fabric-data-agent-perf-remediate

name	fabric-data-agent-perf-remediate
description	Diagnose and resolve Microsoft Fabric Data Agent performance issues including slow query generation, capacity throttling (HTTP 430), Spark session startup delays, KQL/SQL/DAX query timeouts, data source misconfiguration, example query validation failures, resource profile tuning, VOrder optimization, autotune settings, and Lakehouse table maintenance. Use when asked to troubleshoot Fabric Data Agent response times, fix agent query accuracy, debug Operations Agent playbook performance, resolve capacity SKU limits, optimize Spark compute for agents, or diagnose Data Agent data source connection issues.
license	Complete terms in LICENSE.txt

Fabric Data Agent Performance remediate

Systematic toolkit for diagnosing and resolving performance issues in Microsoft Fabric Data Agents, Operations Agents, and their underlying Spark compute and data source infrastructure.

When to Use This Skill

• Data Agent responses are slow or timing out
• Agent-generated SQL/KQL/DAX queries return errors or produce incorrect results
• Spark session startup takes longer than expected (>10 seconds)
• Capacity throttling errors (HTTP 430) during agent workloads
• Operations Agent playbooks fail to execute or trigger actions
• Data source connections are unreliable or stale
• Example queries fail validation against schema
• Resource profiles need tuning for agent-specific workloads
• Table maintenance (VOrder, bin-compaction, vacuum) is overdue
• Autoscale Billing configuration needs optimization

Prerequisites

• Access: Fabric workspace Contributor or Admin role
• Tools: PowerShell 7+, Fabric REST API access (Microsoft Entra token)
• Endpoints: https://api.fabric.microsoft.com/v1/workspaces/{workspaceId}/items
• Monitoring: Access to Fabric Admin Portal and Azure Cost Analysis
• Optional: Azure subscription admin for SKU resizing and Autoscale Billing

Diagnostic Decision Tree

Agent performance issue reported
├── Slow response times?
│   ├── First query after idle? → Spark session startup (see Workflow 1)
│   ├── All queries slow? → Resource profile mismatch (see Workflow 2)
│   └── Intermittent slowness? → Capacity throttling (see Workflow 3)
├── Incorrect query results?
│   ├── Wrong tables/columns? → Data source instructions (see Workflow 4)
│   ├── SQL syntax errors? → Example query validation (see Workflow 5)
│   └── Stale data? → Lakehouse table maintenance (see Workflow 6)
├── Agent not responding?
│   ├── HTTP 430 errors? → Concurrency limits (see Workflow 3)
│   ├── Connection failures? → Data source config (see Workflow 4)
│   └── Operations Agent stuck? → Playbook/action config (see Workflow 7)
└── Cost concerns?
    ├── Unexpected charges? → Autoscale Billing audit (see Workflow 8)
    └── Over-provisioned? → SKU right-sizing (see Workflow 8)

Step-by-Step Workflows

Workflow 1: Spark Session Startup Delays

Symptoms: First query takes 2-5 minutes, subsequent queries are fast.

Scenario	Expected Startup
Default, no custom libraries	5-10 seconds
Default + library dependencies	35 seconds - 5 minutes
High regional traffic	2-5 minutes
Private Links / Managed VNet	2-5 minutes
Network security + libraries	2.5-10 minutes

Resolution steps: See workflow-spark-startup.md

Workflow 2: Resource Profile Optimization

Symptoms: Consistently slow reads or writes across all agent queries.

Profile	Use Case	VOrder	OptimizeWrite
writeHeavy	High-frequency ingestion (default)	Disabled	Disabled
readHeavyForPBI	Power BI dashboard queries	Enabled	Enabled (1GB bin)
readHeavyForSpark	Interactive Spark analytics	Disabled	Enabled (128MB bin)
custom	User-defined workload tuning	Configurable	Configurable

Resolution steps: See workflow-resource-profiles.md

Workflow 3: Capacity Throttling (HTTP 430)

Symptoms: TooManyRequestsForCapacity errors, jobs queued or rejected.

SKU	Spark VCores	Queue Limit
F2	4	4
F8	16	8
F64	128	64
F128	256	128
F256	512	256

Formula: 1 Capacity Unit = 2 Spark VCores

Resolution steps: See workflow-capacity-throttling.md

Workflow 4: Data Source Configuration Issues

Symptoms: Agent queries wrong tables, returns irrelevant results, or fails to connect.

Data Agents use three configuration layers that all affect query quality:

1. Agent Instructions — Global routing rules (which data source for which topic)
2. Data Source Instructions — Schema context, table descriptions, column details
3. Example Queries — Few-shot SQL/KQL/DAX examples for query generation

Resolution steps: See workflow-data-source-config.md

Workflow 5: Example Query Validation

Symptoms: Agent ignores example queries, generates incorrect SQL/KQL syntax.

Key rule: The Fabric Data Agent only uses queries that contain valid SQL/KQL syntax AND match the schema of the selected tables. Queries that fail validation are silently ignored.

Resolution steps: See workflow-example-queries.md

Workflow 6: Lakehouse Table Maintenance

Symptoms: Queries over Delta tables are slow, small file problem, stale statistics.

Three maintenance operations available via REST API:

Operation	Purpose
Bin-compaction	Consolidate small files into optimal sizes
V-Order	Optimize Parquet layout for read performance
Vacuum	Remove unreferenced files older than retention

Resolution steps: See workflow-table-maintenance.md

Workflow 7: Operations Agent Debugging

Symptoms: Operations Agent not triggering actions, playbook failures.

Operations Agent definition requires: goals, instructions, at least one KustoDatabase data source, at least one PowerAutomateAction, and shouldRun: true.

Resolution steps: See workflow-operations-agent.md

Workflow 8: Autoscale Billing and SKU Right-Sizing

Symptoms: Unexpected costs, capacity contention between Spark and other workloads.

Resolution steps: See workflow-autoscale-billing.md

Available Scripts

Script	Purpose
Get-FabricAgentDiagnostics.ps1	Collect agent config, capacity state, and Spark metrics
Test-ExampleQueries.ps1	Validate example queries against data source schema
Invoke-TableMaintenance.ps1	Run bin-compaction, V-Order, and vacuum on Lakehouse tables

remediate Quick Reference

Error	Likely Cause	First Action
HTTP 430	Capacity VCore limit	Check Monitoring Hub for active jobs
Query timeout	Resource profile mismatch	Switch to readHeavyForSpark
Wrong columns	Missing data source instructions	Update schema descriptions
Ignored examples	Invalid SQL/KQL syntax	Validate with Test-ExampleQueries.ps1
2-5 min startup	Private Links or high traffic	Check workspace networking config
Stale results	Missing table maintenance	Run bin-compaction + V-Order
Agent not running	shouldRun: false	Check Operations Agent definition
Autotune disabled	Runtime > 1.2 or HC mode	Verify Fabric Runtime version

References

• Spark Startup Workflow
• Resource Profile Workflow
• Capacity Throttling Workflow
• Data Source Config Workflow
• Example Query Workflow
• Table Maintenance Workflow
• Operations Agent Workflow
• Autoscale Billing Workflow