Supercharging Data Foundations for Agentic AI with Fivetran and Redis

Why It Matters

A solid data foundation enables AI agents to act reliably at scale, cutting errors, revenue loss, and user churn, which is critical for enterprises adopting agentic AI.

Key Takeaways

• Semantic layers ensure consistent business context for AI agents.
• Knowledge graphs connect data, enabling deeper reasoning capabilities.
• Redis Iris provides real‑time context retrieval, reducing token usage.
• Fivetran’s ELT pipelines keep data fresh for continuous AI workloads.
• Open, evolvable architectures prevent vendor lock‑in and future rebuilds.

Pulse Analysis

Enterprises are rapidly moving from isolated machine‑learning models to autonomous AI agents that must understand context, retain memory, and trigger actions in real time. This shift places data at the core of AI strategy, demanding a foundation that delivers clean, governed, and instantly accessible information. Unlike traditional reporting pipelines, agentic workloads require continuous ingestion, transformation, and enrichment so that agents can make decisions without latency, turning raw data into actionable intelligence.

Fivetran and Redis framed the modern data stack as a combination of semantic layers, knowledge graphs, vector stores, and real‑time pipelines. Semantic layers provide a single source of truth, translating raw tables into business‑friendly entities that agents can query confidently. Knowledge graphs interlink entities, enabling richer reasoning and inference across disparate datasets. Vector and retrieval systems unlock unstructured content, while cloud‑native data platforms scale to meet the compute demands of large language models. Fivetran’s ELT approach automates data movement and in‑destination transformations, ensuring that the freshest data is always available for AI workloads.

Redis Iris extends this architecture by acting as a low‑latency context engine. It captures user histories, preferences, and transactional signals, then surfaces the most relevant details to agents via a Context Retriever, dramatically reducing the number of tokens needed for prompt construction. This not only speeds up response times but also improves relevance, mitigating risks such as incomplete answers or forgotten context that can erode trust. Together, these technologies position companies to deploy reliable, scalable agentic AI that drives revenue, reduces churn, and safeguards competitive advantage.