> ## Documentation Index
> Fetch the complete documentation index at: https://docs.avigrah.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Executions & Observability

Executions represent how your data pipelines and AI systems run in real time.

Observability ensures you can **track, understand, and trust** every step of that execution.

Together, they provide full visibility into how your AI systems operate.

***

## What is an Execution?

An Execution is a **single run of a data pipeline**.

Each time a pipeline is triggered:

* Data is processed through defined stages
* Transformations and vectorization occur
* Outputs are generated and stored
* AI systems receive updated data

Every run is tracked as a separate execution.

***

## Execution Lifecycle

Each execution follows a structured flow:

**Pending → Running → Completed / Failed / Stopped**

During execution:

* Data moves through pipeline stages
* Metrics are recorded in real time
* Logs are generated for traceability

***

## Pipeline Stages (Execution Flow)

Executions are broken down into **clear stages**.

Typical stages include:

* **Extract** → Data ingestion from sources
* **Transform** → Cleaning and structuring
* **Vectorize** → Converting data into embeddings
* **Load** → Storing in databases and making it ready for AI

Each stage is:

* Independently tracked
* Measured for duration and status
* Logged for debugging and auditing

***

## Real-Time Observability

Avigrah provides full visibility into execution through multiple layers.

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### Execution Status

At any time, you can see:

* Current status (Running, Completed, Failed, Paused)
* Start time and duration
* Progress across stages

***

### Stage-Level Tracking

Each pipeline stage shows:

* Current state (Pending, Running, Completed, Failed)
* Execution duration
* Stage-specific metrics

This allows precise identification of where issues occur.

***

### Logs (System-Level Visibility)

Every execution generates structured logs.

Logs provide:

* Step-by-step system activity
* Errors and warnings
* Execution messages and events

This ensures full traceability of what happened during execution.

***

### Metrics (Performance Insights)

Executions track key system metrics such as:

* API calls made
* Data processed
* Vectors generated
* Retry attempts

These metrics help evaluate:

* System performance
* Data volume handled
* Operational efficiency

***

## Progress Tracking

Execution progress is calculated based on:

* Number of completed stages
* Total pipeline stages
* Weighted execution flow

This provides a clear view of how far the pipeline has progressed.

***

## Failure Handling & Retries

If an execution fails:

* The system captures the failure stage
* Logs highlight the root cause
* Retry attempts are tracked

Pipelines can be:

* Re-run
* Restarted from execution controls

This ensures reliability in production environments.

***

## Execution History

Each pipeline maintains a history of executions.

This allows you to:

* Review past runs
* Compare performance
* Identify patterns or failures

Execution history is critical for:

* Debugging
* Auditing
* System optimization

***

## Observability as a System Layer

Observability in Avigrah is not an add-on — it is built into the system.

Every execution is:

* Tracked
* Logged
* Measured
* Auditable

This ensures:

* Transparency in AI operations
* Confidence in outputs
* Control over system behavior

***

## How This Connects to AI

Executions directly impact AI performance.

* Pipelines prepare structured data
* Executions ensure data is up-to-date
* AI agents consume the latest processed data

Reliable execution = reliable AI outputs.

***

## Best Practices

### Monitor Active Executions

Track running pipelines to ensure smooth operation.

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### Use Logs for Debugging

Always refer to logs for understanding failures.

***

### Watch Metrics for Scaling

High usage or retries may indicate scaling needs.

***

### Maintain Healthy Pipelines

Consistent execution ensures consistent AI performance.

***

### Review Execution History

Use past runs to improve pipeline efficiency.