DEV Community: N. Ashish

Hermes Predict: An AI Agent That Learns from Machine Failures

N. Ashish — Sun, 31 May 2026 17:14:27 +0000

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
│
▼
Hermes Agent
│
├── Anomaly Detection
│
├── Historical Incident Search
│
├── Root Cause Investigation
│
├── Maintenance Recommendation
│
├── Report Generation
│
└── Skill & Memory Storage
│
▼
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:

nashish109 / NALCO_Proj

# 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.

---

## 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.

---

…

View on GitHub

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.

Most AI Forgets. Hermes Agent Learns.

N. Ashish — Sun, 31 May 2026 16:00:05 +0000

This is a submission for the Hermes Agent Challenge: Write About Hermes Agent.

As a computer science student and developer, I've spent the last few years building everything from full-stack applications and data engineering pipelines to AI-powered predictive analytics systems. Throughout that journey, I've worked with many AI tools that can generate code, explain concepts, and solve problems surprisingly well.

But I've always felt they were missing something important.

Memory

No matter how capable an AI assistant is, most of them forget everything once the conversation ends. The next day, you're back to explaining your project, your architecture, your goals, and your workflow all over again.

While exploring Hermes Agent, I kept coming back to a simple mental model: a tree.

The roots represent memory. The trunk represents accumulated knowledge. The branches represent reusable skills developed through experience. Every completed task strengthens the system, allowing future tasks to build upon previous successes.

That idea is what inspired the visual at the beginning of this article.

Unlike traditional AI assistants that operate conversation by conversation, Hermes is designed around persistence. It remembers, learns, and continuously builds upon previous interactions. The more I explored its capabilities, the more I felt that Hermes isn't just another AI agent framework—it's a glimpse into where AI systems are heading next.

Getting Started with Hermes Agent

One thing I appreciated about Hermes Agent is that it doesn't take long to understand its value.

The setup process is straightforward:

Deploy Hermes locally or on a server
Configure your preferred model provider
Connect tools and external services
Enable memory and skill management
Start building autonomous workflows

Within a short time, you can have an agent capable of executing tasks, interacting with tools, managing files, conducting research, and maintaining context across sessions.

At first glance, that might sound similar to many modern agent frameworks.
But the real difference appears after the first task is completed.

The Difference Between Completing Tasks and Learning From Them

Most AI systems are excellent at solving problems.
Hermes focuses on something equally important: remembering how those problems were solved.

As I explored its architecture, I found this concept particularly interesting.

When Hermes completes a task, it doesn't simply move on. It can transform successful solutions into reusable skills that become part of its growing knowledge base.
That idea immediately resonated with me.
In software development, we rarely solve a problem once. Whether it's debugging APIs, handling deployments, optimizing workflows, or analyzing data, similar challenges appear repeatedly. Human developers naturally build experience from those situations.

Hermes attempts to do something similar.
Every completed task has the potential to become future knowledge.

How Hermes Compares to Other Agent Frameworks

The AI agent ecosystem has grown rapidly.

Frameworks like LangGraph, AutoGen, CrewAI, OpenHands, and others have made it easier than ever to build autonomous systems.
Many of these frameworks focus heavily on orchestration—how agents communicate, delegate tasks, and execute workflows.

Hermes approaches the problem from a different angle.
Its strongest feature isn't simply task execution.
It's continuity.

Instead of treating interactions as isolated events, Hermes combines:

Persistent memory
Skill generation
Tool integration
Autonomous execution
Long-term learning

The result feels less like a workflow engine and more like an evolving digital collaborator.

While many frameworks help agents complete tasks, Hermes is particularly focused on helping agents improve from those tasks.
That distinction may seem small, but I believe it's one of the most important ideas emerging in the agent ecosystem today.

Why This Matters to Me

During my work on AI-powered predictive maintenance systems, one challenge became obvious very quickly.

The real value isn't just detecting a problem.
The real value comes from learning from previous failures.

Imagine an AI agent monitoring machine health data.
A traditional assistant can help analyze a dataset when asked.

A persistent agent like Hermes could potentially:

Monitor incoming sensor streams
Identify anomalies
Track recurring patterns
Document previous incidents
Generate reports automatically
Reuse successful troubleshooting workflows

Over time, the system becomes more useful because it accumulates operational experience rather than starting from zero each time.
That shift—from assistance to accumulated expertise—is what excites me most.

A Technical Perspective

What makes Hermes particularly interesting isn't any single capability.
It's the way multiple capabilities work together to create a system that can continuously evolve.

Persistent Memory

Traditional AI assistants are constrained by session boundaries. Once a conversation ends, much of the accumulated context disappears, forcing users to repeatedly provide project details, preferences, and historical decisions.

Hermes approaches memory differently.

By maintaining information across interactions, it can build a progressively richer understanding of users, projects, and workflows. Instead of repeatedly reconstructing context, the agent can leverage previous knowledge as a foundation for future tasks.

From an engineering perspective, this shifts AI from being a stateless tool to a stateful collaborator.

Skill Creation

One of the most fascinating aspects of Hermes is its ability to transform completed work into reusable skills.

In traditional workflows, an AI might solve a complex problem today and approach a nearly identical problem tomorrow as if it has never encountered it before.

Hermes attempts to break that cycle.

When successful workflows are captured as reusable skills, the agent effectively develops operational experience. Rather than repeatedly rediscovering solutions, it can build upon prior successes, creating a feedback loop where completed work contributes to future performance.

This concept brings AI systems closer to how human expertise develops over time.

Tool Usage

Reasoning alone is rarely enough to solve real-world problems.

Modern AI systems must interact with external environments—searching the web, executing code, reading files, querying APIs, managing data, and gathering information from multiple sources.

Hermes extends beyond conversational intelligence by integrating these capabilities directly into its workflow.

This allows the agent not only to generate plans but also to execute them, validate results, and iterate based on real-world feedback.
The combination of reasoning and action is what enables genuine autonomy.

Autonomous Operation

Many AI assistants remain fundamentally reactive—they wait for instructions, complete a task, and stop.

Hermes explores a more proactive model.

By managing multi-step workflows, coordinating tool usage, scheduling actions, and pursuing longer objectives, the agent can operate beyond the boundaries of a single interaction.

This creates opportunities for continuous monitoring, automated research, operational support, and long-running workflows that would be difficult to achieve through traditional chat-based interfaces.

In many ways, this shifts AI from being an assistant that answers questions to a system capable of pursuing goals.

Why Hermes Matters Beyond Hermes

The reason I find Hermes interesting isn't because of any single feature.

Memory exists elsewhere.
Tool use exists elsewhere.
Planning exists elsewhere.
Automation exists elsewhere.

What's compelling is how Hermes combines these ideas into a system designed around continuity.

For decades, software has been built around storing and retrieving information.
Human expertise has been built around accumulating experience.

Hermes explores what happens when AI can begin doing both.

That question extends far beyond a single framework.
It touches on how future AI systems may operate across software engineering, research, healthcare, finance, education, manufacturing, and countless other domains.

The framework itself is interesting.
But the larger idea behind it is even more interesting.

Why Open Source Matters

Another reason Hermes stands out is its commitment to openness.

As developers, we learn the most when we can inspect, modify, and extend the systems we use.

Open-source projects accelerate innovation because they allow communities to experiment, contribute ideas, and build solutions that may never emerge inside closed ecosystems.

Hermes gives developers that opportunity.

Whether you're interested in software engineering, automation, research, DevOps, or AI experimentation, the framework provides a platform for exploring what persistent AI systems can become.

Looking Ahead

The AI industry often focuses on bigger models, larger context windows, and higher benchmark scores.

Those improvements are important.

But after learning about Hermes Agent, I've started thinking that the next major breakthrough might not be intelligence alone.

It might be accumulated experience.

The ability for AI systems to remember, learn, adapt, and continuously improve from previous interactions could fundamentally change how we work with technology.

For me, that's what makes Hermes Agent interesting.
Not because it can execute tasks.
Not because it can use tools.

But because it explores a future where AI doesn't simply respond to us—it grows alongside us.

Most AI systems help us complete tasks.

Hermes Agent accumulates experience.

And that simple difference might shape the next generation of AI.

I'd love to hear what others think.

Will long-term memory and skill accumulation become the defining features of next-generation AI agents, or do you see a different direction for the future of autonomous systems?