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Databases

Database engineering

Schema design, spatial and vector storage, migrations and performance tuning.

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Conceptual visualization only

Overview

Databases treated as long-lived assets: normalised where it matters, indexed for the queries you actually run, migrated safely, and tuned with evidence from query plans rather than guesswork.

Problems it solves

  • • Queries that slow down as data grows, with no index strategy
  • • Risky manual migrations without rollback
  • • Spatial or vector workloads forced into unsuitable schemas
  • • No visibility into slow queries or lock contention

Who it suits

  • • Platform and backend teams
  • • GIS teams running PostGIS
  • • Teams migrating between database engines

What this capability does

  • • Relational and spatial schema design
  • • Index, partition and query plan optimisation
  • • Safe migrations with rollback paths
  • • Vector and embedding storage design
  • • Caching strategy and connection management

How This AI Agent Works

The delivery process for database engineering: trigger, validation, approved tooling, human approval and the deliverable produced.

Database engineering workflow

From requirements to an approved, monitored schema change.

Workflow preview

Saved with the run so the recorded execution trail shows what was asked. Demonstration runs use sample data. They do not touch your systems.

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Public visitors can preview the structure and inspect every node. Labelled demonstrations and live runs require a signed-in account with configured providers.

72%
Workflow summary as a text list
  1. 1. Requirements: Trigger. Access patterns, volumes and retention rules.
  2. 2. Schema design: AI agent. Model entities, keys, indexes and constraints.
  3. 3. Migration plan: AI agent. Write forward and rollback migration steps.
  4. 4. Validation: Validation. Verify constraints, policies and row-level security.
  5. 5. Performance test: Approved tool. Benchmark the critical queries.
  6. 6. Change approval: Human approval. A database owner approves the migration. Human approval required.
  7. 7. Deploy migration: Action. Apply the approved migration.
  8. 8. Monitor: Monitoring. Track slow queries, locks and growth.
  9. 9. Schema package: Output. Migration files, policy set and performance report.

Approval gate: Change approval. Deliverable: Report.

Workflow audit for this page
Structure only. Run a demonstration to include events.
Workflow audit: template identifier, node count, approval node, output type, available execution modes and mobile test status.
Template IDNodesApproval nodeOutput typePreviewDemoLiveMobile test
database-engineering9Yes. Policy: alwaysReportAvailableAvailable (signed in)Available via Studio · requires ai_provider
Not yet covered

Demonstration runs are stored and replayed from recorded events. Live runs execute server-side in the Studio and only start when the listed integrations report healthy.

Supported industries

  • Government
  • Utilities
  • Banking and finance
  • Healthcare
  • Real estate

Technology stack

Every supported technology for this capability.

  • PostgreSQL
  • PostGIS
  • SQL Server
  • Oracle
  • MySQL
  • MongoDB
  • Redis
  • DuckDB
  • Vector databases
  • Performance tuning

Specialist AI agents

  • Database engineering agent

    Schema and index design

    Profile not published — this role runs inside the capability workflow.

  • SQL agent

    Query rewriting and plan analysis

    Profile not published — this role runs inside the capability workflow.

  • PostGIS agent

    Spatial types, indexes and functions

    Profile not published — this role runs inside the capability workflow.

  • Data migration agent

    Migration sequencing and rollback

    Profile not published — this role runs inside the capability workflow.

Workflow

Every run follows the same auditable spine.

  1. Step 1

    Request

    You describe the objective, constraints and delivery format.

  2. Step 2

    Validate inputs

    Files, schemas, credentials and scope are checked before any agent starts.

  3. Step 3

    Assign agent

    The registry resolves the specialist agent and its permitted tools server-side.

  4. Step 4

    Use approved tools

    Only tools allowed for that agent and your plan can execute.

  5. Step 5

    Process data

    The agent reviews the schema and workload, then proposes structures and indexes.

  6. Step 6

    Validate output

    Results are checked against the expected schema and quality rules.

  7. Step 7

    Human review if required

    Sensitive outputs are withheld until an approver releases them.

  8. Step 8

    Deliver result

    Approved deliverables are released to your workspace with an export trail.

Inputs and outputs

What you provide

  • • Current schema (DDL) or ER diagram
  • • Representative slow queries and plans
  • • Data volumes and growth rate
  • • Availability and migration constraints

What you receive

  • • Target schema and index plan
  • • Migration sequence with rollback steps
  • • Query rewrite recommendations
  • • Monitoring and maintenance checklist

Use cases

  • PostGIS schema for a national asset register
  • Query tuning for a reporting workload that outgrew its indexes
  • Oracle to PostgreSQL migration assessment
  • Vector store design for document retrieval

Integration status

Real configuration states recorded by health checks — nothing here is assumed to be connected.

  • AI providerSign in to view status

    The model gateway every agent run and consultant reply depends on.

  • DatabaseSign in to view status

    Run persistence, agent registry, approvals, usage and audit records.

Actions

Limitations: Schema review works from the DDL and query plans you provide; this app does not connect to your production database.