Agent 35 · Data Automation
DataAnalyse Agent
Finds anomalies in metrics and explains them.
Agent PackInsightLangGraph
Found7
Fixed + verified7
Held for approval0
Recalln/a
One offline run on demo data with planted problems (recorded platform test, November 2026).
How DataAnalyse heals data, step by step
INData inTaxi silver layer.
01Detect7 problems found
02Diagnose1 problem type named
03Plan7 safe to fix · 0 need a person
04Fix + verify7 fixed and re-checked
05ReportPlain-English report and Control Tower record
- Client problem
- Drops in volume are noticed only when someone asks.
- What it does
- Builds hourly metrics and detects anomalies.
- How it heals
- Writes an explained anomaly list.
- Human gate
- None.
- Typical buyer
- Owners, analysts
- Built with
- LangGraph self-healing loop
- Demo data
- Taxi silver layer.
- Tables
- Landing batches and lakehouse tables built by other agents
- Target outcome
- Target: anomalies explained within an hour.
- Path to production
- Insight Pack.
Issues detected on the demo data
| Issue | Risk | Proposed fix | Count |
|---|---|---|---|
| volume_drop 20 trips vs typical 31 for this hour | low | Publish anomaly note and open data-feed incident | 7 |
How we demo it
- Run DataAnalyse.
- Read the anomaly explanation.
Built on AWS
Runs in the platform container on EC2 Graviton (or on demand in Lambda), writes healed tables to the S3 lakehouse, uses Amazon Bedrock only to write explanations, and reports six metrics to CloudWatch.
Step into DigiQuench.
Agents that heal your data.
Watch an agent find planted problems, fix the safe ones and wait for your approval on the rest. Thirty minutes, live.