Salesforce Release Agentic Maturity Model to Support Agentforce Implementation

Salesforce today announced the release of the Agentic Maturity Model – a new framework designed to help CIOs better understand and measure the success of AI agents.

AI is becoming a regular fixture in workplaces, and 84% of CIOs believe AI will be as transformative as the internet. However, there’s been a lot of discussion around technical implementation issues with Agentforce and how CIOs can assess its true value. This new framework certainly indicates that more support is on the way as businesses continue to scale their use of AI agents.

What Is the Salesforce Agentic Maturity Model?

The Agentic Maturity Model is a structured roadmap that will guide CIOs through the four different stages of adopting and scaling AI agents effectively.

As Agentforce continues to find its feet within the ecosystem, this structured approach means businesses can lead with confidence about how their agent is performing, how they can improve it, and where they can identify an ROI.

“Understanding the progression of AI agent capabilities is crucial for long-term success,” said Shibani Ahuja, SVP of Enterprise IT Strategy at Salesforce. “While agents can be deployed quickly, scaling them effectively across the business requires a thoughtful, phased approach. This framework provides a clear roadmap to help organizations move toward higher levels of AI maturity.”

This, of course, has been an ongoing issue for CIOs since the release of Agentforce. A lack of clear guidance has left many to lead with a “patchwork” approach, leaving individuals to work out themselves how mature their agent really is.

Alongside this, the disbanding of the Well-Architected Program has been another significant talking point, with many in the ecosystem arguing that Salesforce made the wrong decision by removing this framework.

However, this Maturity Model is a proactive step towards providing more guidance on how to govern and assess agents effectively, ensuring that you have the best idea of how your agent is performing.

The Four Levels of Agentic Maturity

The four levels of Agentic Maturity outline a clear path for organizations to follow, from basic automation to advanced, autonomous, multi-agent collaboration.

Level 0: Fixed Rules and Repetitive Tasks (Chatbots and Co-Pilots)

The ground level identifies those still using chatbots and co-pilots to handle basic inquiries. While capable of automating tasks, this automated tool is still unable to recommend actions or take further steps in the same way an agent can.

To advance from level 0 to level 1:

• Work out where chatbots and co-pilots are limited and explore opportunities for reasoning capabilities.
• Measure how much time automation saves your team.
• Choose initial projects based on your risk tolerance and create plans to manage these risks.
• Start with clean, unified data sources and prepare for broader integration.
• Prioritize improving customer experience and value over saving time.
• Track early success with clear metrics like faster responses and reduced costs.

Level 1: Information Retrieval Agents

This next level means your agent is now capable of assisting you day-to-day, retrieving relevant information, and recommending actions.

For example, the agent may pull data from a knowledge base to suggest the next steps for a customer, recommending troubleshooting articles or escalating it to a human.

To advance from level 1 to level 2:

• Move from agents suggesting actions to agents taking actions.
• Clean and unify additional data sources as you integrate them.
• Set clear guidelines, testing, and ongoing monitoring for your agents.
• Plan how you’ll collect and use user feedback.
• Ensure customer-facing agents match your brand standards.
• Track success with metrics like time savings, fewer support cases, and higher customer satisfaction.

Level 2: Simple Orchestration, Single Domain

Level 2 identifies that your agents can autonomously complete low-complexity tasks in a siloed data environment. For example, an agent who can use data from your internal calendar and email system to schedule meetings or write follow-up emails.

To advance from level 2 to level 3:

• Choose between one advanced agent or multiple specialized agents working together and consider performance impacts.
• Give agents appropriate access, similar to employees, but limit permissions to what is necessary.
• Build your system to handle future growth and complexity to avoid frequent rebuilds.
• Use standard APIs to easily expand agent capabilities across different systems.

Level 3: Complex Orchestration, Multiple Domain

The next level means that your agent can autonomously create multiple workflows from data across different domains. This could mean your agent handles complex cross-department workflows like sales pipeline management while pulling data from CRM, customer service tickets, and financial reports.

To advance from level 3 to level 4:

• Focus on use cases that need cross-domain agent collaboration, optimizing complex workflows with agent teams.
• Set up a common communication system (like APIs) so agents can collaborate easily.
• Enable agents to register, update, or remove themselves automatically for easier management.
• Build a flexible system that allows any agent to interact with any other.
• Create clear access controls, security rules, and guidelines for monitoring and transparency.
• Establish human oversight alongside AI based on risk levels.
• Plan long-term maintenance and management of AI agents.
• Measure success through efficiency, effectiveness, user experience, and reduced risk.

Level 4: Multi-Agent Orchestration

The final level means you have an “any-to-any-agent” that operates across disparate tasks and is fully supervised by an agent.

To maximize your level 4 agent:

• Improve security and governance for wider collaboration.
• Identify new business opportunities from agent teamwork.
• Create metrics to measure the value of multiple agents working together.
• Set up feedback loops to enhance agent interactions.
• Track success by measuring business outcomes like revenue growth, cost savings, and customer retention.

Getting Ready for Agent Implementation

While this framework provides CIOs with the guidelines they’ve been missing, Shibani also emphasized the importance of businesses being “agent-ready”.

“Organizations must look beyond the technology and consider the broader organizational impact,” Said Shibani. “This includes data readiness, security, and the need for a collaborative human-agent workforce.”

This touches on something we recently covered on Salesforce Ben about Agentforce’s current slow growth and adaption. For Agentforce to grow, it’s not just Salesforce who needs to put the work in, but also the ones looking to implement it.

For agents to grow and develop – as the Maturity Model intends – businesses have to ensure that their data is reliable and their org is clean.

As Ben McCarthy, Founder of Salesforce Ben, put it: “Loads of Salesforce orgs were built the ‘quick and cheap’ way, which isn’t scalable. Before a company can take on AI, they often need to clean that up. There are also data issues, broken processes, and understaffed teams – all of which make it hard to adopt something like Agentforce.”

So, as Salesforce takes the step towards providing the requested guidance, it’s also now time for organizations to ready-up and prepare their org for agents so they can work alongside this Maturity Model.

Final Thoughts

A thorough and rigid guideline for agentic growth should go a long way in helping accelerate the use and adoption of Agentforce across businesses, as CIOs can now use this framework to really tell what direction they should be heading towards.

Make sure to leave your thoughts in the comments below.