Normalized claim
Time: 20 minutes decrease
Initial triage time dropped from more than a day to less than 20 minutes.
Formula 1 builds an RCA assistant with Amazon Bedrock Agents and Knowledge Bases to cut triage and resolution time
Use case typeIT operationsUpdated Jun 13, 2026
Formula 1 (F1) built a purpose-built root cause analysis assistant for race-day issues. The solution uses Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases with log ETL in S3, Glue, Spark, EventBridge, and ECS. It connects to internal and external systems such as Jira, Datadog, SQL databases, and health-check APIs to recommend remediation and route incidents.
Reported outcomes
−86%
timeTime & speed
20 minutestime3 daysquantified impact
Strategic outcomes
Speed & agilityAccelerated incident triage and resolutionSpeed & agilityEnabled rapid solution implementation and testingNew product / capabilityBuilt a purpose-built RCA assistantNew product / capabilityAutomated controlled remediation recommendations
Catalog median for time & speed deployments: −50% across 312 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Time: 86% decrease
End-to-end time to resolution was reduced by as much as 86%.
Normalized claim
Quantified impact: 3 days
The team implemented and tested a solution within 3 days over a race weekend.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Formula 1
- Provider
- AWS
- Maturity
- Unknown
- Linked source
- Amazon Web Services Blog
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 3
- 1IT Operations
- 2Incident Management
- 3Root Cause Analysis
- Race-weekend incidents could take up to 3 weeks to triage and resolve.
- Engineers spent around 15 full engineer days on recurring API and system performance issues.
- Troubleshooting required cross-team investigations across development, operations, infrastructure, and networking.
- Centralize raw logs in Amazon S3.
- Use AWS Glue and Apache Spark ETL to standardize, filter, and aggregate logs before ingestion.
- Use Amazon Bedrock Knowledge Bases for RAG and Amazon Bedrock Agents to orchestrate controlled tool/API calls and generate recommendations.
- Host the chat application on Amazon ECS and integrate with Jira and Datadog.
- Initial triage time dropped from more than a day to less than 20 minutes.
- End-to-end time to resolution was reduced by as much as 86%.
- The team implemented and tested a solution within 3 days over a race weekend.
Architecture
Raw logs are centralized in Amazon S3, transformed with AWS Glue and Apache Spark, and ingested into Amazon Bedrock Knowledge Bases. An Amazon Bedrock Agent orchestrates controlled tool/API calls and RAG over the knowledge base, while the chat UI runs on Amazon ECS and integrates with Jira and Datadog for incident routing and actions.
Sources & evidence1
Evidence: Low35/100Evidence strength
- Customer explicitly identified
- Quantified outcome available
- Technical implementation details available
ExpandedExpanded
The same organization appears in newer AI deployment evidence.
- Same organization re-documented as recently as 2026.
Measures whether this deployment's public evidence persists — not whether the system is still in production.
Type: Blog PostPublished: Feb 18, 2025Publisher: Amazon Web ServicesEvidence: VendorConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
Explore related AI use cases
Customer evidence
Provider evidence
Market context
Browse the catalog
Was this useful?
Community
Comments
No published comments yet.