Agent-Facing Analytics

AI agents and autonomous systems need real-time data to make decisions. Apache Pinot delivers fresh data, sub-second queries, and high concurrency — the analytics backend for intelligent automation.

What Is Agent-Facing Analytics?

Agent-facing analytics is a category of workload where AI models or automated agents query data in real time to make decisions. Unlike user-facing analytics — where a human views a dashboard — the consumer of the query result is a program: an LLM, a fraud engine, a bidding system, or an autonomous monitoring agent.

These systems share common requirements: data must be fresh (seconds, not hours), queries must be fast (milliseconds, not seconds), and the system must handle high concurrency (thousands of agent sessions, not dozens of analysts).

Why Traditional Databases Fail for Agent Workloads

Too slow

Traditional OLAP databases return queries in seconds or minutes — too slow for an agent that needs to respond in under a second.

Not fresh

Batch-oriented warehouses ingest data hourly or daily. Agents making real-time decisions need data that is seconds old, not hours.

Not scalable for high-QPS agent queries

Agent workloads generate far more queries per second than human analysts. Most analytics databases are not built for 100K+ concurrent queries.

Not optimized for hybrid queries

Agent queries combine filtering, aggregation, and increasingly vector search in a single request. Few systems handle all three efficiently.

Why Apache Pinot Fits

Real-time ingestion

Data from Kafka, Pulsar, and Kinesis is queryable within seconds of arrival.

Sub-second latency

P99 query latency under 100ms — fast enough for agents operating within LLM response-time budgets.

High concurrency

100K+ queries per second, supporting thousands of concurrent agent sessions.

Hybrid queries

Combine filtering, aggregation, and vector similarity search in a single SQL query.

Upserts for mutable state

Keep risk profiles, feature stores, and entity states current as new signals arrive.

SQL interface

Agents construct queries programmatically using standard SQL — no custom API needed.

Example Workloads

Concrete patterns showing how AI agents and decision systems query Pinot.

LLM Agent Querying Real-Time Data

LLM agents translate natural language into Pinot SQL, enabling non-technical users and autonomous systems to query fresh analytics data without writing code.

-- Natural language: "How many impressions did campaign X get today?"
-- LLM-generated Pinot SQL:
SELECT campaign_id,
       SUM(impressions) AS total_impressions,
       SUM(clicks) AS total_clicks
FROM ad_events
WHERE ts > ago('PT24H')
  AND campaign_id = ?
GROUP BY campaign_id

Automated Observability Alerts

Autonomous monitoring systems query Pinot on a schedule, run anomaly detection, and trigger alerts — no human in the loop.

SELECT service, COUNT(*) AS error_count,
       PERCENTILE(latency_ms, 99) AS p99
FROM request_logs
WHERE ts > ago('PT15M')
  AND status_code >= 500
GROUP BY service
ORDER BY error_count DESC
LIMIT 10

Fraud Detection Feature Computation

ML fraud models query Pinot for real-time velocity counts and behavioral signals across time windows to score transactions within milliseconds.

SELECT card_id,
       COUNT(*) AS txn_count_5m,
       SUM(amount) AS total_5m,
       DISTINCT_COUNT(merchant_id) AS unique_merchants
FROM transactions
WHERE ts > ago('PT5M')
  AND card_id = ?
GROUP BY card_id

Ad Ranking with Real-Time Signals

Bidding agents query fresh click-through rates, impression counts, and budget pacing data to make bid decisions in real time.

SELECT campaign_id,
       SUM(impressions) AS impr,
       SUM(clicks) AS clicks,
       SUM(spend) AS spend
FROM ad_events
WHERE ts > ago('PT1H')
  AND advertiser_id = ?
GROUP BY campaign_id

Observability Copilots

AI assistants query live infrastructure metrics to triage incidents, correlate errors, and recommend remediation steps.

SELECT host, container_id,
       AVG(cpu_pct) AS avg_cpu,
       MAX(mem_pct) AS max_mem
FROM infra_metrics
WHERE ts > ago('PT10M')
  AND cluster = ?
GROUP BY host, container_id
HAVING AVG(cpu_pct) > 80
ORDER BY avg_cpu DESC

In Production

Companies using Pinot for automated, agent-facing workloads today.

LLM Agent

MiQ

Built an LLM agent using Google Gemini that translates natural language into Pinot SQL, enabling non-technical stakeholders to query real-time programmatic advertising data.

Uber

Built a time-series query engine on Pinot powering 100,000+ automated alerts in production — fully programmatic anomaly detection with no human in the loop.

WePay (JPMorgan Chase)

Computes real-time fraud features — payment velocity, unique merchants across time windows — consumed by ML fraud detection models for automated transaction scoring.

LinkedIn (ThirdEye)

ThirdEye queries Pinot every minute, runs ML-based anomaly detection algorithms, and triggers automated alerts — from system metric monitoring to business reporting.

Nubank

Uses Pinot for real-time observability with automated anomaly detection on Spark job metrics. An automated tuning recommender achieved 96% success rate across tested jobs.

One Database, Two Workloads

Apache Pinot is uniquely positioned at the intersection of user-facing and agent-facing analytics.

User-Facing

Dashboards, embedded analytics, product UIs

Agent-Facing

AI agents, RAG, decision engines, copilots

Apache Pinot

Real-time ingestion · Sub-second latency · 100K+ QPS

Frequently Asked Questions

What is Apache Pinot best for?

User-facing analytics applications
Agent-facing analytics for AI systems
Real-time event analytics
High-concurrency query workloads

Is Pinot good for AI/LLM applications?

Yes. Pinot supports real-time ingestion, sub-second queries, and hybrid filtering plus aggregation, making it suitable for agent-facing analytics and RAG-style systems over fresh data. Its SQL interface lets AI agents construct queries programmatically.

How is agent-facing analytics different from user-facing analytics?

In user-facing analytics, a human views the query result (a dashboard, a report). In agent-facing analytics, the consumer is a program — an LLM, a fraud engine, a recommendation system. Agent workloads typically require higher QPS, fresher data, and programmatic query construction.

Build Your Agent-Facing Analytics Stack

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