README.md

# Diagnostics

<p align="center">
  <strong>Find what is wrong. Understand why. Improve what happens next.</strong>
</p>

<p align="center">
  <img src="https://img.shields.io/badge/AI-Diagnostics-06B6D4?style=for-the-badge" alt="AI Diagnostics">
  <img src="https://img.shields.io/badge/Anomaly-Detection-2563EB?style=for-the-badge" alt="Anomaly Detection">
  <img src="https://img.shields.io/badge/Root--Cause-Analysis-7C3AED?style=for-the-badge" alt="Root Cause Analysis">
  <img src="https://img.shields.io/badge/System-Health-14B8A6?style=for-the-badge" alt="System Health">
</p>

---

## Diagnose before you optimize

**Diagnostics** is an independent Hugging Face organization focused on tools, datasets, models, and experiments that help detect problems, explain failures, and assess the health of intelligent systems.

Modern AI systems are complex.

A failure may come from:

- the model
- the data
- the prompt
- the tool
- the workflow
- the sensor
- the infrastructure
- the network
- the environment
- the user input
- the interaction between several components

Diagnostics is about making those failures visible.

> **Detect. Explain. Verify. Improve.**

---

# The Diagnostic Loop

```text
SIGNAL
  ↓
DETECTION
  ↓
EVIDENCE
  ↓
HYPOTHESIS
  ↓
ROOT CAUSE
  ↓
RECOMMENDED ACTION
  ↓
VERIFICATION
```

A useful diagnostic system should not only say:

> something is wrong

It should help answer:

> **what changed, where it happened, and what is most likely causing it?**

---

# 01 · AI Model Diagnostics

Possible areas:

- model drift
- prediction instability
- confidence shifts
- hallucination patterns
- regression detection
- output inconsistency
- latency changes
- token anomalies
- model-version comparison

---

# 02 · Agent Diagnostics

AI agents introduce new failure modes.

Diagnostics may inspect:

- failed tool calls
- excessive retries
- broken plans
- wrong tool selection
- loops
- incomplete tasks
- permission failures
- unexpected handoffs
- abnormal execution traces

Example:

```text
Task
 ↓
Plan
 ↓
Tool A ✓
 ↓
Tool B ✕
 ↓
Retry ✕
 ↓
Fallback ✓
 ↓
Result
```

A trace can reveal where the workflow began to fail.

---

# 03 · Data Diagnostics

Bad data can create good-looking but unreliable outputs.

Possible checks:

- missing values
- duplicates
- schema drift
- class imbalance
- outliers
- corrupted records
- distribution shift
- suspicious labels
- inconsistent units
- unexpected feature ranges

---

# 04 · Sensor Diagnostics

Physical AI systems depend on reliable signals.

Possible topics:

- calibration drift
- sensor flatlines
- spikes
- missing readings
- noise
- signal degradation
- cross-sensor disagreement
- abnormal variance

Sensor health is often the first layer of system health.

---

# 05 · Inference Diagnostics

Inference failures can come from more than the model.

Possible metrics:

- time to first token
- tokens per second
- error rate
- queue time
- timeout rate
- memory pressure
- fallback rate
- provider failures
- degraded throughput

---

# 06 · Workflow Diagnostics

AI systems increasingly operate as workflows.

Possible diagnostic questions:

- Which step failed?
- Where did latency increase?
- Which component caused the retry?
- Did the fallback path work?
- Did the workflow stop too early?
- Was the final result complete?

---

# 07 · Root-Cause Analysis

Detection is only the beginning.

A diagnostic system may combine:

```text
metrics
+
logs
+
traces
+
events
+
configuration
+
history
```

to generate plausible explanations for a failure.

Possible output:

```text
Observed issue:
High task failure rate

Likely contributors:
1. Tool timeout increase
2. Retry budget exhausted
3. Fallback model unavailable
```

Root-cause analysis should remain evidence-based and clearly separate observations from hypotheses.

---

# 08 · Health Scoring

Diagnostics can summarize system state.

Example:

```text
Model Health        92 / 100
Data Quality        81 / 100
Tool Reliability    74 / 100
Latency Health      88 / 100
Workflow Health     69 / 100
```

A score should never hide the underlying evidence.

Good diagnostics make both visible.

---

# Possible Spaces

### AI Health Check
Run a structured health assessment across model, data, latency, and reliability metrics.

### Agent Trace Diagnostics
Upload an agent trace and detect loops, retries, failures, and abnormal execution patterns.

### Dataset Health Inspector
Check missing values, duplicates, outliers, schema drift, and distribution problems.

### Sensor Diagnostics Lab
Detect flatlines, drift, spikes, and signal-quality problems.

### Inference Diagnostics
Inspect latency, throughput, error rate, fallbacks, and degraded performance.

### Failure Pattern Explorer
Cluster recurring failure cases and surface common signatures.

### Root-Cause Assistant
Combine structured evidence and produce ranked diagnostic hypotheses.

### Regression Detector
Compare two system versions and highlight meaningful changes.

### Workflow Health Monitor
Analyze multi-step workflows and identify weak points.

### Diagnostic Report Builder
Turn structured signals into a clear technical report.

---

# Possible Datasets

Potential datasets may include:

```text
ai-failure-cases
agent-diagnostic-traces
sensor-fault-signals
dataset-quality-issues
inference-regressions
workflow-failure-events
system-health-snapshots
root-cause-scenarios
```

Useful fields may include:

- timestamp
- component
- signal
- anomaly
- severity
- evidence
- hypothesis
- root_cause
- remediation
- outcome

---

# Possible Models

Models may support:

- anomaly detection
- fault classification
- failure prediction
- root-cause ranking
- regression detection
- trace analysis
- log classification
- sensor-fault detection
- system-health scoring
- diagnostic summarization

---

# Diagnostic Dimensions

| Dimension | Question |
|---|---|
| **Detection** | Is something abnormal? |
| **Localization** | Where did it happen? |
| **Severity** | How serious is it? |
| **Explanation** | What evidence supports the finding? |
| **Root Cause** | What is most likely responsible? |
| **Recovery** | What changed after intervention? |
| **Regression** | Is the system getting worse over time? |
| **Confidence** | How certain is the diagnosis? |

---

# A Minimal Diagnostic Record

```json
{
  "component": "tool-router",
  "issue": "increased failure rate",
  "severity": "medium",
  "evidence": {
    "error_rate_before": 0.03,
    "error_rate_now": 0.17
  },
  "hypothesis": "schema mismatch after tool update",
  "confidence": 0.81
}
```

A useful diagnostic record separates:

- observation
- evidence
- hypothesis
- confidence

---

# Diagnostics + Observability

Observability asks:

> What is happening?

Diagnostics asks:

> What is wrong, and why?

The two are closely connected.

```text
OBSERVABILITY
      ↓
SIGNALS
      ↓
DIAGNOSTICS
      ↓
EXPLANATION
      ↓
ACTION
```

---

# Diagnostics + Evaluation

Evaluation tells us whether a system performs well.

Diagnostics helps explain why it does not.

This makes Diagnostics especially useful alongside:

- benchmarks
- agent evals
- model monitoring
- regression testing
- red teaming
- reliability testing

---

# Diagnostics + Physical AI

As AI moves into the physical world, diagnostics becomes even more important.

Robots, vehicles, machines, and sensor systems may need to distinguish between:

- software failure
- model failure
- sensor failure
- network failure
- environmental change
- mechanical fault

That requires multi-layer diagnostics.

---

# Design Principles

### Evidence before explanation
Diagnostics should begin with observable signals.

### Separate fact from hypothesis
A likely cause is not the same as a confirmed cause.

### Keep uncertainty visible
Confidence matters.

### Detect regressions early
Small changes can become large failures.

### Diagnose systems, not only models
AI quality depends on the full stack.

### Make results actionable
A useful diagnosis should help determine what to inspect next.

### Preserve raw evidence
Summaries should not replace underlying traces and measurements.

---

# Technology Directions

Projects may explore:

- Hugging Face Spaces
- Hugging Face Datasets
- anomaly detection
- time-series analysis
- log analysis
- trace inspection
- root-cause analysis
- model monitoring
- sensor analytics
- regression testing
- agent observability
- structured diagnostics
- statistical quality checks
- machine learning
- AI-assisted troubleshooting

---

# Who Is Diagnostics For?

Diagnostics may be useful for:

- AI engineers
- agent developers
- MLOps teams
- reliability engineers
- platform teams
- data scientists
- robotics teams
- IoT developers
- infrastructure engineers
- researchers
- open-source contributors

---

# Long-Term View

As AI systems become more capable, they also become more complex.

Complex systems fail in complex ways.

The future challenge may not only be:

> **Can we build more intelligent systems?**

It may also be:

> **Can we understand when they fail?**

That is the space Diagnostics explores.

---

# Important Note

Projects published here are intended primarily for:

- research
- education
- development
- benchmarking
- prototyping
- technical experimentation

They should not be treated as certified diagnostic systems for medical, safety-critical, industrial, automotive, aviation, or other high-impact environments unless explicitly validated for that use.

---

# Independent Organization

**Diagnostics is an independent Hugging Face community organization.**

It is not an official medical provider, certification body, equipment manufacturer, model provider, standards organization, or Hugging Face organization.

The name **Diagnostics** describes the technical focus:

> **detecting problems, understanding failures, and improving system health.**

---

<p align="center">

# DIAGNOSTICS

### **Detect. Explain. Verify. Improve.**

</p>