DataStax is reshaping the AI landscape by redefining how enterprises build and deploy intelligent agents. We are witnessing a shift where building AI agents is no longer a complex task, and almost everyone can develop their own agents without requiring much technical knowledge.
At the RAG++ Bangalore event, Mukundha Madhavan, technology lead at DataStax, told AIM that AI agents are no longer just theoretical constructs or experimental projects. “They are becoming integral to how businesses operate, and the need for robust, scalable solutions has never been greater.”
Madhavan pointed out a growing trend in which companies are seen moving from just experimenting with AI in proof-of-concept projects to full-scale production environments.
Simplifying Agent Development with Langflow
At the heart of DataStax’s innovation is Langflow – a low-code app builder for RAG and multi-agent AI applications.
“Langflow lets developers visually create workflows simply by dragging and dropping components; it uses AI development as seamlessly as possible,” Madhavan further explained. This kind of approach relieves developers from the burden of learning extensive coding skills because they can invest their time working on the function rather than dealing with technical barriers.
Langflow has included various kinds of agents, including task-oriented ones that are good at performing specific functions. DataStax released multi-agent orchestration in Langflow 1.1 to enable multiple agents to work together on complex tasks. This actually weighs quite a lot in terms of the current demand for AI systems capable of dealing with dynamic and multifaceted workflows.
Moreover, the conditional routing and multimodal inputs in Langflow allow developers to design workflows that dynamically change in response to either the nature of the inputs or certain conditions. Langflow itself has been used to create AI shopping assistants that interconnect customer information with product catalogues in real-time.
However, NVIDIA collaboration has played a huge part in improving the platform’s performance levels. Utilising NVIDIA’s optimised hardware and services, Langflow accelerates data processing by 19 times over traditional provisions.
“There is a lot of dynamism in the collaboration with NVIDIA. It is not only speed but more importantly, it also enables more advanced AI capabilities, which would be impossible otherwise,” Madhavan said.
Where JVector Comes into the Picture?
JVector is designed for speed and flexibility. Using modern graph algorithms inspired by DiskANN (Disk Aware Approximate Nearest Neighbor) algorithms allows JVector to handle large datasets swiftly while maintaining high recall and low latency. In a study done with Deep100M datasets, JVector significantly outperformed classical engines like Lucene. This makes it particularly suitable for large data application scenarios or cases that require real-time search capability.
One of the main features of JVector is its disk-aware design. It can process large datasets that exceed memory capacity. Via DiskANN-style search and product quantisation, JVector compresses vectors into memory-efficient formats while minimising input or output operations during query execution. This feature is particularly relevant for retrieval-augmented generation (RAG) workflows in which large-scale embeddings are leveraged to give contextually relevant information to generative AI models.
JVector allows simultaneous index building and scales with up to 32 threads. This enables developers to update indexes on the fly without affecting active queries, which is critical for real-time AI applications that constantly require data updates.
Moreover, utilising the Panama SIMD API, it speeds up indexing and search operations by by processing multiple data points simultaneously. This leads to faster query responses and improved throughput for enterprise-level applications.
“JVector is not just a tool – it’s a foundation for creating intelligent systems that can understand and respond to complex queries in real-time,” Madhavan aptly said.
The Role of Real-Time Data
A key differentiator for DataStax is its foundation in real-time data management. Built on Apache Cassandra, the platform addresses one of the most pressing challenges in AI development: data fragmentation. “The most common issue I come across when talking to developers and customers is fragmented data sources,” Madhavan noted. “Without a unified view of data, building effective AI agents becomes nearly impossible.”
According to Kohezion, almost 80% of organisations have over half of their data spread across various clouds or infrastructures. Companies can enhance their workflows and boost productivity by bringing these datasets together into one system with real-time access.
DataStax’s real-time data architecture ensures that agents have immediate access to relevant information. This capability is crucial for applications ranging from customer service chatbots to predictive maintenance systems in industrial settings.
This capability has already transformed industries. For example, PhysicsWallah uses DataStax’s platform to deliver personalised learning experiences to over 20 million students in India while managing a 50x surge in traffic without downtime.
DataStax is focused on expanding the potential of AI agents. The company is investigating sophisticated multi-agent systems in which agents not only work together but also learn from one another in real time.
“The future lies in ecosystems of intelligent agents that can adapt dynamically to changing environments,” Madhavan said while sharing his vision.
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