---
title: "Real-Time Decisioning for AI Agents: Why you Need a Customer Context Layer First"
description: "Real-time decisioning for AI agents depends on more than customer records: it requires a customer context layer that captures what people are doing now and connects that behavior to identity. The post positions the data platform as the center of a composable martech architecture, with agents and applications operating on shared data in open formats such as Apache Iceberg and Delta Lake. Snowplow’s described approach places structured event collection, schema validation, enrichment, and identity resolution before data reaches the platform, while operating in the customer’s cloud environment. It distinguishes historical profiles from granular behavioral event streams and argues that agent interaction outcomes should return to the foundation as first-class events. The resulting four-stage loop—collect, resolve and enrich, serve, and learn—supports simultaneous real-time and historical context, with decision quality dependent on source data coherence."
---

# Real-Time Decisioning for AI Agents: Why you Need a Customer Context Layer First

[Databricks](https://yomu.fyi/company/databricks) · Alex Dean · Apr 21, 2026

**Type:** Explainer

## Summary

Real-time decisioning for AI agents depends on more than customer records: it requires a customer context layer that captures what people are doing now and connects that behavior to identity. The post positions the data platform as the center of a composable martech architecture, with agents and applications operating on shared data in open formats such as Apache Iceberg and Delta Lake. Snowplow’s described approach places structured event collection, schema validation, enrichment, and identity resolution before data reaches the platform, while operating in the customer’s cloud environment. It distinguishes historical profiles from granular behavioral event streams and argues that agent interaction outcomes should return to the foundation as first-class events. The resulting four-stage loop—collect, resolve and enrich, serve, and learn—supports simultaneous real-time and historical context, with decision quality dependent on source data coherence.

## Context

AI agents making in-session decisions need current behavioral signals in addition to customer records and historical journeys. The post argues that noisy, inconsistent, or poorly modeled event data, missing identity resolution, and a lack of feedback from agent outcomes can undermine decision quality at scale.

## Approach / What changed

The described customer context layer captures behavioral events, validates them against schemas, enriches them, resolves identities across touchpoints, and makes the resulting context available to agents and customer-facing systems. Agent outcomes are treated as first-class behavioral events and fed back into the data foundation through a four-stage collect, resolve and enrich, serve, and learn loop.

## Takeaways

- Customer records describe who someone is, while customer context describes what they are doing now; real-time decisioning requires both.
- Snowplow’s Event Studio and schema registry are described as enforcing or flagging event structures before behavioral data lands in the data platform.
- The agentic feedback loop consists of collecting events, resolving and enriching them, serving context in real time, and feeding decision outcomes back to the foundation.

**Tags:** [AI Agents](https://yomu.fyi/topic/ai-agents), [Apache Iceberg](https://yomu.fyi/topic/apache-iceberg), [Architecture](https://yomu.fyi/topic/architecture), [Data Pipelines](https://yomu.fyi/topic/data-pipelines), [Delta Lake](https://yomu.fyi/topic/delta-lake)

- Source: [Databricks](https://www.databricks.com/blog/real-time-decisioning-ai-agents-why-you-need-customer-context-layer-first)
- Source URL: https://www.databricks.com/blog/real-time-decisioning-ai-agents-why-you-need-customer-context-layer-first
- Ingested by Yomu: 2026-08-31T03:42:07.313Z

[Read original post](https://www.databricks.com/blog/real-time-decisioning-ai-agents-why-you-need-customer-context-layer-first)