AI Infrastructure

Real-Time Data Infrastructure for AI Agents

AI agents need live data to make accurate decisions. RisingWave maintains always-fresh materialized views that agents query via SQL — enabling the observe-think-act loop with sub-100ms latency and PostgreSQL compatibility.

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Sub-100ms

Query Latency

Pre-computed materialized views deliver instant results for agent observe-think-act loops

PostgreSQL

Native Protocol

Any agent framework that supports PostgreSQL can query RisingWave directly — no custom connectors

Incremental

Computation

Views update automatically as source data changes — no refresh commands or batch schedules

Exactly-Once

Semantics

Internal checkpointing ensures agents never see duplicate or missing data in materialized views

Why Streaming

Why do AI agents fail when data infrastructure is stale?

AI agents operating on batch-refreshed data make decisions based on minutes-old or hours-old snapshots. This staleness causes incorrect actions, missed opportunities, and compounding errors in agentic workflows where each step depends on the accuracy of the previous observation.

Factor	Batch ETL for Agents	RisingWave for Agents
Data Freshness	15-60 min stale	Sub-second fresh
Query Latency	Varies (cold start)	Sub-100ms (pre-computed)
Agent Integration	Custom connectors	PostgreSQL protocol
State Consistency	Eventually consistent	Exactly-once semantics
Operational Burden	Scheduler + monitoring	Zero orchestration
Scaling	Batch job sizing	Elastic streaming

→Stale observations cause cascading errors in multi-step agent workflows
→Polling raw data sources creates load spikes and unpredictable latency
→Batch pipelines require orchestration, scheduling, and monitoring overhead
→Pre-computed views eliminate cold-start latency for agent queries

How It Works

How does RisingWave power the agent observe-think-act loop?

RisingWave continuously ingests streaming data and maintains incrementally-updated materialized views. AI agents observe by querying these views via PostgreSQL protocol, think using their LLM reasoning, and act on always-current state — completing the loop in milliseconds rather than minutes.

Observe: Query Fresh Views

Agents read pre-computed materialized views via standard SQL — no custom connectors, no polling, just SELECT queries

Think: Accurate Reasoning

Fresh data means accurate LLM context. Agents reason over current state, not stale snapshots from the last batch run

Act: Confident Decisions

Sub-100ms query latency enables tight feedback loops where agents can verify state before and after taking action

Loop: Continuous Operation

Views update incrementally as source data changes, so the next observation is always current without agent-side caching

Architecture

What makes RisingWave different from batch-based agent data layers?

Batch ETL pipelines introduce inherent staleness, operational complexity, and unpredictable refresh windows. RisingWave replaces this with continuous incremental computation, delivering always-fresh results through a standard PostgreSQL interface that any agent framework can connect to.

→Continuous incremental computation replaces scheduled batch refreshes
→Standard PostgreSQL protocol works with LangChain, CrewAI, AutoGen, and custom code
→Exactly-once semantics ensure agents never see duplicate or missing data
→Elastic streaming scales with agent workload without manual intervention
→Zero orchestration eliminates Airflow, schedulers, and monitoring pipelines

Frequently Asked Questions

What latency can AI agents expect when querying RisingWave?

How does RisingWave connect to AI agent frameworks?

Can RisingWave replace a batch ETL pipeline for agent data?

Does RisingWave support exactly-once semantics for agent-critical data?

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