Simplify Real-Time Context Engineering for Snowflake Intelligence With Confluent

How Confluent Delivers Real-Time, Trustworthy Context to Snowflake Cortex Agents

Snowflake Intelligence brings enterprise insights to every employee’s fingertips, helping users answer complex questions in natural language with their own personalized enterprise intelligence agent. But for these agents to deliver truly accurate, contextually aware results within Snowflake’s Cortex framework, they need more than access to static or batch data—they need a continuous, trustworthy view of everything happening across the business right now. Without this real-time context, even the most advanced AI systems risk operating on stale or incomplete information.

Confluent Intelligence solves that challenge. As a fully managed suite for streaming AI, it delivers real-time, context-rich, and trustworthy data to power intelligent, multi-agent systems. Built on Apache Kafka® and Apache Flink®, Confluent Intelligence includes Streaming Agents—to build event-driven agents that act as eyes and ears for the business —and a Real-Time Context Engine, which continuously materializes enriched data sets into memory and serves them to AI systems through the Model Context Protocol (MCP) including direct integration with Snowflake Cortex agents for real-time, context-enriched reasoning.

By connecting Confluent Intelligence to Snowflake Intelligence, enterprises can provide every Cortex AI agent with trustworthy, real-time context from across their business. This enables use cases where agents are capable of delivering accurate, situationally aware insights and actions.

Why is Real-Time Context the Key Ingredient for Agentic AI?

AI’s logic now lives in its data rather than its code, meaning developers’ jobs have fundamentally changed. They’re no longer just writing instructions—they’re designing information environments. This new discipline, called context engineering, is about giving AI systems the right data at the right time to make the best possible decisions. Like briefing a human expert, an AI agent performs best when it has full context: the history of a situation, relevant documents, and live updates on what’s happening right now.

In traditional software, data prep happened offline: cleaned, batched, and delivered later. But in agentic AI, data preparation is continuous and real time. The pipeline is the application. Delivering too much or outdated data can slow performance, increase cost, and degrade accuracy. The challenge for modern enterprises is to build a continuous AI context pipeline that curates and serves only the most relevant, up-to-date information—instantly and reliably.

Many organizations have tried to solve this with various approaches, but they fall short:

• MCP-ify Everything: Exposing data sources through an MCP server and connecting it to the model is a quick way to get started, but the underlying data is chaotic and unusable for AI decision-making.

• Live API Calls: Letting AI agents call APIs from core systems provides real-time access but overloads operations, delivers unstructured data, and creates major governance risks.

• Batch Pipelines: ETL and Reverse ETL workflows improve data quality but leave context perpetually stale—never reflecting what’s actually happening right now.

How Does Confluent Help You Architect for Real-Time Context?

Confluent enables you to build intelligent, context-aware AI systems with an event-driven architecture designed to continuously collect, process, and serve data in motion. To deliver AI that acts on the most relevant, trustworthy information, Confluent provides:

• Streaming Data Capture on a fully managed data streaming platform: Ingest a continuous flow of “hot” context—the dynamic, fast-changing events from your business such as new user activity, inventory changes, or updated support tickets.

• Real-Time Stream Processing with Apache Flink®: As events stream in, they must be instantly cleaned, transformed, and enriched. This is Flink’s core strength—it performs sophisticated stateful computations, joining the live event stream with “cold” historical context from batch sources to create a complete, up-to-the-minute view of the business.

• Low-Latency Context Serving with Real-Time Context Engine: The final, processed data—your live, derived dataset representing the current state of the business—must be served to the AI agent with minimal delay. This ensures every action or decision is made using the most current and trustworthy data available.

By putting these architectural requirements into practice, Confluent not only ingests and processes streams in real time, but also makes that context immediately accessible to any AI agent and application—at scale, with memory and coordination across multi-agent systems.

Enabling Event-Driven, Context-Rich Snowflake Cortex AI Agents

Confluent turns Snowflake Intelligence into a continuously learning, event-driven AI system by integrating at three key points:

1. Push high-value, structured data into Snowflake through Tableflow.

2. Interconnect Confluent Streaming Agents with Snowflake’s Cortex agents to bridge live events and analytical insights.

3. Use Real-Time Context Engine to serve fresh business context to Cortex agents via MCP.

The result: Cortex agents capable of making proactive decisions with real-time context and responding instantly to business events—such as anomalies, operational triggers, or customer actions—using the freshest, most trustworthy information available. For example, a manufacturer can automatically rebalance incoming orders across shipping facilities by combining real-time line-sensor alerts predicting equipment failures with historical demand patterns, enabling the AI system to proactively optimize supply chain flow. The following sections provide a closer look at each of these three patterns.

1. Bridge Kafka Streams to Apache Iceberg™️ Tables in Snowflake With Tableflow

Tableflow, a fully managed service on Confluent Cloud, automatically converts Kafka topics into open table formats like Apache Iceberg™ and registers them within Snowflake Open Catalog. This makes streaming data from Confluent instantly discoverable and queryable inside Snowflake, eliminating the need for manual ETL or data movement while preserving schema and lineage.

With Flink and Tableflow, you can process, transform, and persist real-time events as Iceberg tables in object storage—keeping data cost-effective, durable, and ready for analytics or Cortex agents. It’s the easiest way to bring live, trustworthy context from Confluent directly into Snowflake’s intelligence layer.

2. Build Streaming Agents on Confluent Cloud

Streaming Agents on Confluent Cloud enable you to build, deploy, and orchestrate event-driven agents natively on Apache Flink®, allowing you to:

• Integrate seamlessly with any model, tool, and data system to reduce operational complexity—including Snowflake Cortex AI agents that depend on timely, high-quality context.

• Use fresh, contextualized data to effectively reason and take informed action the moment it’s needed, so Cortex agents and Snowflake workloads can deliver more accurate, real-time responses.

• Gain replayability to rewind the stream—whether to recover from failure, test new logic, or audit agent decisions—and iterate faster, safely.

At their core, Streaming Agents are event-driven microservices with a brain. Built on a unified platform with fully managed Flink and Kafka powered by the Kora Engine, which orchestrates event routing and decouples agents at scale, they’re designed to scale reliably to multi-agent systems—Cortex and beyond. Streaming Agents operate on the business in motion. They’re always on, continuously ingesting, reasoning, and reacting to data the moment it arrives.

Streaming Agents provide a complete, real-time AI workflow within your data streams. They integrate model inference directly into Flink SQL pipelines for LLM reasoning and retrieval-augmented generation (RAG), generating real-time embeddings to supply fresh context for semantic search and offering built-in ML functions for forecasting and anomaly detection. They also include secure and reusable connections to external systems and seamless enrichment with external tables and vector search, ensuring agents have access to the most accurate, up-to-date data while maintaining observability, traceability, and enterprise-grade security.

Finally, every input an agent sees is part of an immutable event log, so every action can be revisited. This allows you to go back through the stream to analyze trends, verify compliance, iterate on strategies, and more.

3. Deliver Fresh, Trustworthy Context to Snowflake Cortex Agents With the Real-Time Context Engine

Real-Time Context Engine unifies the serving layer for streaming data into a single managed service. It continuously transforms enriched, streaming data into structured, trustworthy context that any AI application or agent can consume instantly through the fully managed Model Context Protocol. By materializing data into a low-latency, in-memory cache for production apps—while keeping Kafka and Flink complexity under the hood—developers can securely request exactly the data they need, when they need it, without worrying about infrastructure.

Built on Confluent’s data streaming platform, the Real-Time Context Engine combines historical replay, continuous processing, and real-time serving in one place. Role-based access control, authentication, and audit logging are handled natively, so developers can focus on building intelligent systems rather than managing pipelines. Updates to upstream definitions automatically trigger reprocessing of affected data, ensuring downstream AI systems stay consistent. The result is an always-on, continuously enriched source of context that bridges the gap between experiments and production AI, powering intelligent, real-time decision-making across the enterprise.

Bring Real-Time Context to Snowflake Intelligence

Together, Confluent and Snowflake give enterprises a unified data foundation for real-time, context-aware AI. With Tableflow, high-value Kafka data is continuously written into Snowflake as Iceberg tables. With Streaming Agents, event-driven logic and intelligence can interconnect directly with Cortex, bridging the world of live events and analytics. And with the Real-Time Context Engine, Cortex agents gain instant access to live, trustworthy context via MCP.

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