docs: AI agent capabilities, remove context engine page, link release notes to GitHub

docs/cloud/ai-agents/catalog-agent.mdx → docs/cloud/ai-agents/ai-catalog.mdx

@@ -1,18 +1,18 @@
 ---
-title: "Catalog Agent"
-sidebarTitle: "Catalog agent"
+title: "AI Catalog"
+sidebarTitle: "AI Catalog"
 icon: "book-open"
 badge: "Elementary Cloud"
 ---
 
-<Card title="Elementary AI Agent — all capabilities" icon="sparkles" href="/cloud/ai-agents/overview" horizontal="true">
+<Card title="Elementary AI Agent: all capabilities" icon="sparkles" href="/cloud/ai-agents/overview" horizontal="true">
 </Card>
 
-**The Catalog Agent helps data analysts, business users, and AI tools find the right data on their own so engineers don't have to be the bottleneck.** By interpreting natural language questions and surfacing the right context—including data health, lineage, and metric definitions—it enables faster, more confident access to the data people need. The Catalog Agent lays the groundwork for more accurate and effective AI-powered workflows across your organization.
+**AI Catalog helps data analysts, business users, and AI tools find the right data on their own so engineers don't have to be the bottleneck.** It's the Elementary AI Agent's capability for data discovery. By interpreting natural language questions and surfacing the right context—including data health, lineage, and metric definitions—it enables faster, more confident access to the data people need. AI Catalog lays the groundwork for more accurate and effective AI-powered workflows across your organization.
 
-### Using the Catalog Agent
+### Using AI Catalog
 
-Open the Catalog Agent to start a conversation. Just ask a question like **“Which table should I use to analyze user retention?”** or **“Can I trust the orders table?”** and the agent will guide you to the right assets or insights.
+Open the agent to start a conversation. Just ask a question like **“Which table should I use to analyze user retention?”** or **“Can I trust the orders table?”** and the agent will guide you to the right assets or insights.
 The agent will interpret your request using metadata, lineage, and semantic context, and suggest relevant datasets, tables, or metrics. It will also surface freshness, incident status, and test results, and explain how the data is used and whether it can be trusted.
 
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@@ -50,7 +50,7 @@ For every asset, the agent provides real-time indicators of reliability:
 
 ### Who It’s For
 
-The Catalog Agent is built for **data analysts**, **business users**, **AI copilots**, and **intelligent applications** that need to find and understand the right data without making the data team a bottleneck.
+AI Catalog is built for **data analysts**, **business users**, **AI copilots**, and **intelligent applications** that need to find and understand the right data without making the data team a bottleneck.
 
 <Check>We take data privacy seriously. Read our [**AI Privacy Policy**](/cloud/general/ai-privacy-policy) to understand how AI features are secured.</Check>
 

docs/cloud/ai-agents/ai-credits.mdx

@@ -14,10 +14,10 @@ With [MCP](/cloud/mcp/overview), Elementary returns data to the connected agent.
 
 | Action | What the AI does |
 |---|---|
-| [**Incident investigation**](/cloud/ai-agents/triage-resolution-agent) | Diagnoses a data issue: root cause, impacted tables, suggested fix |
-| [**Data discovery**](/cloud/ai-agents/catalog-agent) | Identifies which tables or columns to use for a specific metric or use case |
-| [**Test coverage**](/cloud/ai-agents/test-recommendation-agent) | Generates dbt test PRs based on configured data policies and opens them for review |
-| [**CI PR review**](/cloud/features/ci) | Lineage-aware review on every dbt PR, surfacing downstream impact before merge |
+| [**Incident investigation**](/cloud/ai-agents/ai-triage-resolution) | Diagnoses a data issue: root cause, impacted tables, suggested fix |
+| [**Data discovery**](/cloud/ai-agents/ai-catalog) | Identifies which tables or columns to use for a specific metric or use case |
+| [**Test coverage**](/cloud/ai-agents/ai-test-coverage) | Generates dbt test PRs based on configured data policies and opens them for review |
+| [**AI Code Review**](/cloud/features/ci) | Lineage-aware review on every dbt PR, surfacing downstream impact before merge |
 
 ---
 

docs/cloud/ai-agents/governance-agent.mdx → docs/cloud/ai-agents/ai-governance.mdx

@@ -1,22 +1,22 @@
 ---
-title: "Governance Agent"
-sidebarTitle: "Governance agent"
+title: "AI Governance"
+sidebarTitle: "AI Governance"
 icon: "tags"
 badge: "Elementary Cloud"
 ---
 
-<Card title="Elementary AI Agent — all capabilities" icon="sparkles" href="/cloud/ai-agents/overview" horizontal="true">
+<Card title="Elementary AI Agent: all capabilities" icon="sparkles" href="/cloud/ai-agents/overview" horizontal="true">
 </Card>
 
 **Governing your data isn't just about internal organization, it's the key to making your data reliably usable by both humans and AI.** 
 
-The Governance Agent helps you scale and maintain trusted, well-documented data assets without manual effort. It evaluates your metadata, identifies gaps, and suggests improvements, all based on how your organization already tags, documents, and assigns ownership. 
+AI Governance is the Elementary AI Agent's capability for keeping data assets documented, owned, and classified without manual effort. The agent evaluates your metadata, identifies gaps, and suggests improvements, all based on how your organization already tags, documents, and assigns ownership. 
 
-By learning from your environment’s existing conventions, the agent brings consistency, policy enforcement, and AI-readiness across your pipelines.
+By learning from your environment’s existing conventions, it brings consistency, policy enforcement, and AI-readiness across your pipelines.
 
-### **Using the Governance Agent**
+### **Using AI Governance**
 
-You can trigger the Governance Agent from the Catalog screen, and ask it a question like **"Which tables in the marketing domain are missing ownership?"**. It will analyze the asset’s metadata and context, validate it against both best practices and your custom policies, and generate a list of actionable suggestions. When you’re ready, the agent can open a pull request with the proposed metadata changes.
+Open the agent from the Catalog screen and ask it a question like **"Which tables in the marketing domain are missing ownership?"**. It will analyze the asset’s metadata and context, validate it against both best practices and your custom policies, and generate a list of actionable suggestions. When you’re ready, the agent can open a pull request with the proposed metadata changes.
 
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docs/cloud/ai-agents/performance-cost-agent.mdx → docs/cloud/ai-agents/ai-performance-cost.mdx

@@ -1,19 +1,19 @@
 ---
-title: "Performance & Cost Agent"
-sidebarTitle: "Performance & cost agent"
+title: "AI Performance & Cost"
+sidebarTitle: "AI Performance & Cost"
 icon: "comments-dollar"
 badge: "Elementary Cloud"
 ---
 
-<Card title="Elementary AI Agent — all capabilities" icon="sparkles" href="/cloud/ai-agents/overview" horizontal="true">
+<Card title="Elementary AI Agent: all capabilities" icon="sparkles" href="/cloud/ai-agents/overview" horizontal="true">
 </Card>
 
-**Optimize long-running queries, eliminate bottlenecks, and keep compute costs under control without digging through logs or rewriting SQL by hand.** The Performance and Cost Agent helps you identify inefficiencies in your pipelines and resolve them quickly, so your data stays fresh, fast, and within budget.
+**Optimize long-running queries, eliminate bottlenecks, and keep compute costs under control without digging through logs or rewriting SQL by hand.** AI Performance & Cost is the Elementary AI Agent's capability for finding inefficiencies in your pipelines and resolving them quickly, so your data stays fresh, fast, and within budget.
 
 It improves performance by analyzing query logic, historical execution patterns, and resource usage. This makes it easier to meet data SLAs and ensure products, dashboards, and AI models get the data they need, when they need it.
 
-### Using the Performance and Cost Agent
-Trigger the agent from any asset flagged as underperforming or from a query known to be slow or expensive. It reviews the SQL, surrounding metadata, and historical performance, then recommends code changes and resource configuration tweaks to improve efficiency.
+### Using AI Performance & Cost
+Open the agent from any asset flagged as underperforming or from a query known to be slow or expensive. It reviews the SQL, surrounding metadata, and historical performance, then recommends code changes and resource configuration tweaks to improve efficiency.
 
 You’ll see an estimate of time and cost savings before applying changes. Once approved, the agent opens a pull request with its suggested improvements and can continue iterating until performance targets are met.
 

docs/cloud/ai-agents/test-recommendation-agent.mdx → docs/cloud/ai-agents/ai-test-coverage.mdx

@@ -1,18 +1,18 @@
 ---
-title: "Test Recommendation Agent"
-sidebarTitle: "Test recommendation agent"
+title: "AI Test Coverage"
+sidebarTitle: "AI Test Coverage"
 icon: "vial-circle-check"
 badge: "Elementary Cloud"
 ---
 
-<Card title="Elementary AI Agent — all capabilities" icon="sparkles" href="/cloud/ai-agents/overview" horizontal="true">
+<Card title="Elementary AI Agent: all capabilities" icon="sparkles" href="/cloud/ai-agents/overview" horizontal="true">
 </Card>
 
-**Make sure you’re covered—not by how many tests you write, but by how many problems you catch.** The Test Recommendation Agent analyzes your data asset, metadata, lineage, and existing test suite to suggest high-impact tests that match your team’s style and priorities.
+**Make sure you’re covered—not by how many tests you write, but by how many problems you catch.** AI Test Coverage is the Elementary AI Agent's capability for finding coverage gaps and closing them. The agent analyzes your data assets, metadata, lineage, and existing test suite to suggest high-impact tests that match your team’s style and priorities.
 
 It helps you expand meaningful coverage, reduce alert fatigue, and maintain a reliable test strategy as your pipelines evolve.
 
-### Using the Test Recommendation Agent
+### Using AI Test Coverage
 Open the agent from the test configuration screen and ask a question like **"What tests can ensure all of my customer data is up to date?"** or **"Which upstream tables need tests to protect my revenue dashboard?"**. The agent generates recommendations based on asset type, criticality, lineage, and your existing test configurations. You can approve, adjust, or skip suggestions as needed. Once approved, the agent opens a pull request with changes that follow your team’s current test structure.
 
 

docs/cloud/ai-agents/triage-resolution-agent.mdx → docs/cloud/ai-agents/ai-triage-resolution.mdx

@@ -1,20 +1,20 @@
 ---
-title: "Triage & Resolution Agent"
-sidebarTitle: "Triage & resolution agent"
+title: "AI Triage & Resolution"
+sidebarTitle: "AI Triage & Resolution"
 icon: "toolbox"
 badge: "Elementary Cloud"
 ---
 
-<Card title="Elementary AI Agent — all capabilities" icon="sparkles" href="/cloud/ai-agents/overview" horizontal="true">
+<Card title="Elementary AI Agent: all capabilities" icon="sparkles" href="/cloud/ai-agents/overview" horizontal="true">
 </Card>
 
 **Automates root cause analysis, clarifies the impact, and suggests actionable fixes so you spend less time triaging and more time solving what matters.** The result is a shift from manual investigation to a faster, more focused workflow, allowing you to focus on delivering business value rather than fighting fires.
 
-Whether it's a failed test, a model issue, or an unexpected anomaly, it inspects recent code changes, execution history, lineage, and, when allowed, the data itself. It prioritizes the incidents that matter, proposes a fix, and can open a pull request for you. That way, incidents stop slowing you down, and pipelines begin to fix themselves.
+AI Triage & Resolution is the Elementary AI Agent's capability for investigating and fixing incidents. Whether it's a failed test, a model issue, or an unexpected anomaly, the agent inspects recent code changes, execution history, lineage, and, when allowed, the data itself. It prioritizes the incidents that matter, proposes a fix, and can open a pull request for you. That way, incidents stop slowing you down, and pipelines begin to fix themselves.
 
-## Using the Triage & Resolution agent
+## Using AI Triage & Resolution
 
-From the Incident Management screen click **“Investigate with AI”** to trigger the agent. It will begin analyzing the incident and surface insights. You can follow along, ask clarifying questions, and when you're ready, allow the agent to open a pull request with its suggested fix - all within your review flow.
+From the Incident Management screen click **“Investigate with AI”** to start an investigation. The agent will begin analyzing the incident and surface insights. You can follow along, ask clarifying questions, and when you're ready, allow the agent to open a pull request with its suggested fix - all within your review flow.
 
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