AI-native engineering portfolio focused on autonomous workflows, prompt governance, multi-agent execution, and enterprise operating models.

This organization is structured as a small platform, not a random set of demos. The core story is:

1. install a governed workstation,
2. exchange work through versioned lifecycle contracts,
3. run prompt-aware and agent-aware engineering workflows,
4. deploy through a managed-identity Azure platform,
5. evaluate, observe, validate, and audit what happened.

• End-to-end reference product: cas-reference-product
• Workstation bootstrap: cas-workstation
• Lifecycle contracts: cas-contracts
• Azure platform foundation: cas-platform
• Evaluation evidence: cas-evals
• Autonomous execution: gsd-orchestrator
• Prompt governance: Promptimprover
• Multi-agent workbench: autogen
• Enterprise architecture depth: cloud-security-service-model

Production-oriented reference workload that connects the CAS platform story.

• Demonstrates Foundry Next Gen agent integration through a bounded application interface
• Uses managed identity, canonical lifecycle events, health checks, and OpenTelemetry boundaries
• Provides the workload contract consumed by cas-platform and evaluated by cas-evals

Authoritative, versioned lifecycle contracts for the complete CAS workflow.

• Standardizes prompts, policy decisions, work requests, run events, artifacts, verification, and evaluation
• Requires correlation identifiers and W3C trace context
• Provides executable schemas and compatibility rules

Secure Azure deployment and observability foundation.

• Uses Bicep, isolated environments, and system-assigned managed identity
• Provides Container Apps hosting, Application Insights, budgets, and safe non-deploying validation
• Keeps public ingress disabled by default

Reproducible evaluation and benchmark evidence.

• Runs deterministic golden-task and adversarial-prompt suites
• Measures quality, safety, cost, and latency independently
• Produces machine-readable evidence suitable for regression gates

Windows-first AI-native developer workstation bootstrap.

• Establishes a repeatable engineering baseline
• Validates installed tools and configuration
• Reduces setup drift before autonomous workflows run

C#/.NET 10 autonomous issue-to-PR engine.

• Reads GitHub issues
• Plans and edits through a state machine
• Uses MCP tooling for GitHub operations
• Preserves checkpointed workflow state for retry and recovery

TypeScript MCP-first prompt governance layer.

• Refines prompts before execution
• Injects repo-aware context and reusable rules
• Builds traceability between prompt intent and engineering output

Python local-first multi-agent engineering workbench.

• Coordinates manager-led agent workflows
• Supports provider routing and fallback
• Keeps operator approvals and run artifacts visible

Enterprise cloud security operating model for Azure and hybrid environments.

• Service architecture and governance
• Controls-as-code posture
• Auditability, metrics, and runbooks

Control plane for org-level CI repair automation.

Worker/runtime pattern for queued repair execution on self-hosted runners.

Bounded demo target for the full failure-to-fix loop.

• C#/.NET, TypeScript, Python, PowerShell, and Bicep across one coherent platform story
• Versioned cross-repository contracts and reproducible evaluation evidence
• MCP integration as infrastructure, not just local tooling
• Multi-agent and autonomous workflow design with operational guardrails
• Enterprise-oriented concerns: auditability, resilience, boundaries, rollout, and documentation
• Azure managed identity, infrastructure-as-code, observability, and hybrid architecture

If you are evaluating this portfolio quickly:

1. Read cas-reference-product for the end-to-end application proof.
2. Read cas-workstation for the reproducible developer baseline.
3. Read cas-contracts for the shared lifecycle and traceability model.
4. Read cas-platform for secure Azure hosting and observability.
5. Read cas-evals for measurable quality and safety evidence.
6. Read gsd-orchestrator for autonomous execution design.
7. Read Promptimprover for prompt governance and MCP thinking.
8. Read autogen for operator-facing multi-agent runtime design.
9. Read cloud-security-service-model for enterprise architecture depth.

Shared contribution, security, support, governance, intake, dependency, and release policies are maintained in the organization .github repository. Repository-specific standards may be stricter.

Built by @OgeonX-Ai.