AI Agent

Database Agent

Database Agent that automates data & analytics workflows using your data and tools.

The challenge

Performance problems are noticed by users before the team, and tuning happens after an incident rather than before one. Index and statistics work competes with project delivery, capacity is guessed at from last year, and every change carries the risk of locking a production table.

The outcome

Azure Monitor and Log Analytics collect waits and slow queries, Azure OpenAI reasons over the execution plans, and a Microsoft Foundry agent proposes an index, statistic or rewrite with an estimated impact. Reversible work applies in the maintenance window through Azure Automation; schema change waits for a DBA.

At a glance

Type: ai agents
Category: technical

Next step

Move from solution to engagement.

Build This Solution

01 — Architecture

End-to-end architecture

Wait statistics, slow queries and growth trends are collected continuously. The Foundry agent traces a hot query to its plan, indexes and schema, proposes a change, estimates the impact and assesses the rollback path — applying only reversible, low-risk work inside the agreed maintenance window.

Figure 1 — End-to-end reference architecture for a database performance agent on the Microsoft stack.

02 — Workflow

Process & decision workflow

How a performance problem is observed, diagnosed and corrected — then branched. Low-risk, reversible changes are applied inside the window and the gain verified, while schema changes and high-impact work go to a DBA with the plan, the estimate and the rollback path.

Figure 2 — Observe → diagnose → propose → simulate → check → risk branch → apply or DBA approval.

03 — Components

Key Microsoft components

Database change is high consequence — telemetry, proposal, simulation and rollback all stay on the Microsoft stack.

Azure SQLRelational estate, query store and performance insights.
Azure Cosmos DBDocument and key-value workloads with request-unit metrics.
Azure MonitorResource utilisation, wait statistics and alerting.
Azure Log AnalyticsSlow query logs, deadlock graphs and trend queries.
Microsoft Foundry Agent ServiceDiagnosis, proposal and change orchestration with tools.
Azure OpenAI modelsExecution-plan reasoning and query rewrite suggestions.
Azure AI SearchRetrieval over schema documentation and tuning runbooks.
Azure AI Content SafetyGuardrails on generated data-definition statements.
Azure AutomationMaintenance windows, index jobs and rollback runbooks.
Azure FunctionsTelemetry collectors, baselines and growth forecasting.
Azure API ManagementSecure gateway for data and administration APIs.
Azure Blob StorageBackups, exports and restore verification artefacts.
Azure Key VaultConnection secrets, certificates and managed identities.
Power BI / Microsoft FabricPerformance, capacity and cost dashboards.

04 — AI

What the agent consumes

The capabilities the agent applies to the estate, and the line it does not cross.

AI capabilities embedded in the agent

Telemetry collection
Slow-query analysis
Execution-plan reasoning
Index recommendation
Statistics maintenance
Capacity and growth forecasting
Retrieval-augmented generation
Blast-radius estimation
Rollback planning
Backup verification
Cost attribution
Approval routing

AI responsibility boundaries

The agent applies only reversible, low-risk changes inside an agreed maintenance window. Schema changes, data migrations and anything touching a production write path require DBA approval. It never disables a backup, drops an object or bypasses the change window, and every action records the plan, the estimate and the rollback path.

05 — Personalization

Personalization & evolving process

The same methodology applies to every agent in the catalog. Tune the estate, the tuning template, the change rules and the value model — the page structure stays identical.

Estate & workload profile

Define the database platforms, tiers, workload patterns, maintenance windows and service levels in scope. The personas are the database administrator, the application developer, the platform team and the service owner.

06 — Impact

Key outcomes & business impact

Starting targets for the value case — validate each one against the customer baseline during discovery.

Query performance2×Hot paths tuned continuously rather than after an incident.
Incident preventionProactiveCapacity and growth risks surface before they page someone.
Administrator toil−50%Routine maintenance runs from approved runbooks.
Change evidence100%Each change stores its plan, estimate and rollback path.