How Salesforce's -Level Framework Cuts Through the AI Agent Hype

AI agents have quickly become one of the hottest buzzwords in tech, with every major player from Microsoft to Google announcing new initiatives in this space. But with so many different claims and conflicting definitions, it’s easy to get lost in the hype. Salesforce, however, stands out by introducing a more grounded, practical approach through its Agentic Maturity Model. This framework helps companies better understand the true potential of AI agents, cutting through the noise and offering a roadmap to successful implementation. Let’s dive into how this model works and what it means for the future of AI agents.

Salesforce’s Agentic Maturity Model provides a structured, five-level framework to assess the capabilities of AI agents, from simple automated scripts to complex multi-agent orchestration systems. It’s an essential tool in helping businesses separate genuine AI advancements from simple automation tools that may be falsely labeled as “AI.” Here’s a breakdown of each level and what it means for businesses adopting these technologies.

Level 0: Fixed Rules and Repetitive Tasks

At this foundational level, AI agents are nothing more than scripts executing predefined actions based on fixed rules. Think of basic automation like email filters that sort your messages into folders or automated social media updates. These systems don’t learn or adapt; they just perform the same task repeatedly without any intelligence.

While this might seem rudimentary, it’s still a critical part of the AI landscape. Basic automation forms the foundation for more sophisticated agents by freeing up time from mundane tasks. However, to progress beyond Level 0, AI needs to evolve to handle more dynamic and adaptive actions.

Level 1: Information Retrieval Agents

These agents go beyond basic automation by pulling in information from various sources and making recommendations based on that data. A prime example might be a troubleshooting agent that searches through databases or the web to identify a problem and suggest a solution. While these agents can be useful for specific tasks, they still operate within a limited scope and often rely on data within their own ecosystems.

One key takeaway here is that information retrieval agents work best within controlled ecosystems, like Google or Microsoft, where they can access and pull data from specific platforms. Outside these ecosystems, retrieval capabilities might be limited by the availability of data.

Level 2: Simple Orchestration, Single Domain

Level 2 agents are capable of orchestrating actions across a single data domain. This means they can interact with a variety of data within one platform or application. A good example is Notion’s AI, which can automate tasks and provide insights based on the notes and documents stored within the platform. However, it can’t operate across multiple platforms or integrate data from external sources, which remains a major limitation at this stage.

The power of these agents lies in their ability to automate tasks within a siloed environment, making them valuable for organizations that rely heavily on one platform or set of tools.

Level 3: Complex Orchestration, Multiple Domain

Here, the true promise of AI agents begins to take shape. Level 3 agents can orchestrate complex workflows by pulling data from multiple sources and integrating it to perform more sophisticated tasks. This could involve using APIs to connect different systems or even employing screen-reading techniques to simulate human interaction with different platforms.

While this level holds great potential, the complexity of managing multiple systems and the need for cooperation between vendors can create significant challenges. Integration is key, and without it, agents may struggle to perform at their best.

Level 4: Multi-Agent Orchestration

At the pinnacle of the framework, Level 4 involves the coordination of multiple AI agents working together across different platforms, stacks, and infrastructures. This is where things get truly complex and powerful, with agents able to perform a wide variety of tasks in tandem to achieve a common goal. For example, one AI agent might scan news articles, another could generate content, and another could handle the distribution—all working seamlessly together.

This is where Salesforce’s framework really shines, as it shows just how far AI agents can go when they’re not just performing individual tasks but working collaboratively to handle large-scale, multifaceted operations. However, this level of sophistication is likely to remain limited to large enterprises with the resources to support such complex systems.

What Undercode Says:

The landscape of AI agents is rapidly evolving, and Salesforce’s 5-level framework provides a clear and useful structure for understanding where different AI solutions stand. The key takeaway here is the distinction between genuine AI advancements and what might be AI-washing, where companies slap the “AI” label on simple automation tools that don’t truly embody the intelligence we’re expecting from next-generation agents.

Looking at the current state of AI agents, many vendors are still operating in the Level 0 to Level 1 space, offering basic automation or information retrieval with little to no true learning or intelligence involved. This leaves a lot of room for growth as the technology continues to mature, but it also highlights the need for more careful scrutiny of what’s being marketed as “AI.”

The true potential of AI agents, however, lies in Levels 3 and 4—complex orchestration and multi-agent collaboration. These are the levels that promise real value, particularly in enterprise settings where large-scale workflows and cross-system integration are critical. But as Salesforce points out, reaching this level of sophistication requires a well-thought-out, phased approach. Organizations that rush into agent adoption without understanding the underlying capabilities and limitations are setting themselves up for failure.

As we look to the future, we’ll likely see a divide between companies that are leveraging AI agents to streamline operations and those that are still stuck in the realm of basic automation. The distinction between these levels will be increasingly important, not just for developers and companies building these systems, but also for consumers and businesses looking to adopt them.

Fact Checker Results:

Claim: Salesforce’s framework offers a structured approach to evaluating AI agents.
Verdict: True. The 5-level maturity model helps define AI capabilities and separates genuinely intelligent agents from basic automation.
Claim: Multi-agent orchestration (Level 4) is unlikely to become mainstream outside of enterprise settings.
Verdict: Likely. The complexity and resources required for Level 4 systems make them more suited for large enterprises.
Claim: Most AI agents today are still limited to Levels 0-2, often relying on basic automation or siloed data.
Verdict: True. Many AI agents currently on the market don’t yet provide the advanced capabilities promised by higher levels.