The game has changed radically. Over the past year, the industry focused on the so-called "Second Wave" of AI: Copilots. We treated Artificial Intelligence as a conversational assistant that helped us draft emails or summarize texts.
However, that vision is already falling short. We are entering the "Third Wave": the era of Agentic AI. The challenge is no longer just how to use AI for chatting, but how to become an organization where AI acts, reasons, and solves business problems from start to finish.
80% of HR leaders already project that, within five years, most workforces will be hybrid. It’s not about replacing people, but elevating them so they can manage strategy while agents scale operations.
The results from organizations that are pioneers in adopting agentic ecosystems already show an improvement of up to 33% in customer satisfaction and a significant reduction in employee burnout, who now act as supervisors of a digital workforce that operates 24/7 without friction.
An agentic company is a mature organization where autonomous AI agents and humans operate within a fluid collaborative ecosystem.
Unlike old bots that followed rigid scripts, Agentforce agents, within the context of an agentic company, possess capabilities such as:
Reasoning: They can plan and break down complex tasks.
Action: They don't just suggest; they execute (closing cases, moving inventory, processing returns).
Proactivity: They don't wait for passive instructions; they are proactive team members.
The agentic vision seeks to connect people with agents to unleash their maximum potential. It frees human teams from repetitive, low-value tasks, allowing them to focus on strategy, creativity, and the customer relationships that only they can manage.
AI handles the operational workload, while humans focus on supervising and driving productivity.
The magic of an agentic company doesn't happen by accident. It requires an intentional architecture. If you try to build this without foundations, you will fall into chaos. The key to avoiding this is context and governance.
An agent is only as smart as the data it can access. AI without context is useless and dangerous for the business. This is where Salesforce Data Cloud acts as the heart of the operation. For an agent to succeed, it needs:
Context Engineering: Connecting business rules and real-time data with generative models.
360-Degree Vision: Integrating scattered data to give agents the "memory" needed to personalize every interaction.
In an agentic ecosystem, you often need data from external partners (retailers, banks, partners). But sharing raw data is a security risk. Data Cloud Clean Rooms solve this by creating secure environments where combined datasets are analyzed without revealing sensitive information.
This is vital for industries like Open Finance or Retail Media, allowing for data monetization and insights without sacrificing privacy.
How do we trust agents that act autonomously? We cannot build operational infrastructure on technology that hallucinates. The solution is security by design, which in the Salesforce ecosystem we call the Einstein Trust Layer. This involves:
Guardrails: Strict rules that prevent the agent from taking unauthorized actions.
Simulation and Training: Before launching an agent, we use simulation environments (similar to the "eVerse" concept) to train them with synthetic conversations, avoiding risks with real customer data.
Auditability and Bytecode: Development is evolving toward generating actions (code) that are auditable and traceable, ensuring every decision is the right one.
The biggest myth is that the agentic company eliminates human work. The reality is that it radically transforms roles. We are already seeing a talent migration. For example, the role of the traditional developer or basic support agent is evolving into the Context Engineer or Agent Orchestrator.
What does this mean? New responsibilities. These profiles don't write code line-by-line; instead, they design the logical architecture, supervise data quality, and "edit" AI behavior. In this scenario, observability becomes the ally businesses need. It is now essential to have command centers to monitor agent metrics, identify recurring themes, and correct deviations in real time.
Trust, ethics, and integrity are the foundation of this new era. We cannot build operational infrastructure on unpredictable technology. The industry is responding to this with two key innovations:
To avoid AI "hallucinations," advanced training environments are being created. One example is the "Iverso" concept (publicly known as "eVerse"). Instead of training agents with customer data (which is a risk), a simulation framework is used.
Agents are trained in environments that replicate real business tasks using synthetic conversations to optimize capability and consistency—all without touching real customer data.
Trust also requires explainability. AI development is evolving toward "Bytecode": the ability to create company-grade code directly from natural language. The promise of this technology is that generated code is not only functional but also auditable and traceable. This ensures safer, explainable agents aligned with trust and ethics regulations.
Adopting the agentic company model with Salesforce and Pragma is a survival imperative based on competition and efficiency.
Attempting to build these capabilities "from scratch" (build your own), competing with integrated ecosystems like Agentforce, is not viable. You will face skyrocketing infrastructure costs, massive technical debt, and a lack of governance.
The most profitable, secure, and agile path is adopting a platform that already integrates agents, context (Data Cloud), and trust (Trust Layer). Therefore, the movement toward agentic companies has begun, and the advantage will go to the early adopters of this infrastructure.
Ready to become an agentic company? Let’s talk about how Pragma and Salesforce can help you.