Real-Time Data Infrastructure for Autonomous AI Agents

Autonomous AI agents are no longer a research curiosity. Teams are deploying agents that monitor infrastructure, answer customer questions, execute trades, and coordinate multi-step business workflows with minimal human supervision. These agents need to perceive the world, remember what they have learned, and act through external tools, all in real time.

The bottleneck is almost never the language model. It is the data infrastructure underneath. An agent that reasons correctly but works with stale context makes bad decisions. An agent with fast retrieval but no persistent memory cannot learn from past interactions. An agent with great reasoning but no way to call external tools is just a chatbot.

This article breaks down what data infrastructure autonomous AI agents actually need, maps each requirement to a specific category of technology, and shows how RisingWave fits as the fresh-context layer in a production agentic architecture. All SQL examples are verified and runnable.

The Four Infrastructure Requirements of Autonomous Agents

An autonomous agent operates in a loop: perceive context, reason, act, and remember the outcome. Each phase has a specific data requirement.

1. Fresh Context (Streaming Database)

When an agent decides what to do next, it needs to know the current state of the world. Not the state from an hour ago. Not the state from the last batch run. The state right now.

A fraud detection agent needs to know that this card has already been charged three times in the past 90 seconds. A customer support agent needs to see that the user just hit a 500 error 30 seconds ago before they opened the chat widget. A trading agent needs the latest bid-ask spread, not the price from the previous minute's snapshot.

Traditional databases handle this poorly. You can query a relational database for current state, but if you need derived signals (error rate over the last 5 minutes, average response latency per API endpoint, user activity score for the current session), you either run expensive aggregations on every request or rely on batch jobs that introduce minutes or hours of lag.

A streaming database maintains these derived signals continuously through materialized views. When a new event arrives, only the affected rows in the relevant views are updated. The agent reads a pre-computed result in single-digit milliseconds.

2. Semantic Search (Vector Database)

Fresh context covers structured operational data. But agents also need unstructured knowledge: documentation, past support tickets, policy documents, code snippets, conversation history stored as embeddings.

When an agent needs to answer "what does our rate-limiting policy say?" or "which past incidents match this error signature?", it performs semantic search over a vector index. The vector database (Pinecone, Weaviate, Qdrant, pgvector) stores high-dimensional embeddings and returns approximate nearest neighbors.

Semantic search complements fresh context. A streaming database tells the agent what is happening right now. A vector database tells the agent what it knows about the world from past experience.

3. Memory (Streaming Database + Persistent Store)

Agents need two kinds of memory:

Working memory is the agent's awareness of the current session: what the user has asked, what tools the agent has called, what results were returned. This changes continuously within a session and typically lives in the context window, in a cache, or in a short-term buffer.

Long-term memory is what the agent learns across sessions: user preferences, historical patterns, successful resolution strategies. This must be durable, queryable, and continuously updated as new interactions arrive.

A streaming database handles long-term memory well. You store raw interactions in tables and compute preference summaries, behavioral profiles, and session statistics through materialized views that update incrementally. When a user starts a new session, the agent queries these views to load its memory of that user in a single SELECT. This pattern is covered in depth in Building AI Agent Memory with a Streaming Database.

4. Tool Access (MCP)

An autonomous agent needs to act in the world, not just reason about it. Tool access is how it does that: querying databases, calling APIs, reading files, sending messages, modifying records.

The Model Context Protocol (MCP) has become the standard integration layer for agent tool access. An MCP server exposes a set of tools that any MCP-compatible agent (Claude, ChatGPT, VS Code Copilot, custom agents) can discover and invoke through a standardized JSON-RPC interface. RisingWave's PostgreSQL compatibility means any PostgreSQL MCP server connects to it directly. See How to Connect a Streaming Database to AI Agents via MCP for a full walkthrough.

Where RisingWave Fits in the Agentic Stack

The four requirements above map to distinct infrastructure components. Here is the full agentic architecture:

graph TB
    subgraph Sources
        K[Kafka / Kinesis]
        CDC[Database CDC]
        API[Application Events]
    end

    subgraph FreshContext["Fresh Context Layer (RisingWave)"]
        T1[agent_infra_events]
        T2[agent_infra_users]
        T3[agent_infra_tool_calls]
        MV1[agent_infra_context_snapshot]
        MV2[agent_infra_tool_metrics]
        MV3[agent_infra_event_window]
        T1 --> MV1
        T2 --> MV1
        T3 --> MV2
        T1 --> MV3
    end

    subgraph SemanticLayer["Semantic Search Layer"]
        VDB[Vector Database\nPinecone / Weaviate / pgvector]
    end

    subgraph MemoryLayer["Long-Term Memory (RisingWave)"]
        HIST[Interaction History Tables]
        PREF[Preference Materialized Views]
    end

    subgraph AgentLayer["Agent Layer"]
        MCP[MCP Server]
        LLM[LLM + Agent Framework]
    end

    K --> T1
    CDC --> T2
    API --> T3
    MV1 --> MCP
    MV2 --> MCP
    MV3 --> MCP
    VDB --> LLM
    PREF --> LLM
    MCP --> LLM
    LLM -->|INSERT events| T1
    LLM -->|INSERT tool calls| T3
    LLM -->|INSERT interactions| HIST
    HIST --> PREF

RisingWave occupies two positions in this architecture:

Fresh context layer: Ingests live events from Kafka, CDC, or direct inserts and maintains always-fresh materialized views that the agent can query before each decision.

Long-term memory backend: Stores raw interaction history durably and computes aggregated behavioral profiles through incrementally maintained materialized views.

The vector database handles semantic search over unstructured knowledge. MCP handles the tool integration layer. Each component does what it is best at.

Building the Fresh-Context Layer with RisingWave

Let's build a working fresh-context layer for an autonomous agent. The scenario: multiple AI agents serve different users, generating API calls and tool invocations as they work. Before each decision, an agent queries RisingWave to understand the current state of the user's session.

All SQL below is verified on RisingWave 2.8.0.

Step 1: Define the Event Tables

Three tables capture the raw signals:

-- Live environment events: API calls, tool calls, errors
CREATE TABLE agent_infra_events (
    event_id    BIGINT PRIMARY KEY,
    agent_id    VARCHAR,
    user_id     VARCHAR,
    event_type  VARCHAR,
    resource    VARCHAR,
    status      VARCHAR,
    latency_ms  INT,
    metadata    VARCHAR,
    occurred_at TIMESTAMPTZ
);

-- User dimension: account tier, org, region
CREATE TABLE agent_infra_users (
    user_id    VARCHAR PRIMARY KEY,
    name       VARCHAR,
    plan_tier  VARCHAR,
    org        VARCHAR,
    region     VARCHAR,
    created_at DATE
);

-- Tool call log: which tools the agent invokes and how they perform
CREATE TABLE agent_infra_tool_calls (
    call_id    BIGINT PRIMARY KEY,
    agent_id   VARCHAR,
    user_id    VARCHAR,
    tool_name  VARCHAR,
    success    BOOLEAN,
    latency_ms INT,
    called_at  TIMESTAMPTZ
);

In a production setup, agent_infra_events and agent_infra_tool_calls would typically receive data from Kafka sources. The agent application publishes events to Kafka topics, and RisingWave ingests them continuously:

CREATE SOURCE agent_event_stream
WITH (
    connector = 'kafka',
    topic = 'agent.events',
    properties.bootstrap.server = 'kafka:9092'
) FORMAT PLAIN ENCODE JSON;

For the examples below, we use direct INSERTs.

Step 2: Create Fresh-Context Materialized Views

View 1: Session context snapshot per agent (rolling 2-hour window)

This view gives the agent a complete picture of what has happened in the current work session, joined with the user's account tier for personalization:

CREATE MATERIALIZED VIEW agent_infra_context_snapshot AS
SELECT
    e.agent_id,
    e.user_id,
    u.name,
    u.plan_tier,
    u.org,
    COUNT(*)                                                  AS total_events,
    COUNT(*) FILTER (WHERE e.status = 'error')                AS error_count,
    ROUND(AVG(e.latency_ms)::NUMERIC, 0)                      AS avg_latency_ms,
    MAX(e.latency_ms)                                         AS max_latency_ms,
    MAX(e.occurred_at)                                        AS last_active
FROM agent_infra_events e
JOIN agent_infra_users u ON e.user_id = u.user_id
WHERE e.occurred_at > NOW() - INTERVAL '2 hours'
GROUP BY e.agent_id, e.user_id, u.name, u.plan_tier, u.org;

Query the view:

SELECT * FROM agent_infra_context_snapshot ORDER BY agent_id;

 agent_id | user_id |    name     | plan_tier  |    org    | total_events | error_count | avg_latency_ms | max_latency_ms |          last_active
----------+---------+-------------+------------+-----------+--------------+-------------+----------------+----------------+-------------------------------
 agent-1  | u001    | Alice Chen  | pro        | StreamCo  |            5 |           2 |            488 |           1200 | 2026-04-02 07:23:28.257+00:00
 agent-2  | u002    | Bob Tanaka  | enterprise | DataForge |            3 |           0 |            213 |            340 | 2026-04-02 06:38:28.257+00:00
 agent-3  | u003    | Carol Smith | free       | TechStart |            2 |           1 |            350 |            500 | 2026-04-02 06:48:28.257+00:00
 agent-4  | u004    | David Park  | enterprise | FinanceAI |            3 |           0 |            105 |            160 | 2026-04-02 07:08:28.257+00:00

Before responding to Alice Chen, the agent sees: 5 events in the last 2 hours, 2 errors, 1200ms peak latency. The context arrives in one query, pre-computed and joined with account tier. No application-side aggregation.

View 2: Tool performance summary (rolling 2-hour window)

This view tracks which tools each agent is using and how they are performing. An agent can use this to decide whether to retry a failing tool or escalate:

CREATE MATERIALIZED VIEW agent_infra_tool_metrics AS
SELECT
    tc.agent_id,
    tc.tool_name,
    COUNT(*)                                                    AS call_count,
    SUM(CASE WHEN tc.success THEN 1 ELSE 0 END)                AS success_count,
    ROUND(AVG(tc.latency_ms)::NUMERIC, 0)                       AS avg_latency_ms,
    MAX(tc.called_at)                                           AS last_called
FROM agent_infra_tool_calls tc
WHERE tc.called_at > NOW() - INTERVAL '2 hours'
GROUP BY tc.agent_id, tc.tool_name;

SELECT * FROM agent_infra_tool_metrics ORDER BY agent_id, call_count DESC;

 agent_id |  tool_name   | call_count | success_count | avg_latency_ms |          last_called
----------+--------------+------------+---------------+----------------+-------------------------------
 agent-1  | search_docs  |          2 |             2 |            175 | 2026-04-02 07:20:28.257+00:00
 agent-1  | run_sql      |          1 |             1 |            320 | 2026-04-02 06:13:28.257+00:00
 agent-2  | search_docs  |          1 |             1 |            210 | 2026-04-02 06:28:28.257+00:00
 agent-2  | run_sql      |          1 |             1 |            340 | 2026-04-02 06:33:28.257+00:00
 agent-2  | write_memory |          1 |             1 |            100 | 2026-04-02 06:40:28.257+00:00
 agent-3  | search_docs  |          2 |             1 |            353 | 2026-04-02 06:50:28.257+00:00
 agent-4  | fetch_price  |          2 |             2 |             78 | 2026-04-02 07:03:28.257+00:00
 agent-4  | write_memory |          1 |             1 |            105 | 2026-04-02 07:10:28.257+00:00

agent-3 called search_docs twice but only succeeded once. An orchestrating agent monitoring this view can flag the degraded tool and switch to an alternative.

View 3: 15-minute tumbling windows for error-rate monitoring

This view gives the agent (or an observability system) a windowed view of error patterns, making it easy to spot spikes:

CREATE MATERIALIZED VIEW agent_infra_event_window AS
SELECT
    window_start,
    window_end,
    agent_id,
    COUNT(*)                                            AS events,
    COUNT(*) FILTER (WHERE status = 'error')            AS errors,
    ROUND(
        100.0 * COUNT(*) FILTER (WHERE status = 'error') / COUNT(*),
        1
    )                                                   AS error_pct,
    ROUND(AVG(latency_ms)::NUMERIC, 0)                  AS avg_latency_ms
FROM TUMBLE(agent_infra_events, occurred_at, INTERVAL '15 minutes')
GROUP BY window_start, window_end, agent_id;

SELECT * FROM agent_infra_event_window ORDER BY window_start DESC, agent_id;

       window_start        |        window_end         | agent_id | events | errors | error_pct | avg_latency_ms
---------------------------+---------------------------+----------+--------+--------+-----------+----------------
 2026-04-02 07:15:00+00:00 | 2026-04-02 07:30:00+00:00 | agent-1  |      2 |      1 |      50.0 |            670
 2026-04-02 07:00:00+00:00 | 2026-04-02 07:15:00+00:00 | agent-4  |      2 |      0 |       0.0 |            118
 2026-04-02 06:45:00+00:00 | 2026-04-02 07:00:00+00:00 | agent-3  |      1 |      0 |       0.0 |            200
 2026-04-02 06:30:00+00:00 | 2026-04-02 06:45:00+00:00 | agent-3  |      1 |      1 |     100.0 |            500
 2026-04-02 06:15:00+00:00 | 2026-04-02 06:30:00+00:00 | agent-1  |      1 |      0 |       0.0 |            130
 2026-04-02 06:00:00+00:00 | 2026-04-02 06:15:00+00:00 | agent-1  |      1 |      1 |     100.0 |            850

An orchestration agent watching this view would immediately notice agent-1 running at 50% error rate in the current window. It can page an on-call engineer, reroute traffic, or attempt a self-correcting action.

Step 3: Query Context in Agent Code

Because RisingWave is wire-compatible with PostgreSQL, your agent framework uses the same psycopg2, pgx, or node-postgres driver it already uses for any other database. Here is a Python example:

import psycopg2
import json

def load_agent_context(agent_id: str, user_id: str) -> dict:
    """Load fresh context from RisingWave before the agent's next action."""
    conn = psycopg2.connect(
        host="localhost", port=4566,
        user="root", dbname="dev"
    )
    cur = conn.cursor()

    # Session context: events, errors, latency for this agent
    cur.execute("""
        SELECT name, plan_tier, org, total_events,
               error_count, avg_latency_ms, last_active
        FROM agent_infra_context_snapshot
        WHERE agent_id = %s AND user_id = %s
    """, (agent_id, user_id))
    session = cur.fetchone()

    # Tool performance: which tools are working well right now
    cur.execute("""
        SELECT tool_name, call_count, success_count, avg_latency_ms
        FROM agent_infra_tool_metrics
        WHERE agent_id = %s
        ORDER BY call_count DESC
    """, (agent_id,))
    tools = cur.fetchall()

    cur.close()
    conn.close()

    return {
        "session": {
            "user": session[0], "tier": session[1], "org": session[2],
            "total_events": session[3], "errors": session[4],
            "avg_latency_ms": str(session[5]),
            "last_active": str(session[6])
        } if session else {},
        "tool_health": [
            {"tool": t[0], "calls": t[1], "successes": t[2],
             "avg_ms": str(t[3])}
            for t in tools
        ]
    }

# Before the agent's next action:
context = load_agent_context("agent-1", "u001")
system_prompt = f"""You are an autonomous assistant.

Current session context:
{json.dumps(context['session'], indent=2)}

Tool health (last 2 hours):
{json.dumps(context['tool_health'], indent=2)}

Use this context to decide your next action. If error_count is high or
a tool's success rate is low, escalate or choose an alternative approach."""

The query executes in single-digit milliseconds because both materialized views serve pre-computed results. The agent does not wait for aggregations to run. It reads the state of the world as of the latest event.

Comparison: Batch Pipelines vs. Streaming Database for Agent Context

The alternative to a streaming database is a batch pipeline (Airflow, dbt, Spark Structured Streaming) that refreshes context on a schedule. Here is how the two approaches compare for autonomous agent workloads:

For simple analytical agents that generate weekly reports, batch pipelines are fine. For autonomous agents that make decisions in real time, the staleness risk of batch pipelines becomes a correctness problem. A fraud agent that cannot see the last 30 seconds of transactions is not doing fraud detection.

The Vector Database's Role: Semantic Search, Not Fresh Context

Vector databases and streaming databases solve different problems. This distinction matters for agentic architecture design.

A vector database stores dense numerical representations (embeddings) of documents, code, or conversation history. Its core operation is approximate nearest-neighbor search: given a query embedding, find the N most similar stored embeddings. This is the right tool when the agent needs to answer questions like:

• "Which past support tickets are semantically similar to this user's complaint?"
• "What sections of the documentation explain rate limiting?"
• "Which previous agent runs handled a similar error pattern?"

These are retrieval tasks over unstructured knowledge. The answer does not change from second to second. A slight delay in updating the vector index (minutes to hours) is usually acceptable.

A streaming database is the right tool when the agent needs to know:

• "How many errors has this user hit in the last 5 minutes?"
• "What is the current P95 latency of the run_sql tool?"
• "Which agents are currently in an error state?"

These are operational awareness tasks over structured, continuously changing data. Staleness of minutes is unacceptable.

The practical agentic architecture uses both: RisingWave for structured fresh context and a vector database for semantic retrieval. The agent queries both before reasoning. Neither replaces the other. For a deeper look at streaming data in RAG pipelines, see Real-Time Data Pipeline for RAG Applications.

Production Patterns

Multi-Agent Observability

When multiple agents run concurrently, an orchestrating meta-agent may monitor their health using the same materialized views individual agents use for self-context. The window view (agent_infra_event_window) gives a cross-agent picture of error rates and latency trends. The orchestrating agent can redirect work from degraded agents to healthy ones without any custom monitoring infrastructure.

Temporal Filters for Automatic Data Expiry

Autonomous agents generate high volumes of event data. Managing retention manually (DELETE jobs, archival pipelines) adds operational overhead. RisingWave's temporal filters handle expiry declaratively inside the SQL definition:

-- Context snapshot that automatically covers only the last 30 minutes
CREATE MATERIALIZED VIEW agent_infra_recent_context AS
SELECT
    agent_id,
    user_id,
    COUNT(*) AS events,
    COUNT(*) FILTER (WHERE status = 'error') AS errors,
    MAX(occurred_at) AS last_active
FROM agent_infra_events
WHERE occurred_at > NOW() - INTERVAL '30 minutes'
GROUP BY agent_id, user_id;

Rows older than 30 minutes fall out of this view automatically. Storage is reclaimed without any scheduled jobs.

Scaling for High-Throughput Agent Workloads

For agents running at high throughput, direct INSERTs to RisingWave tables can be replaced with Kafka sources. The agent application publishes events to Kafka topics; RisingWave consumes them continuously through a source connector. This decouples agent write throughput from RisingWave's ingestion capacity and gives you a durable event log as a side effect.

Point lookups against materialized views (filtering by agent_id or user_id) are fast because RisingWave stores materialized view results partitioned by the GROUP BY keys. An agent querying its own context does not trigger a full scan.

What Is Real-Time Data Infrastructure for AI Agents?

Real-time data infrastructure for AI agents is the set of systems that provide agents with fresh operational context, persistent memory, semantic retrieval, and tool access. It differs from traditional data infrastructure in two ways: data must be current within seconds (not hours), and the access pattern is conversational and iterative rather than batch analytical. For autonomous agents that make decisions affecting users or systems in real time, the freshness and latency requirements of the data infrastructure directly determine the correctness of the agent's decisions.

How Does a Streaming Database Differ from a Cache for Agent Context?

A cache (Redis, Memcached) stores pre-computed values that expire on a TTL. You populate it explicitly by running a query and writing the result. When the underlying data changes, the cache entry is stale until it expires or is manually invalidated.

A streaming database like RisingWave maintains materialized views that update incrementally as new data arrives. There is no TTL, no manual invalidation, and no separate process to refresh values. When a new event row is inserted, RisingWave computes the delta to all affected materialized views within milliseconds. The agent always reads the latest result. This is a fundamentally different model: instead of caching a snapshot and managing its staleness, you define the computation declaratively in SQL and RisingWave keeps the result current automatically.

Can I Use RisingWave Alongside My Existing Vector Database?

Yes. RisingWave and a vector database handle different parts of the agent's context retrieval. RisingWave handles structured operational data: event aggregations, session statistics, user profiles, tool performance metrics. These are fresh, structured, and updated in milliseconds. A vector database (Pinecone, Weaviate, Qdrant, or pgvector) handles unstructured knowledge retrieval: semantically similar documents, past conversations, reference content. These are retrieved by embedding similarity and tolerate slightly longer update latency. Most production agentic architectures use both: the agent queries RisingWave for fresh context and the vector database for semantic context before each reasoning step.

Do I Need MCP to Connect My Agent to RisingWave?

No. RisingWave is wire-compatible with PostgreSQL. Any PostgreSQL client library connects to it directly: psycopg2 in Python, pgx in Go, node-postgres in JavaScript, JDBC in the JVM. MCP is the right integration layer when you want standardized, discoverable tool access across multiple agent frameworks (Claude, ChatGPT, VS Code Copilot). It gives agents a uniform way to call RisingWave queries without hardcoded connection logic in every agent. But for a single-framework deployment, a direct psycopg2 connection to port 4566 is all you need.

Conclusion

Autonomous AI agents need four things from their data infrastructure: fresh context for real-time awareness, semantic search for knowledge retrieval, memory for cross-session learning, and tool access for acting in the world. No single system covers all four, but the right combination keeps the architecture simple:

• RisingWave as the fresh-context layer: Maintains always-current materialized views over live event streams, pre-computing the operational context the agent needs before each decision. Temporal filters handle rolling windows and data expiry automatically. The PostgreSQL wire protocol means no new SDKs or drivers.
• A vector database for semantic retrieval: Covers the unstructured knowledge retrieval that structured streaming cannot handle. Pair with RisingWave for real-time RAG pipelines.
• RisingWave as the long-term memory backend: Stores raw interactions and computes behavioral profiles incrementally. The agent loads a user's memory in a single SELECT query.
• MCP as the tool integration layer: Gives agents a standardized protocol to discover and invoke RisingWave queries across any agent framework.

The critical insight is that freshness is a correctness requirement, not a performance optimization. An autonomous agent that acts on stale context makes decisions that are internally consistent but wrong. A streaming database makes freshness a property of the infrastructure rather than a burden on the agent application.

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