Artificial Intelligence

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.

In the world of AI, agents, and dynamic coordination, EDA is a necessity. But to let them understand, reason, and act you must think beyond request/reply and standard EDA, you...

The August 2025 Gartner® report, Innovation Insight: AI Agent Development Frameworks analyzes this evolving space, explaining the drivers behind rapid adoption, benefits and use cases. and how organizations can choose...

The convergence of edge computing and artificial intelligence demands event-driven systems that enable the evolution from centralized AI tools to distributed autonomous intelligence systems.

The competitive edge in AI isn’t just in better models or smarter prompts — it’s in connecting AI to the live, operational pulse of the business from day one.

In this article, I’ll show you how to create AI agents and run them with Solace Agent Mesh—fast to build, easy to scale, and ready for real-world use.

When it comes to agentic AI, we can either learn from the microservices journey and adopt EDA from the start, or repeat history and spend years untangling tightly-coupled systems.

The shift from interactive AI to automated AI is a fundamental change in how we work, and demands a new level of infrastructure to power it.

The future of AI lies in agile agents responding to changing environments and challenges. Solace enables that, while Kafka's limitations mean increasing confinement.

A government contractor asked Solace for help leveraging EDA and agentic AI to build a next-gen command and control system that uses AI agents to analyze information from any source.

We’re excited to announce the beta releases of Solace Standalone RAG Agent and Solace Micro-Integration for Qdrant.