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CASE STUDY / AGENTIC AI

SYJ AI

An open-source autonomous software-engineering workflow that routes work through planning, research, design, coding, review, verification, optimization and documentation using local models.

OPEN SOURCE · LOCAL-FIRSTPython · Ollama · Agents · Sandboxing

Why it matters

Shows how the runtime layer can become an agent layer: explicit stages, local inference and controlled execution.

Architecture

SYSTEM / VERIFIED ARCHITECTURE LENS
OrchestrationPlan → research → design → code → review → verify
Model layerLocal Ollama inference
ExecutionSandboxed workspace + explicit shell controls
OutputCode, review, verification and documentation
Design goalKeep execution local and inspectable
Public repository This page intentionally avoids invented performance metrics, customer counts or production claims.

Evidence / implementation boundary

01Current evidence

The repository describes a local-first software-engineering agent workflow spanning planning, research, design, coding, review, verification, optimization and documentation.

02Tool boundary

Filesystem and shell operations are designed around sandboxing and permission boundaries rather than unrestricted execution.

03Experimental

Local model routing, mobile-oriented operation and optional remote fallback are areas of ongoing work.

04Limitations

Agent-generated changes require testing and human review. The project is not presented as an unrestricted autonomous replacement for engineering judgment.

Roadmap

Strengthen permission controls, failure handling, reproducible tests and model-routing observability.

Engineering focus

Problem framing → architecture → implementation → validation → documentation → the next useful version. The portfolio is designed to show decisions and evidence, not just screenshots.