Real-time Context Engine for AI agent context serving in Confluent Cloud¶
Confluent Intelligence provides a Real-time Context Engine that is optimized to provide real-time context to AI agents, enabling low-latency data access for intelligent applications.
The Real-time Context Engine is a fully managed serving layer that continuously builds, updates, and delivers context via the Model Context Protocol (MCP). It materializes streaming data into in-memory, low-latency views that can be queried instantly, while preserving full governance, lineage, and auditability. When upstream definitions or schemas change, the engine automatically reprocesses impacted data to prevent drift, ensuring downstream AI systems remain consistent without manual intervention or rebuilds.
The Real-time Context Engine provides these key benefits:
- Continuously serves live, low-latency context, so AI decisions reflect the current state
- Unifies replay, processing, and serving, so context stays accurate even as definitions and schemas evolve
- Provides a fully managed MCP interface, so you don’t need to build your own context server
- Materializes context in-memory with full lineage for instant responses backed by replayable, governed streams
- Delivers built-in auditability and RBAC, so every context call is traceable and compliant for enterprise use
- Abstracts Kafka and Flink complexity, so you define logic once and consume real-time context immediately
Note
The Real-time Context Engine is available as an Early Access Program feature in Confluent Cloud.
An Early Access feature is a component of Confluent Cloud introduced to gain feedback. This feature should be used only for evaluation and non-production testing purposes or to provide feedback to Confluent, particularly as it becomes more widely available in follow-on preview editions.
Early Access Program features are intended for evaluation use in development and testing environments only, and not for production use. Early Access Program features are provided: (a) without support; (b) “AS IS”; and (c) without indemnification, warranty, or condition of any kind. No service level commitment will apply to Early Access Program features. Early Access Program features are considered to be a Proof of Concept as defined in the Confluent Cloud Terms of Service. Confluent may discontinue providing preview releases of the Early Access Program features at any time in Confluent’s sole discretion.
If you would like to participate in the Early Access Program, contact Confluent.
How the Real-time Context Engine enhances AI agents¶
The Real-time Context Engine enables AI agents to query the most up-to-date context, grounding their responses in real-time data. It supports structured data with lookup by primary key. The Real-time Context Engine is available to AI agents by using MCP.
Real-time Context Engine tables are always loaded in memory, so they provide low-latency response times for agent queries. AI agents require fast access to relevant data to make informed decisions and provide accurate responses. The Real-time Context Engine provides the low-latency data access needed for real-time AI agent context serving.
Best practices¶
- Design for AI workloads: Structure data for easy agent consumption
- Monitor performance: Track query latency and memory usage
- Optimize data freshness: Ensure real-time updates for accurate context
- Test with realistic loads: Validate performance with expected agent usage