Agentic AI allows companies to finally deliver seamless, consistent, and engaging customer experiences that build trust and long-term loyalty. But first, you have to break through the “friction walls.”
AI agents can search for data faster than traditional automation solutions that often suffer from latency. Even a 15-second wait time in response to a query is a latency tax that affects the customer’s experience.
Even worse, perhaps, is the contextual conflict that occurs when an AI agent gives two different answers to the same customer because it’s pulling from a marketing data silo and a logistics system that haven’t been unified.
Successfully adopting agentic AI requires organizations to integrate data in a streamlined, secure manner. This enables the technology to better understand a customer’s context and adapt processes in real time to serve — and even anticipate — their needs.
Faster, easier data integration
Traditionally, unifying data meant moving data from siloed systems into a giant data lake. However, today there are less complex and more cost-effective options available.
The rise of open interoperability standards such as the Model Context Protocol (MCP) and the Agent2Agent (A2A) protocol allow AI agents to communicate and take action across enterprise systems like Salesforce and BigQuery. A headless architecture, meanwhile, allows AI agents and custom applications to interact directly with the platform, making the browser interface optional.
This approach means that AI agents simply need to access the data to query it, which accelerates workflows and delivers customer value.
For example: A sales rep is trying to determine the best promotion they can offer a customer without dropping below the target gross margin. They might also want to suggest add-ons based on how a customer is actually using a product. Salesforce Headless 360 and Gemini Enterprise can help the rep by querying data directly without the need for the rep to go through a user interface and provide the relevant pricing policies and margin rules. This lets the rep take the best possible next action without having to touch the CRM.
The result: a seamless user experience where AI agents have the context they need to complete end-to-end workflows across enterprise systems.
When AI agents work as a dynamic duo
Breaking down data silos becomes even more strategic with a multi-agent architecture using the A2A protocol, developed by Google Cloud, Salesforce, and 50+ technology partners.
Consider a scenario where a retail customer wants to return a pair of shoes and reorder a larger size. An Agentforce Sales agent can securely access and orchestrate with a logistics AI agent to check available inventory and estimated delivery times — without the customer having to lift a finger.
Multi-agent orchestration across vendors lets companies pursue new use cases while handling back-end complexity on employees’ behalf, delivering a seamless experience.
Learn more: Google Cloud and Salesforce co-innovations in AI help organizations better integrate and manage their data for improved customer and employee experiences.