Case Study
Multi-Agent Data Pipeline Automation with AWS Bedrock and Claude
How we eliminated manual data operations for a regulated enterprise client
We built a multi-agent system on AWS Bedrock powered by Claude that autonomously monitors, diagnoses, and self-heals data pipelines — cutting issue resolution from 6–8 hours to 30 minutes and freeing engineers from reactive ops work.
DELIVERED AT SCALE
30M+
Pipeline events monitored daily
30 min
Issue resolution — down from 6–8 hours
70%
Reduction in manual ops work for the data team
Built on AWS Bedrock + Claude — production-grade multi-agent architecture
The Problem
Data engineers were firefighting instead of building
Complex, high-volume pipelines with no intelligent monitoring meant every failure required manual intervention — pulling engineers away from product work.
No Intelligent Failure Detection
Pipelines processing 30M+ daily events had no smart anomaly detection. Engineers relied on CloudWatch alerts that fired too late — often after data loss had already occurred downstream.
Manual Remediation at Every Step
Every SQS DLQ overflow, Glue job failure, or MSK consumer lag required a human to diagnose, find the root cause, and manually restart or patch the pipeline — often taking 6 to 8 hours.
Engineers Stuck in Reactive Mode
The data engineering team spent over 70% of their time on ops and incident response. Building new pipelines, improving data quality, and delivering analytical value had become secondary activities.
The Solution
A Claude-powered multi-agent system that runs the ops layer autonomously
We built a three-agent architecture on AWS Bedrock AgentCore where Claude handles reasoning, diagnosis, and remediation decisions — while AWS services handle execution.
Monitoring Agent
Continuously polls CloudWatch metrics, SQS DLQ depths, MSK consumer lag, and Glue job states. Claude interprets patterns and flags anomalies before they cascade into failures.
Diagnosis Agent
When a failure is detected, Claude reasons across pipeline logs, schema registry state, and upstream data contracts to identify the root cause — classifying failures into transient, schema, or infrastructure categories.
Remediation Agent
For classified failure types, the remediation agent executes predefined runbooks — restarting Glue jobs, replaying DLQ messages, adjusting MSK consumer offsets — and confirms resolution via downstream data validation.
Human-in-the-Loop Escalation
Novel or high-risk failures are escalated to the engineering team via Slack with Claude's diagnosis summary and recommended action — so engineers make decisions with full context, not cold alerts.
Tech Stack
Built on AWS Bedrock, AgentCore, and Claude
Every component was chosen for production reliability and enterprise security compliance.
Why Datavent
Senior-led, production-first delivery
We don't hand you a report. We stay until it's in production — and we're accountable to the outcomes we define upfront.
Production-first mindset
We architect for day-30, not day-1. Every agent system we build handles real-world scale — 30M+ events/day — from week one.
Full AWS + Claude expertise
From MSK and Glue to Bedrock, AgentCore, and OpenSearch — we cover the entire AWS data and AI stack in one engagement.
Embedded, not outsourced
We work inside your team's tools — Jira, GitHub, Slack. We hire, train, and mentor engineers. We leave your team stronger than we found it.
Measured by outcomes
6–8 hours → 30 minutes issue resolution. 70% reduction in manual ops. We define success metrics upfront and are accountable to them.
Regulated industry depth
Proven delivery in pharma, banking, and energy — the industries where data governance, security, and sovereignty matter most.
Talk to an Expert
We scope and deliver multi-agent automation systems tailored to your data stack — whether you're on AWS, Snowflake, or Databricks. Book a free session with one of our solution architects.