Couchbase and Arize AI are partnering to bring robust monitoring, evaluation, and optimization capabilities to AI-driven applications—delivering a powerful solution for building and monitoring Retrieval Augmented Generation (RAG) and agent applications at scale.
By leveraging Couchbase’s high-performance vector database and the Arize AI observability platform and enhanced monitoring capabilities, enterprises can confidently build, deploy, and optimize agentic RAG solutions in production, according to the vendors.
By joining forces, Couchbase and Arize AI are revolutionizing how developers build and evaluate AI agent applications. Developers can construct sophisticated agent applications by leveraging Couchbase Capella as a single data platform for LLM caching, long-term and short-term agent memory, vector embedding use cases, analytics, and operational workloads along with their favorite agent development framework for orchestrating agent workflows.
Couchbase Agent Catalog further enhances this system by providing a centralized store for multi-agent workflows within an organization that allows for storage, management, and discovery of various agent tools, prompt versioning, and LLM trace debugging.
To ensure high reliability and transparency, Arize AI provides critical observability features, including:
- Tracing Agent Function Calls: Arize enables detailed monitoring of the agent’s function calls, including retrieval steps and LLM interactions, to track how responses are generated.
- Dataset Benchmarking: Developers can create a structured dataset to evaluate and compare agent performance over time.
- Performance Evaluation with LLM as a Judge: Using built-in evaluators, Arize leverages LLMs to assess response accuracy, relevance, and overall agent effectiveness.
- Experimenting with Retrieval Strategies: By adjusting chunk sizes, overlaps, and the number of retrieved documents (K-value), developers can analyze their impact on agent performance.
- Comparative Analysis in Arize: The platform allows side-by-side comparisons of different retrieval strategies, helping teams determine the optimal configuration for their agent.
According to the companies, this integration empowers enterprises to build robust, production-ready GenAI applications with strong observability and optimization capabilities. By leveraging agentic RAG with monitored retrieval decisions, organizations can improve accuracy, reduce hallucinations, and ensure optimal performance over time.
As enterprises continue to push the boundaries of GenAI, combining high-performance vector storage with AI observability will be key to deploying reliable and scalable applications. With Couchbase and Arize, organizations have the tools to confidently navigate the challenges of enterprise GenAI deployment, the companies said.
For more information about this news, visit www.couchbase.com.