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. run prompt-aware and agent-aware engineering workflows,
3. observe, validate, and audit what happened,
4. scale those patterns toward enterprise controls.

• Autonomous execution: gsd-orchestrator
• Prompt governance: Promptimprover
• Multi-agent workbench: autogen
• Enterprise architecture depth: cloud-security-service-model

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
• 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 and hybrid architecture thinking beyond application code alone

If you are evaluating this portfolio quickly:

1. Read gsd-orchestrator for autonomous execution design.
2. Read Promptimprover for prompt governance and MCP thinking.
3. Read autogen for operator-facing multi-agent runtime design.
4. Read cloud-security-service-model for enterprise architecture depth.

Built by @OgeonX-Ai.