Back office ops · Production

Agmatix improves agricultural field trial analysis with Leafy AI assistant on Amazon Bedrock

The problem

Building analytical dashboards for field trial data was complex and time-consuming: each trial could contain hundreds of parameters making it hard to identify the meaningful ones, selecting the right visualization technique from a wide range of options was difficult, and drawing conclusions between data points remained challenging even after dashboards were created.

Workflow diagram · grounded in source
1
User submits natural language question
trigger
“The user submits the question to Agmatix's AI assistant, Leafy”
2
Application reads from data lake
integration
“The application reads the field trial data, business rules, and other required data from the data lake”
3
Agent builds and sends prompt to FM
ai_action
“The agent inside the Insights application collects questions and tasks and the relevant data, and sends it as a prompt to the FM through Amazon Bedrock”
4
Generative AI model responds
ai_action
“The generative AI model's response is sent back to the Insights application”
5
Insights displayed via widgets
output
“The response is displayed to the user through the widgets visualizing the trial data and the answer to the user's specific question”
Reported outcome

By integrating Amazon Bedrock, Agmatix's data-driven field trials service observed over 20% improved efficiency, more than 25% improvement in data integrity, and a three-fold increase in analysis potential throughput.

Reported metrics
Efficiency improvementover 20%
Data integrity improvementmore than 25%
Analysis potential throughputthree-fold increase
Reported stack
Amazon BedrockAnthropic ClaudeLeafyAmazon S3AWS GlueAWS Lambda
Source
https://aws.amazon.com/blogs/machine-learning/generative-ai-for-agriculture-how-agmatix-is-improving-agriculture-with-amazon-bedrock?tag=soumet-20
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

By integrating Amazon Bedrock, Agmatix's data-driven field trials service observed over 20% improved efficiency, more than 25% improvement in data integrity, and a three-fold increase in analysis potential throughput.

What tools did this team use?

Amazon Bedrock, Anthropic Claude, Leafy, Amazon S3, AWS Glue, AWS Lambda.

What results were reported?

Efficiency improvement: over 20%; Data integrity improvement: more than 25%; Analysis potential throughput: three-fold increase (source-reported, not independently verified).

How is this back office ops AI workflow structured?

User submits natural language question → Application reads from data lake → Agent builds and sends prompt to FM → Generative AI model responds → Insights displayed via widgets.

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