Advancing Through the Salesforce Agentic Maturity Model
Orchestrate growth with Salesforce agents
On April 10, 2025, Salesforce introduced the Agentic Maturity Model, a structured framework to help IT leaders and CIOs measure and evolve the impact of AI agents across their organizations. As generative AI gains traction, enterprises are eager to harness agentic intelligence, but many still struggle with trust, ROI, and orchestration challenges.
Salesforce’s model addresses these gaps by offering a five-level roadmap that helps leaders deploy AI agents in a phased, measurable, and responsible manner.
According to a 2024 McKinsey report, companies using AI agents at scale have seen productivity improvements of up to 40%, particularly in customer support, sales, and operations. But the journey to agentic maturity is complex and that’s where this model offers clarity.
Understanding the Salesforce Agentic Maturity Model
The Agentic Maturity Model is Salesforce’s blueprint for scaling enterprise AI agents. It’s about developing intelligent agents that evolve with business needs, grow in autonomy, and work harmoniously across domains and teams.
Salesforce's Agentforce platform plays a pivotal role in the Agentic Maturity Model, offering AI-driven solutions that enhance CRM capabilities. For a comprehensive overview of Agentforce's features and benefits, refer to our detailed analysis in Agentforce: The Future of AI-Powered CRM.
The model is built on five distinct levels of maturity, each representing a significant leap in capability and business impact.
| Level | Stage Name | Description | Key Capabilities | Examples |
|---|---|---|---|---|
| 0 | Chatbots & Co-pilots | Basic bots that provide pre-fed information. No action recommendation or autonomy. |
|
|
| 1 | Information Retrieval Agents | Agents that classify intent, retrieve data, and recommend actions using Atlas Reasoning Engine. |
|
|
| 2 | Simple Orchestration (Single Domain) | Agents that can take low-complexity autonomous actions within one domain. |
|
|
| 3 | Complex Orchestration (Multiple Domains) | Agents that orchestrate across departments and systems to complete end-to-end tasks. |
|
|
| 4 | Multi-Agent Orchestration (Any-to-Any) | A collaborative network of agents working across functions under supervision. |
|
|
Key Enablers for Progression
To move up the maturity curve, organizations must invest in four foundational pillars:
| Pillar | Role |
|---|---|
| Einstein Trust Layer | Ensures security, data masking, auditability |
| Connected Data Infrastructure | Real-time, unified access to data across silos |
| Modular Automation & Flow Design | Enables plug-and-play workflows |
| ROI Measurement Framework | Tracks business outcomes like retention, efficiency, CSAT |
Evolving Through the Salesforce Agentic Maturity Model
From Level 0 to Level 1: From Static Responses to Intelligent Information Retrieval
Level 0: Organizations typically rely on basic chatbots or co-pilots that follow pre-set scripts. These bots can answer FAQs or guide users with simple instructions (e.g., password resets), but cannot interpret user intent or recommend actions.
Goal of Level 1 : Transition to Information Retrieval Agents that understand user intent and offer contextual suggestions.
What’s Needed to Advance:
- Integrate the Atlas Reasoning Engine to classify and understand user intent.
- Connect the agent to enterprise knowledge bases, CRM, or CMS for dynamic data access.
- Start building trust in AI outputs by aligning them with the Einstein Trust Layer for secure data handling.
Example: A Subscription Agent that reads CRM data to recommend applying loyalty points to a new subscription.
Challenges:
- Ensuring relevance and accuracy of retrieved content.
- Early-stage governance and compliance considerations.
From Level 1 to Level 2: From Recommendations to Action Automation
Level 1 agents can retrieve and suggest; Level 2 agents begin to autonomously act within a defined domain.
Goal of Level 2: Achieve Simple Orchestration, autonomously completing low-complexity tasks.
What’s Needed to Advance:
- Implement domain-specific action orchestration, such as creating support cases or scheduling meetings.
- Leverage Salesforce’s Flow or Apex capabilities to let agents trigger workflows.
- Introduce basic guardrails via Einstein Trust Layer.
Example: An agent that not only suggests activating a subscription but also initiates the subscription setup, sends emails, and creates support tickets.
Challenges:
- Monitoring bot-initiated actions.
- Defining clear rules for task boundaries.
From Level 2 to Level 3: Expanding from Single Domain to Multi-Domain Orchestration
Level 2 agents operate in isolated domains (e.g., CRM). Level 3 agents must coordinate across systems, CRM, billing, customer service, etc.
Goal of Level 3: Enable Complex Orchestration across multiple data and process domains.
What’s Needed to Advance:
- Establish data interoperability across enterprise systems.
- Upgrade orchestration logic to handle conditional flows and dependencies.
- Scale up data masking and access control with the Einstein Trust Layer.
Example: A Subscription Agent that creates opportunities in Salesforce, pulls billing info from a finance system, and executes personalized marketing flows—all in one session.
Challenges:
- Siloed data and system access issues.
- Complex approval workflows and privacy policies.
From Level 3 to Level 4: Toward Multi-Agent Collaboration
Level 3 agents execute workflows autonomously across domains. Level 4 agents introduce multi-agent orchestration, where specialized agents collaborate in real time under a supervisory agent.
Goal of Level 4: Enable an “Any-to-Any” model with multi-agent collaboration and coordination.
What’s Needed to Advance:
- Design agent roles: e.g., Marketing Agent, Service Agent, Subscription Agent.
- Deploy a Supervisor Agent to manage delegation, conflict resolution, and final approvals.
- Implement multi-agent observability, auditing, and performance metrics.
Example: A Subscription Agent checks with the Marketing Agent to suggest promotions, then coordinates with the Service Agent to verify product eligibility—all autonomously.
Challenges:
- Agent accountability and interaction control.
- Complex real-time data sharing across agents.
Why Advancing Matters
According to Salesforce’s State of IT Report (2024):
Advancing through this maturity model unlocks measurable ROI, improved automation, and seamless user experiences, ultimately reshaping how organizations operate in the AI era.
Final Thoughts
The Salesforce Agentic Maturity Model offers a powerful blueprint for organizations eager to embrace the next frontier of enterprise intelligence. Rather than viewing AI agents as mere tools, this model encourages businesses to see them as strategic collaborators, capable of driving automation, innovation, and cross-functional efficiency.
Each stage in the maturity journey represents a meaningful leap forward. As companies advance toward higher levels of agentic maturity, the impact extends far beyond cost savings or time reduction. By 2026, an estimated 70% of enterprises will have AI agents embedded in mission-critical processes (IDC). Those who begin investing in agent maturity today will be positioned not just to operate more efficiently, but to lead boldly in a rapidly evolving digital world.
The journey through the Agentic Maturity Model is an opportunity to design smarter systems, empower more meaningful work, and create a resilient, intelligent enterprise for the future.
Stay tuned for our next post, where we offer a diagnostic checklist and roadmap template for AI agent adoption in Salesforce.