We all know that AI agents can make our jobs easier – they can take a clearly defined task that needs to be done and carry it out on their own. However, many companies are gradually discovering that for these agents to be truly reliable business partners. Therefore, it is not enough to use an advanced language model. And here is the key to understanding the whole thing.
Why LLM models are not enough
A large language model (LLM) on its own can never fully capture the specifics of your business. Business data is constantly changing, and the model’s responses will only be as accurate and up-to-date as its most recent training. In order for the model to properly understand what you expect it to do, it is also necessary to create a large framework of data and context around it.
In addition, you will need to design and implement a series chinese overseas canada database of retrieval systems along with an appropriate data structure to help your agents better understand the requirements and tasks you are giving them. Retrieval systems (or search systems) in the field of AI are technologies and methods designed to search, extract, and present relevant information from large data sources. This means you will need to create mechanisms for effective coordination and specific actions.
An easy path to your own AI agents
And that’s just the beginning of what companies need to build . Therefore, around a model to make AI agents reliable.
Why LLM models enough?
That’s where AgentForce comes in – a solution that has all of these missing systems built in.
For example:
A service agent answering customer inquiries and redirecting incoming requests.
An SDR agent who actively reaches out to potential customers and schedules meetings for your sales representatives.
An agent sales coach who assists during customer calls, provides real-time advice and helps resolve the other party’s objections.
Simply describe in Salesforce what kind of agent you want to create in natural language, and AgentForce will automatically generate the agent based on your description. You can then further customize it – for example, define the topics the agent should handle and add instructions to them that it should follow. Finally, you add constraints that determine the rules of its behavior. You can test the resulting agent behavior directly in the Agent Builder tool, where you can easily verify how your agent works, gradually learning, adapting and improving.
How does AgentForce work?
The basic building block of an agent’s capabilities is a large how to make wishes come true language model. But as we mentioned, it would not be enough on its own.
AgentForce begins by analyzing available data through retrieval systems that provide agents with as much relevant information as possible to perform their tasks. Its goal is to ensure that the model generates answers that are accurate, relevant, and supported by valid information.)
The model is not asked to directly perform the task. Instead, AgentForce obtains a detailed plan from the model on how to perform the task, and then executes that plan to achieve the desired result.
Why LLM models enough
And that’s not all. To be reliable, agents need to learn and adapt as they work. It allows agents to repeatedly work on thailand data a task, step by step improving their recommendations, learning and adapting to new situations.
Data access
In addition, it can access external data connected via the Data Cloud , which currently offers more than 200 connectors for integration with third-party applications and systems. You don’t nee