What I Built
I built Hermes Predict, an AI-powered predictive maintenance assistant that goes beyond simply detecting machine failures.
Traditional predictive maintenance systems can identify anomalies and predict failures, but they often stop there. Every incident is treated independently, and valuable troubleshooting knowledge can remain scattered across reports, dashboards, or team members.
Hermes Predict uses Hermes Agent to act as a persistent reliability engineer.
The system continuously analyzes machine telemetry, detects anomalies, investigates historical incidents, generates maintenance recommendations, and most importantly, learns from previous failures.
Every resolved incident becomes part of Hermes' growing knowledge base, allowing future troubleshooting workflows to benefit from past experience.
The goal is simple:
Don't just predict failures. Learn from them.
Demo
Workflow
Sensor Data
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Hermes Agent
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├── Anomaly Detection
│
├── Historical Incident Search
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├── Root Cause Investigation
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├── Maintenance Recommendation
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├── Report Generation
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└── Skill & Memory Storage
│
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Improved Future Responses
Example Scenario
A machine begins showing abnormal vibration patterns.
Maintenance Recommendation
After investigation, Hermes Predict generates a detailed maintenance recommendation report.
Hermes Predict:
- Detects the anomaly.
- Searches previous incidents.
- Identifies similar historical failures.
- Generates a probable root-cause analysis.
- Creates a maintenance report.
- Stores the successful troubleshooting workflow as a reusable skill.
When a similar issue occurs later, Hermes can leverage its previous experience rather than starting from scratch.
Code
GitHub Repository:
# LAST 200 HOURS
An AI-powered predictive maintenance system built with Hermes Agent.
LAST 200 HOURS analyzes industrial telemetry data to predict equipment failures, estimate Remaining Useful Life (RUL), detect anomalies, generate maintenance recommendations, and learn from historical machine behavior.
Built for the Hermes Agent Challenge.
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## What It Does
- Predicts Remaining Useful Life (RUL)
- Detects abnormal machine behavior
- Calculates failure probability
- Generates maintenance recommendations
- Simulates future equipment degradation
- Produces PDF maintenance reports
- Ingests real-time telemetry data
- Evaluates and retrains predictive models
---
## Hermes Agent
Hermes Agent serves as the reasoning and decision-making layer of the platform.
It:
- Analyzes telemetry insights
- Investigates anomalies
- Explains failure risks
- Generates maintenance recommendations
- Produces maintenance reports
- Supports operational decision-making
Unlike traditional predictive maintenance systems that stop at predictions, Hermes Agent helps transform predictions into actionable maintenance intelligence.
---…My Tech Stack
- Frontend
- Angular
- Backend
- Spring Boot
- Python
- Data & AI
- Pandas
- NumPy
- Scikit-Learn
- LSTM-based predictive models
- Agent Layer
- Hermes Agent
- Database
- PostgreSQL
- Visualization
- Chart.js
- Matplotlib
How I Used Hermes Agent
Hermes Agent sits at the heart of the system and powers the decision-making layer.
Rather than functioning as a simple chatbot, Hermes acts as an autonomous maintenance analyst.
Persistent Memory
One of the biggest challenges in predictive maintenance is preserving operational knowledge.
Hermes stores previous incidents, investigations, recommendations, and outcomes.
This enables the system to maintain long-term context and build a growing understanding of machine behavior.
Skill Creation
Every successfully resolved maintenance event becomes a reusable troubleshooting skill.
Instead of repeatedly rediscovering solutions, Hermes can apply previous knowledge to similar situations.
This mirrors how experienced maintenance engineers develop expertise over time.
Tool Usage
Hermes interacts with:
- Sensor datasets
- Historical maintenance records
- Failure logs
- Predictive models
- Reporting systems
By combining reasoning with tool access, Hermes can move beyond analysis and actively support operational decision-making.
** Autonomous Workflows**
Hermes coordinates multiple steps automatically:
- Monitoring telemetry
- Detecting anomalies
- Investigating incidents
- Generating recommendations
- Producing reports
- Updating its knowledge base
This transforms the system from a predictive model into a continuously learning maintenance assistant.
Why Hermes Agent Was the Right Choice
Many AI systems can analyze data.
Many agent frameworks can execute workflows.
What made Hermes Agent particularly compelling for this project was its emphasis on continuity.
Machine failures are rarely isolated events.
The ability to remember previous incidents, learn from successful resolutions, and reuse accumulated knowledge aligns naturally with the goals of predictive maintenance.
Hermes doesn't simply help solve a problem.
It helps ensure that every solved problem makes the system more capable in the future.
That philosophy is exactly what this project was built around.
Future Improvements
Some features I plan to explore next include:
- Real-time IoT sensor integration
- Automated maintenance scheduling
- Multi-agent collaboration for large industrial environments
- Failure pattern visualization dashboards
- Predictive spare-parts recommendation
- Cross-machine knowledge transfer using Hermes memory and skills
- Closing Thoughts
Building Hermes Predict helped me think differently about AI agents.
The most interesting aspect wasn't anomaly detection or report generation.
It was the idea that every interaction could become future experience.
For predictive maintenance systems, accumulated experience is often more valuable than individual predictions.
That's what Hermes Agent brings to the table.
It doesn't just help predict failures.
It learns from them.
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