Content about artificial intelligence, unified context, customer service, and the systems that follow the customer journey.
Six perspectives on connecting conversations, events, and operational information to the context used by AI agents.
The new version of Secretar.AI arrives on September 8 with Unified Context™, connecting information from the customer journey to provide continuity in AI-powered service.
Learn the concept, the information that can make up the context, and how Unified Context™ applies to the work of AI agents across systems.
A customer’s relationship with a company spans channels, documents, and events. We created Unified Context™ to connect that context to the work of AI.
When information exists within the company but does not reach the person providing service, customers have to reconstruct their own story. Learn how to recognize this problem.
Omnichannel aims to provide continuity across channels. Unified context connects operational information that helps sustain this experience in AI-powered customer service.
Each stage adds information to the customer relationship. Learn which records matter and how they can support continuity in AI-powered customer service.
How Unified Context relates to memory, RAG, context engineering, customer data, omnichannel, MCP, and multiple AI agents.
Agent memory retains information across interactions. Unified Context focuses on connecting the information belonging to an entity across integrated systems.
RAG retrieves information to support generation. Unified Context addresses the connection between an entity and its information across integrated systems.
Context engineering shapes what an AI model receives. Unified Context focuses on connecting entity information that can inform that model's work.
Customer 360 and Unified Context share an interest in connected customer information. Their useful comparison concerns scope and operational use.
Omnichannel coordinates continuity across channels. Unified Context focuses on connecting entity information across integrated systems. Their capabilities can overlap.
CDPs and Unified Context can overlap in customer data unification. The useful comparison is which component owns identity, context and access for each workflow.
MCP defines a connection protocol for AI applications and external capabilities. Unified Context addresses how information about the same entity is related.
Sharing context across agents requires more than forwarding a conversation. Follow a conceptual workflow that preserves entity references, source evidence and task scope.