Every time I get a press release about AI agents, I get a slightly queasy feeling.
It is not quite as bad as that dizzy feeling I get every time someone insists on pitching me about vibe coding, nor is it the nails on a chalkboard feeling I get every time a PR rep sends me something with the word "convo" in it when asking for an interview or discussion with one of their clients.
Also: AI agents aren’t just assistants: How they’re changing the future of work today
And yet everyone is all about agents. Microsoft did a series of announcements last week that promoted its extensive use of AI agents, not just for the enterprise, but for every Windows user. Google this week did a series of announcements that included AI agents in a wide range of applications, including writing your code. Because that’s not like letting the fox guard the henhouse — not at all.
But my real concern about agents is that they seem to be over-promised because there are so many limitations in the interaction of agents between ecosystems.
Into this crazy bouillabaisse of AI promotion and innovation, Salesforce enters with a fairly impressive dose of sanity.
Salesforce is introducing its Agentic Maturity Model, a framework that defines key stages of AI agent adoption and capabilities.
This can help give us a common vocabulary when evaluating agent offerings from the various vendors who are flooding the market.
"While agents can be deployed quickly, scaling them effectively across the business requires a thoughtful, phased approach," says Shibani Ahuja, SVP of Enterprise IT Strategy at Salesforce. "Understanding the progression of Al agent capabilities is crucial for long-term success, and this framework provides a clear roadmap to help organizations move toward higher levels of AI maturity."
See, there’s a big gap between the public’s picture of an AI agent and what’s possible.
To vendors, AI agents are pretty much anything that can follow a bunch of steps using AI capabilities. This allows vendors to AI wash almost any offering, even if the true capabilities are fairly uninspired — or as in Apple’s case with Siri, largely vaporware.
But Salesforce gives us five levels:
- Level 0: Fixed rules and repetitive tasks
- Level 1: Information retrieval agents
- Level 2: Simple orchestration, single domain
- Level 3: Complex orchestration, multiple domain
- Level 4: Multi-agent orchestration
Essentially, we’re going from basic scripts all the way up to teams of agents working in concert to accomplish complex tasks across a variety of infrastructures.
This is very helpful because then we can look at an offering and determine that, yeah, it is "agentic," but it is really not much more than a script — Level 0. Or, wow, you’re talking about an entire supply chain that’s automated, intelligent, and highly adaptive across vendors — Level 4.
Using the Agentic Maturity Model, let’s look into each of the five levels in a bit more depth.
Level 0: Fixed rules and repetitive tasks
Salesforce describes this as "automation of repetitive tasks using predefined rules, with no reasoning or learning capabilities." A great example of this is your customized email filters. There is no real AI involved whatsoever, but those rules do help get the job done.
Level 1: Information retrieval agents
Salesforce defines this as agents that go out and pull in information and, as a result of that information, recommend actions. They use the example of a troubleshooting agent, where you describe a problem, the agent does some searching, and then recommends a fix. Another example might be a shopping agent that can compare offerings and prices and make recommendations.
Level 2: Simple orchestration, single domain
Level 2 directly addresses the ecosystem issue by specifying that agentic activity take place in a siloed data environment. What this means is that all the data used is stored and available from one environment.
Level 3: Complex orchestration, multiple domain
Now we start to get to what the whole agentic AI concept promises. Salesforce describes this level as "autonomously orchestrate multiple workflows with harmonized data across multiple domains." In other words, your application will not break if you need to get data from different ecosystems or sources and integrate them using other systems.
Level 4: Multi-agent orchestration
Salesforce defines this as "Any-to-any-agent operability across disparate stacks with agent supervision."
Can we do better?
I actually quite like the five levels and Salesforce’s definition for each of them. I think they fairly represent the stages of AI agentude and what sorts of tasks they can perform. But the name of the model, Agentic Maturity Model? Well, that could be better.
Conclusion
I think this system works, and I will be referencing it as I talk about agents in the future.
FAQs
Q: What is the Agentic Maturity Model?
A: It is a framework that defines key stages of AI agent adoption and capabilities.
Q: What are the five levels of the Agentic Maturity Model?
A: Level 0: Fixed rules and repetitive tasks, Level 1: Information retrieval agents, Level 2: Simple orchestration, single domain, Level 3: Complex orchestration, multiple domain, Level 4: Multi-agent orchestration.
Q: What is the main concern about AI agents?
A: That they seem to be over-promised because there are so many limitations in the interaction of agents between ecosystems.
Q: What is the Agentic Maturity Model good for?
A: It provides a clear roadmap to help organizations move toward higher levels of AI maturity.
