Salesforce recently launched Agentforce to streamline customer and employee interaction within each Salesforce cloud. Agentforce agents are self-sufficient, proactive applications that perform specialized tasks to assist employees and customers. This post will explain Agentforce, its core concept, and its process for taking action.
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Agentforce agents use Large Language Models (LLM) to analyze and understand the full context of customer interactions. Based on the context, they decide on the next step to take. The best part is that these agents use trusted business data, such as Salesforce CRM data and external Data Cloud data, to generate responses that are consistent with the company’s brand voice and guidelines.
Key Components of an Agentforce Agent
Agentforce Agent has five core components to complete the analysis and provide the next step. Below are those core components
- Role—An agent’s purpose is represented by his or her role. This role defines the task at hand and the overall goals that the agent should achieve on your team.
- Knowledge – Knowledge is the data that an agent requires to be successful. This could include company knowledge articles, CRM data, external data obtained through Data Cloud, public websites, and so on.
- Actions—An action is the goal that an agent can achieve. An agent can perform a predefined task in response to a trigger or instruction. For example, it could execute a flow, prompt template, Apex code, or any external API call.
- Guardrails—Guardrails are best practices for designing and implementing a successful application. They are the guidelines that an agent should follow. These could be natural-language instructions telling the agent what it can and cannot do, when to escalate to a human, or security features built into the Einstein Trust Layer.
- Channels – Channels are the applications in which an agent performs work. This could include your website, CRM, mobile app, Slack, and other platforms.
What is the brain of an Agent?
The Atlas Reasoning Engine is the brain of Agentforce agents. Users can train agents to respond naturally and adapt to situations. This will make them faster, more versatile, and more capable. Topics improve accuracy by categorizing requests into specific categories with defined scope and rules. I also define which actions an agent can and cannot perform. Below are key features of Atlas Reasoning Engine.
Multi-turn chat: The reasoning engine enables interactive communication with users by taking into customer/employee accounts and adapting to added conversational context. This will improve the accuracy of the service provided by the agent.
Topic classification: The reasoning engine categorizes user utterances into topics based on predefined descriptions, ensuring appropriate responses. Based on the categorized topic, the user will get the correct response from an Agentforce agent.
Instructions and actions: Each topic includes specific instructions and actions, such as confirming order details or obtaining additional information, to help users accurately and effectively.
Knowledge retrieval: The reasoning engine employs various strategies, such as advanced retrieval augmentation generation (RAG). This selectively applies many language models to improve the quality of queries over time by retrieving the most relevant knowledge chunks and assessing the response’s quality.
Searchable public data: Agents now have safe access to public data via the Einstein Trust Layer, which broadens their knowledge base. Einstein Trust Layer will handle the security of customer data.
Out of Box Agents
Agentforce provides several out-of-the-box agents to handle customer/employee interactions. These agents can be easily customized using Salesforce flow; no Apex code is required to customize them. We can use Apex to enhance functionality or add features to them. Out-of-the-box agents can be set up within a minute and are scalable with growing businesses.
Below are a few out-of-the-box agents we can use to enhance customer interaction.
- Service Agent—A Service Agent utilizes advanced AI technology to replace conventional chatbots. This enables the management of various service issues without relying on preprogrammed scenarios, thereby enhancing the efficiency of customer service.
- Sales Development Representative (SDR) – The Sales Development Representative (SDR) interacts with prospects around the clock, addressing inquiries, handling objections, and coordinating meetings utilizing CRM and external data. This enables your sales team to concentrate on fostering stronger customer relationships.
- Sales Coach—The Sales Coach offers customized role-play sessions for your sales team. It leverages Salesforce data and generative AI to assist sellers in practicing pitches and objections that are specifically aligned with individual deals.
- Merchandiser – The merchandiser supports your ecommerce team by facilitating site setup, establishing goals, creating personalized promotions, crafting product descriptions, and providing data-driven insights, thereby streamlining daily operations.
- Buyer Agent – Buyer Agent improves the B2B purchasing process, assisting your buyers in discovering products, completing transactions, and monitoring orders through chat or within sales portals.
- Personal Shopper – A Personal Shopper functions as a digital concierge across your ecommerce platforms or messaging applications, providing tailored product recommendations and aiding with search inquiries.
- Campaign Optimizer – Campaign Optimizer automates the entire campaign lifecycle, leveraging AI to analyze, generate, personalize, and optimize marketing campaigns in alignment with business objectives.
Why agents are better than chatbots?
Conventional bots (Einstein Bots Chat) work as pre-configured assistants. They can only respond to questions they have been programmed to answer. If they lack knowledge on a topic, they will provide a standard response.
On the other hand, Agentforce agents leverage Large Language Models (LLMs) and generative AI to understand the complete context of customer interaction. They independently identify and implement suitable actions, including handling requests or addressing concerns, in accordance with your organization’s CRM data and brand standards.
How an Agent Takes Action
Agents implement actions and follow established guidelines through natural language descriptions defining tasks and operational limits. This document provides an overview of their operational procedures. The following is a brief description of their operations.
- The agent initially receives a trigger, which may involve a discussion with an employee or customer, a modification in data, or an automation process.
- The agent leverages the LLM and natural language descriptions to ascertain the context and determine the most suitable topic for the task at hand, including the scope, required data, and essential conditions.
- An agent determines and organizes actions according to the specific task requirements. These operations are executed via flows, Apex classes, APIs, or direct prompts.
- Agents strategically design and execute tasks in alignment with predefined protocols. The systems feature cohesive mechanisms for identifying harm and toxicity, leveraging the Einstein Trust Layer to guarantee the prevention of unsuitable or detrimental actions.
Why business should use Agentforce?
- Agentforce will increase customer experience by quickly and correctly responding to customer queries. It will respond to queries 33% more accurately and 2x more relevant than traditional chatbots.
- Agentforce can handle growing customer service demands without performance degradation. It scales easily across multiple support channels, including chat, email, phone, and social media, offering a unified agent experience.
- As business grow, Agentforce adapts accordingly, enabling you to manage a greater number of cases without a corresponding rise in operational expenses. This scalability contributes to sustaining profitability and facilitating revenue growth.
Availability and Pricing
Agentforce for Service and Sales will be generally available on October 25, 2024. Agentforce pricing begins at $2 for each conversation, and volume discounts are available.
References
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