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The MDO Agent Deck is an agentic AI framework for multidisciplinary design optimization (MDO). It exposes engineering workflows through an MCP server and orchestrates domain agents such as airfoil, wing, and aircraft. The MDO Agent Deck framework is not open source. Instead, it is distributed on PyPI as compiled shared library package for evaluation purpose. Check LICENSE for more details.

Trustworthy Agentic AI Framework for MDO

The framework is designed to be trustworthy for engineering using the following guardrails:

  • Narrowly scoped, domain-specific agents.
  • Strictly constrained input parameters per skill.
  • A robust review-and-correction loop at each phase.
  • Fully transparent and auditable execution workflows.

How the Framework Works

For each user request/prompt, the agent execution follows a strict sequence:

  1. The MCP server identifies the best domain agent and skill for the request.
  2. It semetically parse the input information from user-prompt to the skill’s input values.
  3. It creates a new isolated case folder for that run.
  4. It runs the prepare phase and checks the review result.
  5. It runs the run phase and checks the review result.
  6. It runs an analyze phase and checks the final review result.

This review gate after every phase is a key guardrail. If a review fails, the workflow stops and requests correction before moving forward.

For example, if a user asks:

Generate a CFD mesh for NACA0012 at Mach 0.05, Reynolds 20,000, y+ target 50.

The execution will look like this:

  1. The MCP server identifies the airfoil agent and the generate-cfd-mesh skill for this request.
  2. It semantically parses the user prompt into skill inputs (airfoil_profile=naca0012, mach_number=0.05, reynolds_number=20000, y_plus_target=50).
  3. It creates a new isolated case folder, e.g., airfoil_mesh_naca0012_ma005_20k_y50_0000
  4. In prepare, it copies geometry and mesh configuration files into that folder, then checks the review result.
  5. In run, it create a bash script that contains the predefined mesh generation commands, and run the bash script.
  6. In analyze, it computes mesh quality metrics, verify if the mesh quality passes thresholds, and also generate mesh plots.

If any review fails (for example invalid input ranges or mesh quality below threshold), the workflow stops at that phase and reports what must be fixed.

Fig. 1. Schematic of the agentic AI workflow

Transparency and Auditability

The framework records workflow and runtime context in the working directory:

  • agent_workflow.json: auditable case-by-case phase history (set_skill_inputs, prepare, review_prepare, run, review_run, analyze, review_analyze) with timestamps and status.
  • agent_state.json: persisted bindings (inputs and case directories) so sessions can resume after MCP server restarts.

This design supports traceability, debugging, and reproducibility for engineering studies.

Deployment Modes

The MCP server supports three run modes:

  • Docker: default containerized workflow.
  • Native: direct execution on local/HPC environment.
  • HPC: submit jobs to the cluster queue and run them when compute resources are available.