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Organizations have spent centuries figuring out how to hire, train, supervise, and organize human talent. Solace Agent Mesh is how that wisdom gets applied to AI agents.
Agent mesh observability requires more than traditional microservices tooling. Solace Agent Mesh adds built-in visibility into LLM latency, token usage, and dynamic execution flows with interactive tracing designed specifically for...
Agent Mesh provides two integration paths that enable existing LangGraph agents to participate in the mesh while the broader architecture matures.
Solace Agent Mesh gives you two ways to coordinate agents: Orchestrator is backed by a LLM and when it receives a task, it decides at runtime which agents to invoke,...
RFQ is a structurally complex process, not a simple workflow. The multi-system, multi-party, exception-heavy nature of quote generation makes it a poor fit for rule-based automation and a strong fit...
In Solace Agent Mesh, entrypoints (formerly known as gateways) are external-facing interfaces that connect your agent mesh to the outside world. They sit between your users, or your systems, and...
Without connections to your databases, APIs, knowledge bases, and enterprise systems, even the most sophisticated agent remains limited to general knowledge -- this is where data connectors become essential.
Solace Agent Mesh provides the real-time context and orchestration layer that SAP enterprises need to get agentic AI from pilot to production.
In this blog post, I dive into Solace Event Portal MCP Server and explain how to integrate it with Solace Agent Mesh.
Many enterprise AI agents fail in production due to fragile integrations and tightly coupled workflows; this post explains how Solace Agent Mesh provides an event-driven foundation for resilient agent orchestration,...
The future of multi-agent AI architectures isn't about sending more data to LLMs, it's about sending the right information at the right time. And with Solace Agent Mesh, that future is...
In Solace Agent Mesh, an agent is the main processing unit that receives prompts, executes a couple of LLM-supported actions, and gives the response back to the original requestor.
