AI AgentSoftware Development Agent
Software Development Agent that automates it & engineering workflows using your data and tools.
The challenge
Engineers spend the day on scaffolding, wiring and test boilerplate instead of design. Context is scattered across the codebase, old decision records and half-remembered conversations, and review queues grow because changes arrive without an explanation of why they look the way they do.
The outcome
Azure AI Search retrieves the codebase and decision records, a Microsoft Foundry agent plans the change, and Azure OpenAI implements it to the house patterns with tests. Secrets, licences and vulnerabilities are scanned before a pull request opens, and a human reviews and merges every change.
At a glance
- Type
- ai agents
- Category
- technical
01 — Architecture
End-to-end architecture
An issue is read against the codebase and its prior decisions. The Foundry agent proposes an approach, writes the change to the house patterns, generates tests and runs the security and licence scans — then opens a pull request. A human reviews and merges every change; the agent never pushes to a protected branch.
- Developer: Developer / Lead
- Source: GitHub, Teams / Boards
- Build: Container Registry, Azure Load Testing
- AI & agent: Microsoft Foundry Agent, Azure OpenAI models, Azure AI Search, Azure AI Content Safety
- Data & records: Azure Cosmos DB, Azure Blob Storage, Azure Key Vault
- Review & insight: Human code review, Application Insights, Power BI / Fabric
02 — Workflow
Process & decision workflow
How an issue becomes a reviewable change — understand, plan, implement, test and scan, then branch. A green, in-scope change opens a pull request with its rationale, while failing checks or scope creep stop and go to a human first. Failing checks stay visible rather than being worked around.
- Understand: Issue read against the codebase and prior decisions
- Plan: Approach proposed with the files it will touch
- Implement: Change written to the house style and patterns
- Test: Unit, integration and regression tests executed
- Scan: Secrets, licences and vulnerabilities checked
- Ship: Pull request opened with rationale and evidence
- Path 1 · green and in scope — Open the pull request: Change proposed with tests and rationale
- Path 2 · failing or wider than asked — Human review first: Diff, failures and open questions presented
03 — Components
Key Microsoft components
Generated code is only safe behind real gates — retrieval, generation, testing, scanning and human review all stay on the Microsoft stack.
GitHubRepositories, pull requests, protected branches and CI.
Microsoft Teams / BoardsWork items, discussion and delivery context.
Microsoft Foundry Agent ServicePlanning, implementation and review orchestration.
Azure OpenAI modelsCode generation, test authoring and change explanation.
Azure AI SearchRetrieval over the codebase, decision records and docs.
Azure AI Content SafetySecret, licence and unsafe-pattern scanning.
Azure Load TestingPerformance and regression gates before promotion.
Azure Container RegistryVersioned build artefacts with provenance.
Azure Kubernetes ServiceProgressive release with health gates and rollback.
Azure Application InsightsPost-release behaviour, errors and performance.
Azure Cosmos DBTask, branch and agent execution state.
Azure Blob StorageBuild artefacts, scan reports and test evidence.
Azure Key VaultBuild credentials, signing keys and secrets.
Power BI / Microsoft FabricLead time, review load and defect dashboards.
04 — AI
What the agent consumes
The capabilities the agent applies to every change, and the line it does not cross.
AI capabilities embedded in the agent
- Codebase retrieval
- Requirement interpretation
- Change planning
- Code generation
- Test generation
- Static analysis
- Secret and licence scanning
- Vulnerability detection
- Diff summarisation
- Pull-request drafting
- Flow metrics
- Human review routing
AI responsibility boundaries
The agent proposes changes through a pull request; a human reviews and merges every one. It does not push to a protected branch, widen scope beyond the issue, weaken a test to make a build pass, or commit a secret. Failing checks stay failing and visible rather than being worked around.
05 — Personalization
Personalization & evolving process
The same methodology applies to every agent in the catalog. Tune the codebase, the change template, the quality gates and the value model — the page structure stays identical.
Codebase & standards profile
Define the repositories, languages, architecture patterns, house style and decision records in scope. The personas are the developer, the reviewer, the tech lead and the security engineer.
Change template
One consistent flow for every development agent: understand, plan, implement, test, scan, open a pull request, review, merge and watch the runtime behaviour.
Quality & security gates
Declare the coverage thresholds, static analysis rules, licence policy, dependency policy and branch protections. A gate is never disabled to let a generated change through.
Value model
Capture baseline metrics first, then map the expected benefits: change lead time, review load, test coverage, escaped defects and time spent on boilerplate.
06 — Impact
Key outcomes & business impact
Starting targets for the value case — validate each one against the customer baseline during discovery.
- Change lead time−40%A tested, explained change is waiting when review starts.
- Time on boilerplate−70%Scaffolding, tests and wiring stop consuming the day.
- Review focusDesignReviewers judge the approach, not the formatting.
- Change traceability100%Every change carries its rationale, tests and approver.
Illustrative improvement index
Manual baseline = 100. Illustrative targets, not a commitment — confirm against the customer baseline.
- Lead time to merge: manual baseline 100, AI-assisted target 60
- Time on boilerplate: manual baseline 100, AI-assisted target 30
- Escaped defects: manual baseline 100, AI-assisted target 65
Related & recommended
Derived automatically from our solution knowledge graph.
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Technologies
What powers this solution.
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