API and application engineering
Typed APIs, authentication, background workers and service architecture you can operate.
Conceptual visualization only
Overview
Service interfaces designed contract-first: validated schemas, explicit auth, predictable errors, background work for anything slow, and observability from day one. Built to integrate with the systems you already run rather than replace them.
Problems it solves
- • Undocumented endpoints with inconsistent error shapes
- • Long-running requests timing out instead of queueing
- • Authentication and authorisation bolted on late
- • No way to trace a failing request end to end
Who it suits
- • Product engineering teams
- • Integration and platform teams
- • Businesses exposing data to partners
What this capability does
- • OpenAPI-first service design
- • Authentication, roles and permission models
- • Background jobs, queues and scheduling
- • Realtime channels and streaming responses
- • Service decomposition and integration contracts
How This AI Agent Works
The delivery process for api and application engineering: trigger, validation, approved tooling, human approval and the deliverable produced.
API engineering workflow
From API requirements to a secured, monitored deployment.
Saved with the run so the recorded execution trail shows what was asked. Demonstration runs use sample data. They do not touch your systems.
Public visitors can preview the structure and inspect every node. Labelled demonstrations and live runs require a signed-in account with configured providers.
Workflow summary as a text list
- 1. API requirements: Trigger. Consumers, payloads, limits and SLAs.
- 2. Contract design: AI agent. Draft the OpenAPI contract and versioning policy.
- 3. Authentication: Action. Implement authentication, scopes and rate limits.
- 4. Implementation: Action. Build handlers against the agreed contract.
- 5. Testing: Validation. Run contract, integration and load tests.
- 6. Security review: Validation. Check authz, exposure, logging and input validation.
- 7. Deployment approval: Human approval. An owner approves the release. Human approval required.
- 8. Monitoring: Monitoring. Track error rates, latency and consumer usage.
- 9. API package: Output. Deployed API, contract and consumer documentation.
Approval gate: Deployment approval. Deliverable: Report.
Workflow audit for this page
| Template ID | Nodes | Approval node | Output type | Preview | Demo | Live | Mobile test |
|---|---|---|---|---|---|---|---|
| api-application-engineering | 9 | Yes. Policy: always | Report | Available | Available (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
- Technology
- Telecommunications
- Banking and finance
- Transportation and logistics
Technology stack
Every supported technology for this capability.
- FastAPI
- Flask
- Django
- REST APIs
- GraphQL
- WebSockets
- Authentication
- Background workers
- Microservices
Specialist AI agents
API engineering agent
Endpoint and schema design
Profile not published — this role runs inside the capability workflow.
Application architect agent
Service boundaries and data flow
Profile not published — this role runs inside the capability workflow.
Security review agent
Auth, exposure and compliance review
Open agent profileTesting agent
Contract and integration test plan
Profile not published — this role runs inside the capability workflow.
Workflow
Every run follows the same auditable spine.
- Step 1
Request
You describe the objective, constraints and delivery format.
- Step 2
Validate inputs
Files, schemas, credentials and scope are checked before any agent starts.
- Step 3
Assign agent
The registry resolves the specialist agent and its permitted tools server-side.
- Step 4
Use approved tools
Only tools allowed for that agent and your plan can execute.
- Step 5
Process data
The agent drafts the endpoint list, schemas, auth model and error contract.
- Step 6
Validate output
Results are checked against the expected schema and quality rules.
- Step 7
Human review if required
Sensitive outputs are withheld until an approver releases them.
- Step 8
Deliver result
Approved deliverables are released to your workspace with an export trail.
Inputs and outputs
What you provide
- • Functional requirements or user stories
- • Existing schemas or sample payloads
- • Auth and tenancy model
- • Non-functional targets (throughput, latency)
What you receive
- • Endpoint and schema specification
- • Auth and permission matrix
- • Error contract and status codes
- • Test and rollout plan
Use cases
- Partner-facing data API with keys, quotas and audit logging
- Internal service exposing spatial analysis to business apps
- Webhook ingestion pipeline with signature verification
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.
- AuthenticationSign in to view status
Sign-in, sessions and the bearer token every protected call carries.