with minor tuning. They were able to get more accuracy out of the box initially and then really being able to achieve levels they thought that it would have taken years for them to do with a DIY approach. One customer saw their accuracy go from like 60% to 85% pretty much out of the gate.

Thank you for going through all of that. There is a lot in every one of those categories. So much to unpack there. Tune in to our next video interview to learn the top three recommendations for getting started with agentic AI.