> For the complete documentation index, see [llms.txt](https://docs.opsmx.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.opsmx.com/context-engine/context-supporting-layers/progressive-discovery-and-learning.md).

# Progressive Discovery & Learning

Progressive Discovery & Learning is the **adaptive intelligence layer** of the Context Engine — the capability that enables the OpsMx platform to continuously improve its understanding of an organization's unique environment over time. Rather than operating on static rules or fixed baselines, this layer learns from new data, interactions, and outcomes — making AI-driven insights progressively more accurate, relevant, and predictive with every cycle.

In a software delivery environment, patterns evolve constantly — deployment frequencies change, new services are added, failure modes shift, and team behaviors adapt. A context engine that does not learn from these changes quickly becomes out of date. Progressive Discovery & Learning ensures the Context Engine stays current and continues to deepen its understanding of the specific environment it is operating in.

{% hint style="info" %}
Progressive Discovery & Learning is what transforms OpsMx from a tool that reacts to events into a platform that anticipates them — moving from reactive analysis to proactive, predictive intelligence.
{% endhint %}

## **What Progressive Discovery & Learning Captures**

| Learning Area                    | What Gets Learned                                                                                   |
| -------------------------------- | --------------------------------------------------------------------------------------------------- |
| **Pipeline Behavior**            | Typical build durations, failure rates per stage, common failure patterns per repository            |
| **Deployment Patterns**          | Normal deployment frequency, typical promotion timelines, rollback triggers                         |
| **Security Trends**              | Recurring vulnerability types per team or service, remediation time patterns, exception frequency   |
| **Runtime Baselines**            | Normal resource usage, API call volumes, network traffic patterns per service and environment       |
| **Incident Correlations**        | Which combinations of events and signals historically preceded production incidents                 |
| **Remediation Outcomes**         | Which fix actions resolved which categories of issues — used to improve future recommendations      |
| **Team & Service Relationships** | How services interact, which teams own which components, ownership patterns across the organization |


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.opsmx.com/context-engine/context-supporting-layers/progressive-discovery-and-learning.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.