Evidence: Medium50/100

Halter scales farm management using AI on Amazon Bedrock (Clank agent)

Use case typeObservabilityUpdated Jul 8, 2026

Halter built Clank, an autonomous AI agent on Amazon Bedrock that listens for CloudWatch-triggered alerts, gathers logs and context through ephemeral tasks on ECS/Fargate, analyzes incidents with Claude, and proposes code changes for human review. The system automates incident response and routine engineering workflows across a connected livestock platform that serves farmers remotely.

Organization
Halter
Industry
Agriculture
Location
New Zealand
Published
July 2026

Reported outcomes

2,500 commits

co-authored commitsOther quantified impact

90 tasks/weektasks automated per week431 pull requestspull requests reviewed82%pull request merge rate215 hoursmanual effort saved48 minutesmedian review-to-merge time

Strategic outcomes

Speed & agilityAccelerated incident resolution and code deliveryScale & capacityScaled alert handling without scaling engineering workloadCustomer experience & trustImproved platform reliability for remote farm operations
Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Tasks automated per week: 90 tasks/week increase

AWS Solutions Case StudiesJul 8, 2026Customer storyExplicit claimMedium evidence strength

“Clank now operates at scale across Halter’s engineering workflows, supporting more than 16,000 monitored system checks and handling over 90 tasks each week through Slack-based workflows.”

Normalized claim

Pull requests reviewed: 431 pull requests increase

AWS Solutions Case StudiesJul 8, 2026Customer storyExplicit claimMedium evidence strength

“Over an 11-week period, Clank reviewed more than 431 pull requests across 39 repositories”

Normalized claim

Pull request merge rate: 82%

AWS Solutions Case StudiesJul 8, 2026Customer storyExplicit claimMedium evidence strength

“with 82 percent merged”

Normalized claim

Co-authored commits: 2,500 commits

AWS Solutions Case StudiesJul 8, 2026Customer storyExplicit claimMedium evidence strength

“and contributed to over 2,500 co-authored commits.”

Normalized claim

Manual effort saved: 215 hours decrease

AWS Solutions Case StudiesJul 8, 2026Customer storyExplicit claimMedium evidence strength

“saving up to 215 hours of manual effort in just three months.”

Normalized claim

Median review-to-merge time: 48 minutes

AWS Solutions Case StudiesJul 8, 2026Customer storyExplicit claimMedium evidence strength

“The median time for an AI-generated code change to be reviewed and merged is just 48 minutes”

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
Customer
Halter
Provider
AWS
Maturity
Unknown

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

  • 1Observability
  • 2Workflow automation
  • 3Developer productivity
  • Engineering teams were burdened by more than 16,000 production alerts and spent significant time investigating incidents, tuning thresholds, and doing root-cause analysis.
  • The workload slowed engineering changes and forced engineers to wake up at night when alerts fired.
  • Halter built Clank on Amazon Bedrock using Anthropic Claude.
  • Clank integrates with Slack, GitHub, and Linear; CloudWatch alerts trigger webhooks and short-lived ECS/Fargate tasks that collect logs and system data, then Claude analyzes events, identifies root causes, and often proposes code changes and opens pull requests for review.
Technologies
Amazon BedrockAnthropic Claude 3Amazon CloudWatchAWS FargateAmazon ECSSlackGitHubLinear
  • Clank automates more than 90 tasks per week.
  • Over 11 weeks it reviewed more than 431 pull requests across 39 repositories, with 82% merged, and contributed to over 2,500 co-authored commits.
  • Halter says the system saved up to 215 hours of manual effort in three months and reduced median AI-generated code change review/merge time to 48 minutes.
Architecture

CloudWatch alerts trigger webhooks that launch ephemeral tasks on AWS Fargate using Amazon ECS. Each task retrieves relevant logs and system data, then connects to Claude on Amazon Bedrock to analyze incidents, identify root causes, and generate structured responses or code changes for pull request review. Slack, GitHub, and Linear are integrated into the workflow.

Implementation partners1
Anthropic
Sources & evidence1
Evidence: Medium50/100Evidence strength
  • Customer explicitly identified
  • Primary source available
  • Quantified outcome available
  • Technical implementation details available
Type: Customer StoryPublished: Jul 8, 2026Publisher: AWSEvidence: PrimaryConfidence: High
Primary source

AI-generated summary. Verify important details with the linked sources before relying on this case.

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