AI Agents

Real-Time Data for AI Agents

AI agents make decisions based on context. Most databases update once per day. RisingWave maintains fresh features in milliseconds, enabling agents to act on live data.

Start Free SQL Streaming

Sub-100ms

Feature Freshness

Agents see updated data nearly instantaneously as events stream in

SQL

Feature Engineering

Define real-time features with standard SQL — no custom pipelines needed

Vector

Search on Live Data

Embed documents in real-time. RAG agents always get fresh context

PostgreSQL

LLM Integration

Agents query features via standard PostgreSQL drivers and SQL

The Problem

How does stale data break AI agent decisions?

When agents query outdated feature stores or knowledge bases, they make decisions on incorrect information. Stale user profiles, old inventory counts, or yesterday's market data lead to poor recommendations, missed opportunities, and security blind spots. Real-time context is essential for intelligent decision-making.

Data Point	Batch (Stale)	Streaming (RisingWave)
Feature Freshness	Hours (batch refresh)	Milliseconds (streaming)
User Spend	$500 (midnight snapshot)	$2000 (current, live)
Inventory	50 units (6 hours ago)	2 units (real-time)
Risk Score	5% (yesterday's data)	87% (current behavior)

→RAG retrieves yesterday's articles while breaking news is missing
→Inventory shows 50 units (6 hours ago) but only 2 remain
→Fraud score reads 5% risk from yesterday — customer is now high-risk
→Recommendation engine uses stale preferences, missing current session

How It Works

What is a real-time feature store and why do AI agents need it?

A real-time feature store continuously computes and maintains fresh data features for AI agents to query. Instead of batch updates, RisingWave streams events, aggregates them in SQL, and exposes live features with sub-100ms latency. Agents always see current state for informed decisions.

Real-Time Features

Ingest events, update aggregations in milliseconds. Agents query current values.

Vector Search on Live Data

Embed documents in real-time. RAG agents get fresh context for accurate answers.

Dynamic Knowledge Graphs

Update relationships instantly. Agents see current state for informed decisions.

Fresh Risk Scores

Compute fraud detection on current behavior. Block threats in real-time.

Use Cases

What use cases benefit most from real-time AI agents?

Any scenario where agents must respond instantly to changing conditions benefits from real-time features. Customer support needs live order status, trading agents need current market data, recommendation engines need user behavior, and fraud systems need real-time transaction patterns.

→Smart Customer Support — Agent sees real-time orders, history, inventory. Resolves instantly.
→Trading Agents — Monitor positions, news, market data. Hedge or capture opportunities in milliseconds.
→Recommendation Agents — Personalize based on current user behavior, not yesterday's snapshot.
→Fraud Prevention Agents — Detect anomalies as they happen. Block threats instantly.
→RAG Pipelines — Always-fresh document embeddings for accurate retrieval-augmented generation.
→IoT Monitoring — Stream sensor data to agents that detect failures before they cascade.

Frequently Asked Questions

How fresh can features be in RisingWave?

Can RisingWave handle vector embeddings?

Does RisingWave work with LLM frameworks?

How do I aggregate features in real-time?

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