Salesforce's Agentforce platform adds security, data access, and reasoning to AI agents so they can actually work in business. It combines retrieval, vector search, and action orchestration so agents can plan and execute tasks, not just generate text. Prebuilt agents for service, sales, and coaching are ready to use.
October 29, 2024
The promise of AI agents is they'll help us work faster by enabling us to express a job to be done and doing that job autonomously on our behalf. But companies are struggling to deliver on this promise and finding that it takes much more than a large language model to make AI agents reliable for business. And here's why. A model will never truly know your business. Because your business data is always changing and a model's response will only be as current as the last time it was trained. Which means you'll need to build a lot of data retrieval systems around the model to give your agents understanding about the jobs you're asking them to do. And models, well, they have no concept of security. Once your data goes into the model, your security permissions are gone. Everyone can see everything. Which means you'll need to build security enforcement systems around the model to protect your data. And finally, a model, it can't actually take action. It can only recommend action that you'll need to execute. Which means you'll need to build action orchestration systems around the model to turn recommendations into actions in your business. And this is just the beginning of what companies need to build around a model to make AI agents reliable. But what if there was a way to skip all this and just start building an agent? Well, that is exactly what Agent Force enables your company to do. Because these missing systems are built in and built on the Salesforce platform and integrated with your customer360. Which means the only question is what agent should you build first? And agentforce helps with that to by providing a library of pre built agents like an AI service agent that answers customer questions and deflects incoming cases. Or an AI SDR agent that proactively works leads and books meetings for your sales reps. Or an AI sales coach agent that attends customer calls and provides real time tips and objection handling. Each of these agents was built on a platform and it's a platform you can use to build your own custom agents. Which you can start doing by simply describing the agent you want to build in natural language. AgentForce will then use Salesforce's understanding of your business to auto generate this agent. Then you'll customize your agents by describing the topics they can engage with and the instructions they'll use when engaging with those topics using natural language descriptions. And these form the guardrails that direct your agent's behavior. And you can test that behavior right here inside Agent Builder where you can inspect exactly how your agents do a job as they reason step by step, learning and adapting as they work but how does this work? How does AgentForce reason and learn? Well, as I mentioned earlier, at the heart of an agent's capabilities is a large language model. But the model is an incomplete solution for turning a job into an outcome in your business. Which is why AgentForce starts with data retrieval, which helps your agents get as much understanding as possible about the jobs they're asked to do. All of that context is put into a grounded prompt that is sent to the model. But we're not asking the model to do the job. Instead, AgentForce is asking the model for a plan to do the job. And then AgentForce orchestrates the execution of that plan to produce the desired outcome. But this is still an incomplete picture of how this really works. And here's why. When starting a job, it is rare that we have perfect understanding. In fact, it's only by working a job that we get a full understanding of the job. This is true for us, and it's true for your AI agents as well. Which means to be reliable, your agents need to be capable of learning and adapting as they work. And that's why we created the ATLAS reasoning engine, which coordinates all these systems into a reasoning loop that allows your agents to work a job step by step, observing, recommending, executing, learning and adapting as they navigate to the desired outcome. This is what makes agent force more reliable. But none of this is possible without access to the right data. And AgentForce has built in access to your Salesforce CRM data as well as the 0 copy external data you've connected with data cloud. These are integrations you don't need to build, but AgentForce also has built in access to vector databases to power semantic search and retrieval augmented generation, which you can use by making those semantic search results a part of the prompts used to ground the model. Which gives your agents a way to retrieve data that provides a much deeper understanding of the job to be done. And data that is protected by a trust layer that enforces your company's security policies and data that sometimes allows your agent to recognize that it doesn't know the answer and instead of hallucinating, should get a human involved, which AgentForce also supports with seamless handoffs to your employees. Because AgentForce is pre integrated with your customer 360, which means you don't need to build a lot of interface integration to create a collaborative experience between your agents and your employees. This is how AgentForce helps you deliver on the promise of AI agents by providing all the missing ingredients that make AI agents real and reliable for business to get started. Visit agentforce. Com. Thanks for watching.
