Picture the perfect personal assistant. They’re probably highly knowledgeable, and if they don’t have the answer to something right away, they know where to look to find it. They understand how to take directions and carry out tasks without being guided through every step. The best assistants can also recall earlier conversations and return to past tasks without missing a beat.
Not everyone is lucky enough to have a personal assistant. But with recent developments in AI, autonomous agents are a way for everyone to have their own AI-powered “personal” assistant. Autonomous agents are emerging with the goal of giving people always-on support so they can clear their to-do lists of manual tasks and focus on more complex tasks instead.
What are autonomous agents?
There has been a significant shift toward transforming large language models (LLMs) into software entities – or autonomous agents – that can independently perform tasks to achieve specific goals rather than simply respond to queries from human users. This may seem like a minor shift, but it presents a world of new possibilities.
By merging the linguistic capabilities of an LLM with the autonomy to execute tasks and make decisions, users can leverage generative AI as an active teammate in accomplishing tasks in real time. For example, platforms like the Salesforce Platform help organizations build AI apps (like autonomous agents) to give everyone a knowledgeable, goal-oriented virtual helper who’s just one click away.
What makes autonomous agents different from traditional bots?
Autonomous agents are built on sophisticated language models that understand and generate text and perform complex reasoning to execute tasks. They’re set apart from traditional bots and frameworks by using tools and memory.
Tools act as extensions that help fill in knowledge gaps, fetch real-time data and executive tasks, and use the results to decide the next action. Meanwhile, an agent’s memory allows it to recall past interactions. This makes it possible for them to quickly pick up previous tasks from past actions.
Tools and memory are what make autonomous agents unique in the world of AI apps. Together, they create a virtual entity that’s capable of performing complex tasks and deciding on next-best actions, freeing employees to focus on issues that require human attention.
Where can you get autonomous agents?
Most companies know how to hire a human agent, but how do you find an autonomous, AI-powered one? When it comes to implementing an autonomous agent, there are several options:
- Open-source frameworks: These are frameworks that offer a comprehensive code base that can be tailored and expanded to develop reliable and effective agent apps.
- Custom development: Create a bespoke autonomous agent from the ground up and customize it to meet specific business requirements. This process includes designing the agent’s architecture, such as its processing abilities, interaction tools, prompts, memory and context, and APIs and user interfaces. Integration solutions like MuleSoft are especially helpful in this context because they allow developers to easily connect apps and systems for a more seamless flow of data, which autonomous agents rely on for delivering accurate and holistic results.
- Low-code AI platforms: Certain AI platforms provide low-code options for setting up autonomous agents. For instance, the Salesforce Platform features tools like Copilot Builder, Prompt Builder, and Model Builder to facilitate the creation of AI apps tailored to your business.
Each of these options presents its own benefits and challenges. Keep in mind that your selection will depend on your organization’s capabilities, expertise, and the specific challenges you want to solve with autonomous agents.
Getting more done with Agentforce
Agentforce is the Salesforce answer to an agent-based, generative AI conversational assistant that guides users through interacting with Salesforce.
While there are several similarities between Agentforce and an open-source agent framework, the real difference is access to Salesforce’s entire metadata platform. This allows for more accurate and relevant responses, better privacy and compliance, and no need for costly AI model training.
Agentforce also has several features for building your own AI-powered assistant:
- Prompt Builder: Craft and manage prompt templates to generate precise and personalized outputs from AI models. Build autonomous agents that can complete tasks with greater speed by creating no-code, reusable prompt templates within the secure confines of the Einstein Trust Layer. Prompt Builder also facilitates the integration of prompts with business data from CRM, flows, and MuleSoft APIs, ensuring that the AI’s responses are relevant and grounded in the company’s data.
- Agent Builder: Allow IT teams to devise custom AI actions for specific business functions. It offers low-code AI capabilities for customizing agents and integrating AI into any CRM application. It also supports the personalization of conversational AI assistants, empowering them to execute tasks and deliver insights customized to the business’s unique requirements.
Extending your autonomous agents’ reach with MuleSoft
In addition to being knowledgeable and able to take action, the best autonomous agents can also action outcomes across the organization – not just within a single system. Agents must be able to reach into different parts of your business and trigger actions so that their objective doesn’t get held up because the agent doesn’t have access to data or permissions it needs.
MuleSoft enhances the capabilities of autonomous agents in Salesforce by providing robust integration and automation tools that extend their reach past the CRM environment.
Here are a few examples of how this extended reach makes autonomous agents drive even greater efficiency not only in your CRM but across your enterprise business platforms:
- Enterprise resource planning (ERP): When a sales deal is finalized in Salesforce, MuleSoft integration ensures an autonomous agent can create a sales order in the ERP, automatically adjusting inventory levels and aligning financial records.
- Logistics: MuleSoft integration between Salesforce and logistics systems allows for the creation of autonomous agents that enhance shipping operations. For instance, when a customer’s order is entered into Salesforce, the agent could automatically relay the order details to the logistics provider, arrange the shipment, and update the customer with tracking information.
- Finance: By connecting Salesforce with financial systems, an autonomous agent could be built to enable more immediate financial reporting and analysis. For example, when a sale is recorded in Salesforce, the agent could prompt the financial system to refresh revenue figures and generate relevant financial reports, ensuring current financial insight.
Organizations can streamline more workflows, minimize manual data entry, and enhance operational efficiency by empowering autonomous agents to interact with and act on data from external systems. The connectivity provided by MuleSoft allows these virtual helpers to perform more comprehensive and impactful tasks, optimizing processes across multiple functions and ensuring a more seamless flow of insights throughout the organization.
Learn more about how MuleSoft’s integration capabilities help you build AI apps with further reach and greater connectivity in our whitepaper, Blueprint for Implementing AI.
