There’s a big difference between the kinds of AI agents you might use to help with specific tasks in software like Microsoft and dynamic AI agents that can integrate across an entire organization’s systems. These two approaches may both fall under the “AI agent” umbrella, but their roles and capabilities are worlds apart.
If your business is thinking about implementing AI to automate workflows, understanding these differences could help you decide what kind of solution is right for you.
Specialized AI Agents: Narrow, Yet Powerful
Microsoft, Salesforce, and other major tech companies have developed AI agents that are deeply integrated into their ecosystems, providing tailored solutions for specific business needs. For example, Microsoft’s AI agents, like the Security Copilot, automate tasks within their suite of products, focusing on security and IT operations. These agents are fantastic for improving productivity within Microsoft’s software, handling tasks like triaging alerts or analyzing manufacturing data, all within the boundaries of Microsoft’s environment.
Similarly, Salesforce has developed its own AI agent, AgentForce, which is designed to work within Salesforce’s platform. This tool automates a variety of customer relationship management (CRM) tasks, such as scheduling, data entry, and even handling basic customer queries. Salesforce’s AI agents use natural language processing (NLP) to streamline workflows and improve user experiences.
Other companies, like Oracle, have their own task-specific AI agents, such as the Miracle Agent embedded in Oracle Fusion Applications.
But there are limitations to these types of AI agents. They’re highly specialized, designed to handle specific tasks within the confines of their platforms. For example, Salesforce’s AgentForce is great if you’re heavily invested in Salesforce, but it won’t do much if you’re using a different CRM or need to pull data from external systems. Similarly, Oracle’s Miracle Agent is fantastic within the Oracle suite, but if your systems rely on other software or databases, integration can be tricky.
Multi Agent Systems: Broad, Flexible, and Tailored
On the other hand, tailor made or agnostic AI agents, like Beecker’s, take a much broader approach, designed to operate across various systems within a company. These agents are engineered to integrate seamlessly with everything from your ERP system to HR databases and even external services like tax agencies or social security systems. They can move information from one system to another, validate data, and trigger actions based on business rules.
For example, an AI agent could automatically pull HR data from your internal systems, validate it with external tax authority databases, and update your payroll software accordingly. These kinds of agents are designed to work across different departments and platforms, making them versatile and adaptable to your business’s unique needs. In fact, they could even interact and share information with other agents. This is exactly what we offer at Beecker.
While tools like Salesforce’s AgentForce and Oracle’s Miracle Agent are incredibly useful for streamlining specific processes within their ecosystems, a custom-built AI agent is much more flexible. If your organization relies on a combination of different software systems (such as ERP, CRM, or HR tools), a custom AI agent can be designed to seamlessly connect those systems, ensuring data consistency across the board.
What’s the Big Difference?
- Scope: Specialized agents like Microsoft’s or Salesforce’s are great at automating tasks within their ecosystem but are limited outside of it. Custom-built agents, on the other hand, offer a much broader scope, able to handle complex workflows across various systems.
- Integration: While agents like Salesforce’s AgentForce work well within the Salesforce platform, custom-built agents are designed to connect disparate systems, ensuring smooth data flow between platforms like your ERP, HR, and external services such as tax or social security authorities.
- Flexibility: Specialized agents are limited to predefined tasks and environments, while custom-built agents are adaptable to your specific business processes, ensuring that they align with your unique needs and operations.
Building AI Agents: The Realities of Budget and Complexity
Recently, many businesses have approached us with the idea that they can build an AI agent themselves, often with a minimal budget. While it’s true that creating a simple AI agent for a single, straightforward task is possible, this is where the misconception begins. These self-built solutions might work for tasks like automating one part of a process, but they lack the depth and flexibility required for more complex, multi-step workflows. At our company, the AI agents we develop are multi-agent systems—meaning they are capable of performing multiple tasks, integrating across various systems, and handling data validation, decision-making, and real-time actions. This complexity requires not just technical expertise, but a significant investment in time, resources, and infrastructure. Simply put, while a single-task agent may fit within a small budget, building an agent that can perform a wide range of tasks across your business systems—ensuring accuracy and adaptability—is a much larger undertaking, requiring a strategic approach and a bigger investment.
Multi Agent Systems vs Specific AI Agents, Which Is Right for Your Business?
It really depends on your company’s specific needs. If you already rely on Microsoft, Salesforce, or Oracle for your day-to-day operations and need automation within those systems, specialized agents will likely do the job. They’re easy to integrate and handle specific tasks efficiently.
But if your business needs to automate processes across multiple platforms, from finance to HR to tax compliance, a more complex AI agent (or multi agent system) may be the best choice. These agents offer much more flexibility, capable of automating entire workflows across different systems, ensuring data consistency, and reducing human error.
With the differences between specialized and complex AI agents in mind, you can now make an informed decision on which solution is the best fit for your business.