Orignally Posted on LinkhedIn Here
In the evolving landscape of AI-driven tools, the introduction of Agentforce has sparked discussions about its relationship with Einstein Copilot, which launched during Dreamforce 2023.
While some might perceive Agentforce as merely a rebranding of Einstein Copilot, a deeper exploration reveals significant differences in technology and functionality.
- But do you think that’s the case?
- Do we understand the technology behind the differences?
- Even searching for Copilot in Setup it takes to
Setup > Einstein Generative AI > Agent Studio > Agents😃 so it’s easy for anyone to assume that it is just a rebranding of product.
This article aims to clarify these distinctions and highlight the advancements that Agentforce brings to the table.
Einstein Copilot: A Brief Overview
Einstein Copilot was designed as a generative AI-powered conversational assistant, utilizing a mechanism known as Chain-of-Thought reasoning (CoT) reasoning.
In this mechanism, the AI system mimics human-style decision-making by generating a plan containing a sequence of steps to attain a goal.
- With CoT-based reasoning, Einstein Copilot could co-create and co-work in the flow of the work. While this functionality allowed for a more interactive experience than traditional bots, but still Einstein Copilot had limitations.
- It struggled with true conversational depth and could not effectively redirect users if it generated an incorrect plan. It needed to use context better to respond to more user queries.
- Additionally, it faced challenges in scaling to accommodate numerous applications, which often led to degraded performance in terms of both latency and response quality.
This led to an AI experience that was not adaptive: Users could not provide new and useful information as a conversation progressed.
Enter Agentforce: A New Era of Conversational Automation
Agentforce is positioned as the first enterprise-grade conversational automation platform capable of making proactive, intelligent decisions with minimal human intervention. Looking to understand the basics, then check out -> All About Agentforce: Salesforce’s Revolutionary AI Innovation!
7 Key enhancements in Agentforce
1. Orchestration based on ReAct prompting vs. CoT
Agentforce employs a more sophisticated approach known as Reasoning and Acting (ReAct) prompting. This method involves a feedback loop where the system continuously reasons, acts, and observes until the user’s goal is achieved. This iterative process allows Agentforce to adjust to new information and ask clarifying questions, resulting in a more fluid and natural conversational experience.
2. Topic classification
Einstein Copilot didn’t have the concept of Topics. This is one of the defining features of Agentforce is its ability to classify user inputs into relevant topics.
This classification maps to specific user intents or tasks, enabling the platform to identify tailored set of instructions, business policies, and actions to fulfill requests effectively.
3. Use LLMs for responses
Agentforce enhances conversational richness by utilizing LLMs that can respond based on the context of previous interactions. This capability allows users to engage in more dynamic dialogues, ask follow-up questions, and achieve higher rates of goal fulfillment.
4. Enhanced Reasoning Capabilities
A crucial update in Agentforce is its ability to prompt LLMs to explain their reasoning behind selected actions. This feature significantly reduces the occurrence of hallucinations (incorrect or nonsensical outputs) and helps build trust between users and the AI.
With this capability, admins and developers can fine-tune the agent to align with their needs.
5. Proactive action
Agentforce can initiate actions based on user inputs as well as data-driven triggers within CRM systems or business processes. Such as
- a case status update
- an email received by a brand
- a meeting starting in five minutes.
This proactive functionality expands the applicability of Agentforce across various business environments.
6. Dynamic information retrieval
Agentforce can access dynamic information through a Retrieval-Augmented Generation (RAG) process, allowing it to perform semantic searches on both structured and unstructured data. Some use cases are:
- Using semantic search on structured and unstructured data in Data Cloud
- Information retrieval tools like web search and knowledge Q&A. This process of combining data from multiple sources lets the agent handle business tasks much more effectively and efficiently.
- Ability of agents to use Flows, APIs, and Apex classes, allows agent to tap into all processes and contextually pass the data
7. Transfer to human agent
Agentforce treats “transfer to human agent” as yet another action, which allows for a conversation to be safely and seamlessly transferred to humans in any desired business scenario. Agentfoce has baked in rules in the system prompts to prevent LLMs from digressing and hallucinating. Despite the advanced mechanisms in place to reduce errors, there are situations where human intervention is necessary, and Agentforce supports this transition seamlessly.
Final thoughts
In conclusion, while it may be tempting to view Agentforce as merely a rebranding of Einstein Copilot, it represents a substantial advancement in AI-driven conversational automation. Agentforce introduces innovative features and capabilities that significantly enhance its functionality, setting it apart from its predecessor.
If you’re seeking help or support in implementation these AI-driven Agentic solutions, please don’t hesitate to reach out to me on LinkedIn or at gouravsood.com
Explore More in this Blog Series
- All About Agentforce: Salesforce’s Revolutionary AI Innovation!
- Is Agentforce just rebranding of Einstein Copilot? This post 👆
Stay tuned for more insights, and feel free to like, share, or comment to engage in the conversation about this groundbreaking platform! 🚀