Software Engineering Agent
Drafts implementation changes against an issue and repo conventions
Tool: Static analysis · Source: Git repositories
Industry
Technology AI agent operations
Repository, CI pipeline, cloud infrastructure and production telemetry
Operational layers in view
Code repositoryAPI callsCloud infrastructureTest pipelineSecurity scanDeployments
Software Engineering Agent
Code Review Agent
Testing Agent
Security Agent
DevOps Agent
Monitoring Agent
Node activity below is a demonstration using sample technology data.
Ask the live agents
Describe the problem in your own words. We classify the industry, assign specialist agents, and you watch the workflow run and produce the visual deliverables — maps, dashboards, charts, tables and reports — instead of one block of text.
Demo mode uses clearly labelled sample data — never presented as verified.
Assigned specialists
Technology
- Software Engineering Agent
- Code Review Agent
- Testing Agent
- Security Agent
- DevOps Agent
- Monitoring Agent
Deliverables planned
Workflow diagram
Data tables
Charts
KPI panel
Annotated document
Pauses for human approval
Human release approval
Workflow the run follows
- 1. User request
- 2. Requirement validation
- 3. Industry classification
- 4. Specialist agent selection
- 5. Data and file collection
- 6. Tool authorisation
- 7. Execution plan
- 8. Multi-agent collaboration
- 9. Analysis or generation
- 10. Quality validation
- 11. Human approval
- 12. Visual deliverables
- 13. Monitoring and audit
Demo
Technology AI agent operations centre
Workflow: Issue or alert received → Monitor production · Run pending · started — · elapsed 0s
Active agents
0
Tasks running
0
Completed steps
0/9
Tool calls
0
Human approvals
1
Warnings
0
Success rate
0%
Est. completion
36s
Active agents
Pending
Code Review Agent
Reviews diffs for correctness, style and regressions
Tool: Static analysis · Source: Git repositories
Testing Agent
Generates and executes unit, integration and e2e checks
Tool: Static analysis · Source: Git repositories
Security Agent
Runs dependency, secret and static-analysis scans
Tool: Static analysis · Source: Git repositories
Workflow canvas
Live activity stream
No activity yet. Start the demo run to watch technology agents work through the workflow with sample data.
Demonstration using sample data — no real client operation is shown. Sign in and connect an approved AI provider and authorised industry data source to activate live execution.
Technology specialist agents
Each agent owns one step of the workflow, with its own tools, inputs and approval level.
Autonomous
Software Engineering Agent
Drafts implementation changes against an issue and repo conventions
- Inputs:
- Git repositories, CI/CD runs, Cloud inventory
- Tools:
- Static analysis, Unit and e2e runners, SAST/DAST scanner
Review advised
Code Review Agent
Reviews diffs for correctness, style and regressions
- Inputs:
- Git repositories, CI/CD runs, Cloud inventory
- Tools:
- Static analysis, Unit and e2e runners, SAST/DAST scanner
Autonomous
Testing Agent
Generates and executes unit, integration and e2e checks
- Inputs:
- Git repositories, CI/CD runs, Cloud inventory
- Tools:
- Static analysis, Unit and e2e runners, SAST/DAST scanner
Approval required
Security Agent
Runs dependency, secret and static-analysis scans
- Inputs:
- Git repositories, CI/CD runs, Cloud inventory
- Tools:
- Static analysis, Unit and e2e runners, SAST/DAST scanner
Approval required
DevOps Agent
Plans infrastructure changes and deployment steps
- Inputs:
- Git repositories, CI/CD runs, Cloud inventory
- Tools:
- Static analysis, Unit and e2e runners, SAST/DAST scanner
Autonomous
Monitoring Agent
Watches error rates and latency after release
- Inputs:
- Git repositories, CI/CD runs, Cloud inventory
- Tools:
- Static analysis, Unit and e2e runners, SAST/DAST scanner
Solution blueprint
Operational context
Industry challenges
- Operational data for technology is spread across disconnected systems
- Analysis-heavy work is repeated manually every cycle
- Risks and exceptions are found late
- Reporting and audit overhead grows with volume
Solution patterns
Recommended AI solutions
- Specialist agents that own one step of the workflow each
- Grounded retrieval over your permitted systems only
- Predictive scoring with explainable reasons
- Approval-gated execution into your systems of record
Data readiness
Required data
- Git repositories
- CI/CD runs
- Cloud inventory
- Error and APM telemetry
- Dependency manifests
Controlled process
Example workflow
- Issue or alert received
- Validate repository context
- Draft change
- Review diff
- Run tests
Connection points
Integration opportunities
- GitHub / GitLab
- CI runners
- Cloud provider APIs
- Observability stack
Measure after baseline
Expected outcomes
- Analysis cycle time
- Manual review effort
- Decision auditability
- Earlier risk detection
Governance controls
Security and compliance
- Row-level security scopes every read to the requesting organisation
- Retrieval is limited to documents the user may already open
- Every agent action, tool call and approval is written to the audit log
- Sensitive steps pause until a named human approves them
Inputs, integrations and outputs
Inputs
- • Git repositories
- • CI/CD runs
- • Cloud inventory
- • Error and APM telemetry
- • Dependency manifests
Integrations
- • GitHub / GitLab
- • CI runners
- • Cloud provider APIs
- • Observability stack
Outputs
- • Change summary (PDF, JSON)
- • Test and scan report (CSV, PDF)
- • Release dashboard (Dashboard)
Business outcomes to baseline and measure
Analysis cycle time
Target and measurement method agreed during discovery.
Manual review effort
Target and measurement method agreed during discovery.
Decision auditability
Target and measurement method agreed during discovery.
Earlier risk detection
Target and measurement method agreed during discovery.
No performance result is promised before a baseline is measured. Targets are agreed for each engagement and reported against real delivery data.
How This AI Agent Works
The process the technology agent team follows, from trigger to approved deliverable.
API engineering workflow
From API requirements to a secured, monitored deployment.
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.
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. 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
Structure only. Run a demonstration to include events.
| 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.
Relevant case study
Illustrative case study
Wastewater network GIS for a national water utility
A wastewater network needed utility-GIS structuring, mapping and quality-controlled network intelligence for daily operations.
View all case studiesReady to scope your AI initiative?
Share your objective and constraints. A specialist reviews every request before any recommendation is issued.