Build an Agentic Proof-of-Concept

### 2. Build an Agentic Proof-of-Concept
- Leverage the scaffolding and lessons from the **LLM-Augmented Fluent PoC**.
- **Demonstrate autonomy:** The agent should plan and execute multi-step actions rather than respond to isolated prompts.
- **Initial scope:** Solver setup and execution.
- **Later PoCs:** Extend to other stages (e.g. meshing) using specialized agents coordinated by a master agent for end-to-end workflows.
- **Success criterion:** Determine whether the PyFluent scaffolding adequately supports robust agentic workflows.

Start by reviewing the LLM-Augmented Fluent PoC scaffolding and the solver setup and execution flow. Define a multi-step autonomous workflow, then assess whether the existing PyFluent scaffolding supports its execution. Done means determining and documenting whether it enables robust agentic workflows.

Written by the indexing model from the issue text.