<!-- SEO Keywords: AI CAD, AI CAE, AI CAX, Text-to-CAD, Text-to-CAE, Text-to-CAX, Generative CAD, AI Engineering Workbench, MCP CAD, build123d, OpenCASCADE, CalculiX -->

<div align="center">

CAD/CAE Copilot

Most AI CAD tools stop when a picture appears. CAD/CAE Copilot turns an engineering spec into real, editable, verifiable CAD — and keeps every artifact reproducible.

An AI-native CAD/CAE/CAX workbench. An MCP-capable agent writes real

build123d / OpenCASCADE geometry, exports STEP/STL/GLB, names the parts,

exposes stable topology pointers, runs a deterministic critique, and can

continue into CAE — all preserved in one reproducible .aieng package.

You bring your own MCP client (e.g. Claude Code, Codex, Copilot, Cursor),

which carries its own model access. The aieng backend itself needs no API key.

<a href="docs/assets/images/hero.webp">

<img src="docs/assets/images/hero.webp" width="100%" alt="A fully specified industrial motor mounting fixture modeled and inspected with aieng"/>

</a>


What is this? ·

Quick Start ·

CAD Examples ·

Benchmarks ·

Why aieng ·

One-prompt setup ·

MCP Setup ·

Agent Guide

_{English | <a href="README.zh.md">中文</a>}

_{Real STEP/STL/GLB · Editable parameters · Named parts · Stable topology pointers · Deterministic critique · CAD → CAE artifacts · Approval-gated actions}

</div>

At a glance

CAD/CAE Copilot lets an MCP-capable agent turn an engineering specification into

a reproducible CAD/CAE package.

• Input: explicit mechanical specs, dimensions, constraints, and follow-up

edits.

• CAD kernel: real build123d / OpenCASCADE geometry, not a mock renderer.
• Outputs: STEP, STL, GLB, generated source, thumbnails, topology maps, and

.aieng packages.

• Editability: named parts, editable parameters, and stable @face:*

pointers when topology mapping succeeds.

• Verification: deterministic critique, geometry checks, design-rule

assertions, and diff-aware edits.

• CAE path: material, loads, constraints, mesh, solver artifacts, and

evidence are kept beside the CAD model.

• Agent model: bring your own MCP client and model access; the backend

itself does not require an API key.

engineering spec
  -> MCP agent
  -> build123d / OpenCASCADE CAD
  -> named parts + topology pointers
  -> critique / CAE setup / solver evidence
  -> reproducible .aieng package

What is this? — architecture & information flow

In one picture: a natural-language engineering spec becomes optimized, verified

CAD/CAE, driven end-to-end by your own MCP agent and preserved in one

reproducible .aieng package.

<a href="docs/cadcaecopilotarchitectureinformation_flow.png">

<img src="docs/cadcaecopilotarchitectureinformation_flow.png" width="100%" alt="CAD/CAE Copilot system architecture and information flow: user requirement → agent (plan/reason) → MCP tool layer → CAD modeling → CAE setup & simulation → result evaluation → closed-loop optimization, with a human-in-the-loop trust/approval layer and an .aieng engineering package"/>

</a>

**User requirement → agent (plan & reason) → MCP tool layer → CAD modeling &

editing → CAE setup & simulation → result evaluation → closed-loop

optimization. A human-in-the-loop trust & approval** layer gates every

mutation, a credibility tier is stamped on every result, and all artifacts

land in a self-describing .aieng package — so any MCP-capable agent can design,

analyze, optimize, and deliver verified, reproducible CAD/CAE solutions.

Quantitative benchmarking

The project includes a machine-readable analytical FEA benchmark corpus at

`aieng/benchmarks/datasets/analytical_fea`.

It builds runnable .aieng cases, compares solver metrics against documented

closed-form references, and emits an

aieng.benchmark.analytical_fea.scorecard JSON artifact. The harness reports

"agreement within tolerance"; it is not certification or a production-safety

claim.

cd aieng
python -m aieng.benchmarks.analytical_fea --out analytical_fea_scorecard.json

CI runs the analytical benchmark corpus tests so corpus/reference drift and

scorecard regressions fail fast.

Quick start

Three ways in — pick one and you're modeling in minutes.

Before you start: you need your own MCP client (Claude Code, OpenAI

Codex, GitHub Copilot, Cursor, …) with its own model access. The aieng

backend itself needs no API key — your agent connects to it over MCP and

drives the workbench through its own harness.

For a first try, Docker (Option 2) is the most reliable — it pins the

build123d / OpenCASCADE / CalculiX stack so nothing has to compile on your

machine. The local dev install is best once you intend to hack on the code.

Option 1: GitHub Codespaces (fastest, zero install)

Click "Open in GitHub Codespaces" above. The environment sets itself up;

when it finishes loading, run make dev (or python3 scripts/dev.py if make

is unavailable). Then connect an agent and paste the

motor mounting fixture prompt, or the

shorter bracket prompt:

Create a 120 × 80 × 12 mm machined bearing support bracket with a centered
Ø42 mm horizontal bearing bore, four Ø10 mm base mounting holes, and two
mirrored gussets. Preserve the exact dimensions, expose editable parameters,
verify the final geometry, and run the deterministic engineering critique.

Inspect the generated model, named parts, verification results, and stable

@face:* references in the workbench.

Option 2: Docker all-in-one (recommended local package)

Packages the backend, built viewer, MCP HTTP server, build123d / OpenCASCADE

dependencies, and CalculiX into one container.

Quick start — pull the published image (no local build):

docker pull ghcr.io/armpro24-blip/aieng-workbench:latest
docker run --rm -it -p 8000:8000 -p 8765:8765 -v aieng-data:/data ghcr.io/armpro24-blip/aieng-workbench:latest

For an agent-guided first run, paste the

one-prompt setup into your MCP-capable client.

It checks Docker, backend health, MCP endpoint visibility, onboarding calls, and

approval-surface readiness before any optional CAD smoke.

The alpha image is published to GHCR from main after the Docker smoke passes

(latest + an immutable sha-<commit> tag). Alpha-scoped, not

production-certified.

Releases: the current pre-release is

`v0.1.0-alpha.4`.

Distribution is the Git tag plus the GHCR image — **PyPI publication is not

planned**, so the Git form below is the install path, not a stopgap. To run just

the MCP server without cloning (Python 3.11+, pinned to the release tag):

uvx \
  --from "aieng-workbench-mcp[full] @ git+https://github.com/armpro24-blip/cad-cae-copilot.git@v0.1.0-alpha.4#subdirectory=aieng-ui/backend" \
  --with "aieng-format @ git+https://github.com/armpro24-blip/cad-cae-copilot.git@v0.1.0-alpha.4#subdirectory=aieng" \
  aieng-workbench-mcp --approval-mode client --data-dir ~/.aieng-workbench

What a successful first run looks like

After the first successful CAD smoke, you should be able to verify:

• the workbench viewer shows a generated model,
• STEP/STL/GLB or preview artifacts were written,
• generated source and metadata are preserved in the project package,
• named parts and editable parameters are present when the model exposes them,
• stable topology references such as @face:* are available when topology

mapping succeeds,

• deterministic critique or geometry checks report findings honestly,
• and the .aieng package contains reproducible artifacts and provenance.

CAE setup, mesh generation, and solver execution are separate approval-gated

steps. A readiness or preflight report does not mean the solver has run.

Contributor path — build locally from source (Docker Compose or manual,

for developing the image or running an unmerged branch):

docker compose up -d
# or:
docker build -t aieng/workbench:local .
docker run --rm -it -p 8000:8000 -p 8765:8765 -v aieng-data:/data aieng/workbench:local

Open the viewer at http://localhost:8000/app/ and point an MCP-over-HTTP client

at http://localhost:8765/sse. Projects and .aieng packages persist in the

aieng-data volume. The container enables AIENG_MCP_MANAGED_APPROVAL=1 by

default, so approval-gated CAD/CAE tools surface through the workbench UI.

Option 3: Local developer install

Best when you intend to modify the code. Prerequisite: a conda env named

exactly `aieng311` (Python ≥ 3.11) with build123d — the MCP configs

and run scripts assume this name. The build123d / OpenCASCADE (OCP) install

can be slow or fail on some platforms; if it does, prefer the Docker path

above.

conda create -n aieng311 python=3.11 -y
conda activate aieng311
pip install build123d
cd aieng-ui/backend && pip install -e .

Then start both services in one terminal (Ctrl+C stops both):

make dev                  # macOS / Linux / WSL
.\dev.ps1                 # Windows PowerShell
python scripts/dev.py     # cross-platform fallback

Backend → FastAPI on http://127.0.0.1:8000; frontend → Vite on

http://localhost:5173. Custom ports: BACKEND_PORT=8080 FRONTEND_PORT=3000 make dev.

<details>

<summary>Start services individually / run tests</summary>

make backend     # or: cd aieng-ui/backend && uvicorn app.main:app --host 127.0.0.1 --port 8000 --reload
make frontend    # or: cd aieng-ui/frontend && npm install && npm run dev
cd aieng-ui/backend && python -m pytest    # backend test suite

</details>

From specification to verified CAD

The hero model above was built from an explicit industrial fixture

specification — fixed dimensions, named parts, exact hole and slot locations,

required symmetry, and no permission to invent extra geometry. The agent

executes and verifies the spec; it does not silently fill in the engineering.

<details>

<summary><strong>Copy the motor mounting fixture prompt</strong></summary>

Create a fully specified industrial motor mounting fixture using millimeters.

Coordinate system:
- X is the fixture width, Y is the fixture depth, and Z is vertical.
- Center the complete fixture on X=0.
- Place the bottom face of the base plate at Z=0.

Base plate:
- Create a 180 × 140 × 14 mm base plate.
- Add four Ø11 mm vertical through-holes at X=±70 mm and Y=±50 mm.
- Add an Ø18 mm, 5 mm deep counterbore to the top of every mounting hole.
- Add a 3 mm fillet to the four outside vertical corners.

Motor support:
- Add a centered rear vertical support plate, 130 mm wide, 14 mm thick,
  and 120 mm tall above the base.
- Add a Ø72 mm horizontal locating bore through the plate along Y.
- Position its center at X=0 and Z=78 mm.
- Add four Ø8.5 mm horizontal mounting holes on a Ø100 mm bolt circle.

Reinforcement and rails:
- Add two mirrored 12 mm thick triangular gussets extending 45 mm forward
  and rising 65 mm above the base.
- Add two separate 110 × 12 × 8 mm guide rails centered at X=±38 mm, Y=-12 mm.
- Add one centered 70 × 6 mm longitudinal slot to each rail.

Modeling requirements:
- Create named parts "fixture_body", "guide_rail_left", and "guide_rail_right".
- Color the fixture body dark blue-gray and both guide rails orange.
- Declare all major dimensions as editable UPPER_SNAKE_CASE constants.
- Preserve exact left/right symmetry.
- Verify overall dimensions, named parts, and stable topology pointers.
- Run the deterministic engineering critique after modeling.
- Do not add a motor, fasteners, logos, decorative features, or unspecified geometry.

</details>

Industrial CAD examples

Each example starts from explicit dimensions, feature locations, and modeling

constraints — the agent executes and verifies the specification rather than

inventing the requirements.

<table>

<tr>

<td width="33%" align="center">

<a href="docs/assets/images/example1.webp">

<img src="docs/assets/images/example1.webp" width="100%" alt="aieng generating and verifying a fully specified machined bearing support bracket"/>

</a>

<br>

<strong>Machined Bearing Bracket</strong>

<br>

_{Datums, bore, mounting pattern, gussets, fillets, and critique}

</td>

<td width="33%" align="center">

<a href="docs/assets/images/example2.webp">

<img src="docs/assets/images/example2.webp" width="100%" alt="aieng generating and auditing a fully specified six-port pneumatic manifold"/>

</a>

<br>

<strong>Six-Port Pneumatic Manifold</strong>

<br>

_{Exact envelope, port spacing, counterbores, and editable dimensions}

</td>

<td width="33%" align="center">

<a href="docs/assets/images/example3.webp">

<img src="docs/assets/images/example3.webp" width="100%" alt="aieng generating a named-part industrial junction-box assembly with stable face pointers"/>

</a>

<br>

<strong>Industrial Junction Box</strong>

<br>

_{Named assembly parts, exported artifacts, and stable face pointers}

</td>

</tr>

</table>

<details>

<summary><strong>What these examples verify</strong></summary>

• Machined bearing support bracket — one manufacturable solid with a

specified base envelope, horizontal bearing bore, symmetric mounting pattern,

mirrored gussets, fillets, and chamfers. The workbench caught and corrected

construction errors, then verified the final datums, topology, editable

parameters, and engineering critique.

• Six-port pneumatic manifold — a specification-driven manifold with an

exact 160 × 50 × 40 mm envelope, six equally spaced outlets, axial inlet

ports, counterbored mounting holes, edge fillets, opening chamfers, and

editable dimensions.

• Industrial junction-box assembly — a two-part enclosure assembly with

named base and lid solids, internal mounting bosses, cable-gland openings,

separated lid placement, generated STEP/STL/GLB artifacts, and a selectable

stable face pointer for precise follow-up work.

</details>

Why aieng — beyond text-to-CAD

Most AI CAD and text-to-CAD demos stop when a model appears. aieng treats

geometry generation as one step in a reviewable engineering workflow built

around self-describing .aieng packages: editable parameters, stable topology,

provenance, and a full CAD → CAE path all survive after the picture.

Think of .aieng as a portable engineering evidence passport. It is not a CAD

kernel, solver, PLM database, or certification stamp; it is the shared package

where humans and MCP-capable agents can inspect what geometry exists, what CAE

setup is present, what result evidence was written, which claims cite evidence,

and what provenance explains how the package got there. A package can be handed

to another engineer or agent without losing the CAD/CAE context that matters for

review; see the package handoff runbook for the

safe send/receive flow.

| Capability | Typical text-to-CAD demo | aieng |

|------------|:------------------------:|:-----:|

| Generate real CAD exports (STEP/STL/GLB) | Yes | Yes |

| Execute explicit dimensions and datums | Partial | Yes |

| Preserve editable source and parameters | Partial | Yes |

| Name parts and expose stable topology references | Rarely | Yes |

| Verify geometry and run deterministic critique | Rarely | Yes |

| Diff every edit (topology drift + manufacturability) | No | Yes |

| Design-rule assertions that fail the build (require) | No | Yes |

| Stamp a credibility tier on every result (V&V-40) | No | Yes |

| Preserve artifacts and provenance in one package | Rarely | Yes |

| Continue from CAD into CAE workflows | Rarely | Yes |

| Require approval for gated engineering actions | Rarely | Yes |

| Standard parts library | No | Yes |

| Extended material database (51 materials) | No | Yes |

| BOM generation | No | Yes |

What that buys you:

• Real, exportable CAD — agent-written build123d / OpenCASCADE geometry

produces STEP, STL, GLB, topology maps, feature graphs, and 4-view thumbnails.

Not a stub.

• Specification-driven execution — agents follow explicit dimensions,

datums, feature positions, symmetry, and manufacturing constraints instead of

freely inventing a design.

• Inspect and correct — geometry reports, deterministic critiques, named

parts, and stable @face:* pointers support precise verification and

follow-up edits.

• Reproducible engineering packages — .aieng packages preserve geometry,

generated source, analysis state, artifacts, metadata, and provenance, so a

result is reviewable instead of opaque.

• Agent-independent MCP tools — Claude Code, GitHub Copilot, OpenAI Codex,

Cursor, and other MCP-capable agents drive the same backend.

• CAD → CAE path — material, boundary conditions, mesh, solver runs, result

mappings, and evidence live beside the CAD model.

Who it's for: AI agent / MCP developers wanting engineering tools beyond

text and code; mechanical engineers exploring AI-assisted CAD/CAE with real

geometry; and makers, researchers, and open-source contributors interested in

CAD, CAE, MCP, VS Code extensions, or build123d / OpenCASCADE.

Proof, not just screenshots

Each successful CAD run can preserve inspectable artifacts, not just a rendered

image:

• generated build123d source,
• STEP/STL/GLB exports,
• named parts and editable parameters,
• topology maps and stable @face:* pointers when mapping succeeds,
• preview thumbnails,
• critique and geometry-check results,
• provenance and package metadata inside the .aieng evidence package.

That means another engineer or MCP-capable agent can inspect, modify, rerun, and

review the result instead of trusting a static render.

Trust layer — verified & explainable by construction

The crowded field is generation; the empty field is trust. Every

AI-suggested change here is checked and explainable, not just rendered:

• Diff on every edit — each parametric edit / part swap / append returns a

regression_diff (did unintended parts move?) and a critique_diff (did the

edit worsen min-wall / hole-spacing / floating-part / symmetry rules?),

re-surfaced as a first-class before→after diff in the viewer.

• Design-rule assertions — author require(WALL >= 3, "wall below 3mm") (or a

bare assert) in build123d code; a violation fails the build deterministically

with a structured design_rule_violation, so constraints are verified by

construction instead of hoped for.

• V&V-40 credibility tiering — every result-bearing output carries one ordered

tier (critique_finding < surrogate_prediction < proxy_assembly_result <

executed_solver_result); a claim is never presented as more credible than its

evidence, and production_ready is never assumed. Running the solver is not

enough: a completed run on a mesh whose measured accuracy band is unreliable

is stamped unreliable_mesh, not executed_solver_result.

• Mesh accuracy by measurement, not hope — FE meshes default to quadratic

tetrahedra (C3D10; linear tets under-predicted bending stress ~2× in

calibration), and every mesh carries a measured accuracy verdict

(reliable / marginal / unreliable) plus an optional GCI/Richardson

convergence study (cae.mesh_convergence).

• Surrogate error-band discipline — a surrogate-predicted number is never shown

without its uncertainty band + a leave-one-out validation error.

• Consistency-gated agency — low cross-sample agreement on an LLM-judged step

routes to ask the user instead of acting on a guess.

• NAFEMS-style V&V suite — analytically-backed linear-static benchmarks guard

the solver path in CI (results are verified against reference within tolerance,

never "certified").

How it works

1. Provide a mechanical specification with explicit dimensions and constraints.
2. An MCP-capable agent uses aieng tools to create real CAD geometry.
3. aieng exports the model and records named parts, topology, editable

parameters, source, and provenance.

1. Inspect the result visually and numerically, then reference exact parts,

features, or faces (@face:*) for follow-up changes.

1. Query the extended material database (51 engineering materials) to assign

accurate mechanical and thermal properties to parts.

1. Insert standard parts — fasteners, bearings, shafts, structural profiles,

and standard holes — directly from the library into the model.

1. Generate a Bill of Materials (BOM) from the assembled parts for review

and procurement.

1. Continue into CAE setup and solver workflows once the required engineering

inputs are available.

Materials & standard parts workflow:

aieng.list_materials { category: "Aluminum Alloy" }
aieng.get_material_details { material_name: "Al6061-T6" }
aieng.compare_materials { material_names: ["Al6061-T6", "Steel-316L"] }

aieng.list_standard_parts { category: "fastener" }
aieng.get_standard_part_specs { part_type: "hex_bolt", preset_name: "M8" }
aieng.insert_standard_part { part_type: "hex_bolt", preset_name: "M8", position: [0,0,0] }

aieng.generate_bom { format: "markdown" }

The workbench UI and the

`aieng-vscode-extension` provide visual inspection for

live backend projects and .aieng packages.

Visual inspection in VS Code

The VS Code extension is the most visual way to experience aieng — a front-end

for the .aieng package format, MCP tools, and CAD/CAE backend that brings the

AI-CAD design loop directly into your editor. It can:

• open a local .aieng package as a read-only custom editor,
• connect to a live backend project preview,
• visualize generated GLB/STL CAD outputs,
• and copy stable @face:id pointers back into your chat with an agent.

The extension is one layer of the system, not the whole thing — the core is the

package format and engineering backend that let agents and humans share

reproducible CAD/CAE project state. Setup and development notes live in

`aieng-vscode-extension/README.md`. For the

current editor-first path, see

`docs/mcp-first-vscode-workflow.md`.

Drive aieng from AI agents via MCP

The backend exposes its tool registry as an MCP server (aieng-workbench),

so agents drive the workbench through their own harnesses — no API key needed on

our side. Connection configs are committed and load automatically for a fresh

clone, assuming the aieng311 env exists:

| Agent | Config file |

|-------|-------------|

| Claude Code | .mcp.json |

| VS Code / GitHub Copilot | .vscode/mcp.json |

| Cursor | .cursor/mcp.json |

| Cline | its own cline_mcp_settings.json (copy the block from MCP_SETUP) |

| OpenAI Codex | add [mcp_servers.*] to ~/.codex/config.toml (see MCP_SETUP) |

Approval works three ways (--approval-mode): your client's own prompt

(client), the workbench viewer / VS Code extension card (managed), or

headless MCP elicitation (elicit) for agents with no UI — with a fail-safe

deny when no surface can answer. Run aieng-workbench-mcp --doctor to check that

your MCP config, backend, and tool set are wired before you start. Full matrix

of tested-vs-documented clients:

MCP client compatibility.

First three calls every session:

1. aieng.agent_readme                  -> compact operational onboarding
2. aieng.list_projects                 -> discover project IDs
3. aieng.agent_context { project_id }  -> geometry state, pointers, next steps

Use aieng.guide { topic } for task-specific detail, or

aieng.agent_readme { detail: "full" } when the complete canonical

`AGENTS.md` is genuinely required.

The sustainable modeling loop:

cad.get_source            -> see accumulated source, named parts, has_base
cad.execute_build123d     -> build/extend geometry (mode=replace|append)
                            - set .label on parts -> semantic names you can reference
                            - mode=append builds onto `previous_result`
                            - returns a thumbnail + named_parts / parts_added
(inspect the result, repeat)

Full tool details, pointer syntax, and approval-gated operations live in

AGENTS.md; MCP wiring by client in

aieng-ui/backend/MCP_SETUP.md.

Showcase demos

Canonical backend demos, each runnable as a single test:

1. CAD Generation → Structural FEA → Topology Optimization

Runs the CAD → FEA → topology optimization loop and writes back editable

optimized geometry.

<img src="docs/assets/showcase/geometrycaeflow.svg" width="800" alt="CAD Generation to Structural FEA to Topology Optimization"/>

pytest aieng/tests/test_topology_optimization.py -q

Key artifacts: analysis/topology_optimization.json, geometry/shape_ir.json

Boundary: 2D plane-stress; 3D SIMP is experimental/reference only.

Details →

2. Rebuild CAD from Mesh → Export STEP

Reconstructs analytic CAD from a mesh and exports STEP when the shell validates.

<img src="docs/assets/showcase/meshtocad_flow.svg" width="880" alt="Mesh to Region Segmentation to Surface Fitting to Face Generation to Sew Shell to Export STEP"/>

pytest aieng/tests/test_mesh_brep_solidification.py -q

Key artifacts: geometry/reconstructed.step (when valid),

graph/mesh_brep_stitching_plan.json

Boundary: Mesh-derived/lossy; plane/cylinder dominant; freeform/NURBS

future work; partial shells do not produce STEP.

Details →

3. Assembly Model → Selected-Part Optimization

Builds a proxy assembly analysis model and optimizes one selected design part.

<img src="docs/assets/showcase/assemblyoptimizationflow.svg" width="880" alt="Assembly Model to Resolve Interfaces to Simplified Analysis to Topopt Problem to Optimized Part"/>

pytest aieng-ui/backend/tests/test_assembly_topopt_demo.py -q

Key artifacts: analysis/assembly_topology_optimization.json,

parts/bracket/geometry/optimized_shape_ir.json

Boundary: Proxy connections only; no real contact/friction/bolt preload;

one design part only; not production-certified.

Details →

4. Design Study: Adjustable Dimensions → Compare → Adopt

Validates, executes, compares, and optionally adopts parameterized design

candidates without overwriting the baseline.

<img src="docs/assets/showcase/designstudyflow.svg" width="960" alt="Setup Problem to Propose Candidate to Safety Checks to Build Design Copy to Compare Options to Adopt Best"/>

pytest aieng-ui/backend/tests/test_design_study_demo.py -q

Key artifacts: analysis/design_study_candidate_ranking.json,

analysis/design_study_acceptance.json,

accepted/candidate_good/geometry/shape_ir.json

Boundary: Static metrics in demo; no autonomous optimization; no baseline

overwrite; ranking is advisory.

Details →

5. Real CAD->CAE Value Demo

Runs one canonical single-solid cantilever through CAD creation, face-pointer

selection, approval-gated CalculiX execution, a GCI/Richardson mesh-convergence

study, FRD-derived viewer fields, and a shareable engineering report.

python aieng-ui/backend/scripts/value_demo_packet.py --format markdown

Key artifacts: simulation/runs/value_demo_run_001/outputs/result.frd,

results/computed_metrics.json, analysis/mesh_convergence_report.json,

engineering report HTML.

Boundary: Requires real Gmsh/CalculiX; linear static — discretization

uncertainty is bounded by the GCI study (measured 1.31% on the reference

cantilever), not assumed away; no synthetic fallback counts as a successful demo.

Runbook ->

Current limitations

Honesty boundaries — outputs are review material, not production sign-off:

• Not production-certified CAD/CAE. Outputs still require human engineering judgment.
• No fatigue or durability analysis. Supported: linear static, modal, linear buckling, steady-state thermal, one-way thermal-stress. A cyclic load, a cycle count and a required life have nowhere to be entered, and the material library carries no endurance or S-N data. A cold-start agent trial found this boundary stated in exactly one document, so it is stated here too.
• Assembly contact and bolt preload are proxy-only; real nonlinear contact is future work.
• 3D SIMP is experimental/reference, not production-certified.
• Mesh-to-CAD works best for plane/cylinder-dominant geometry; broader freeform

and NURBS fitting is future work.

• Design study is agent-guided explicit execution, not autonomous global optimization.

Repository layout

| Path | Status | What it is |

|------|--------|------------|

| `aieng-ui/` | Active | FastAPI backend, React workbench, and MCP server — the active CAD/CAE engine (build123d) |

| aieng/ | Core library | .aieng semantic package format engine, schemas, validation, CLI, Shape IR, and evidence model |

| aieng-vscode-extension/ | Active | VS Code visualization front-end for .aieng packages and live project previews |

| aieng-agent-skills/ | Active | SKILL.md contracts teaching agents how to use the ecosystem |

| legacy/aieng-freecad-mcp/ | Legacy | Old FreeCAD execution adapter — not used by the active path |

| archive/CAD-Agent-main/ | Archived | Historical and experimental auxiliary CAD-agent material |

Documentation

| Doc | Purpose |

|-----|---------|

| AGENTS.md | Canonical agent guide — tools, workflows, and conventions |

| docs/prompt-guide.md | Plain sentences to type at your connected agent — the user-facing phrasing guide |

| aieng-ui/backend/MCP_SETUP.md | Per-agent MCP wiring for Claude Code, Copilot, Cursor, and Codex |

| docs/cad_execution_boundary.md | Local-first execution boundary, privacy posture, approval gates, and sandbox limits |

| docs/parametric-edit-governance.md | Reviewable, reversible CAD parameter edit workflow and honesty boundaries |

| docs/cae-credibility-ladder.md | CAE evidence tiers from artifact presence to benchmark calibration and review support |

| docs/canonical_engineering_scenarios.md | Canonical engineering scenarios - reproducible packs, verification commands, artifacts, and honesty boundaries |

| aieng-vscode-extension/README.md | VS Code extension usage and development notes |

| aieng/docs/showcase_gallery.md | Showcase gallery — demo talking points, visual guidance, and honesty boundaries |

| aieng/docs/demo_catalog.md | Backend demo catalog — run commands, expected artifacts, and maturity levels |

| aieng/docs/backend_capability_matrix.md | Capability status snapshot |

| aieng/docs/roadmap.md | Phase-by-phase development roadmap |

| CLAUDE.md | Claude Code entry pointer |

| .github/copilot-instructions.md | GitHub Copilot entry pointer |

Contributing

Contributions are welcome across the package format, backend workflows, MCP

tooling, and VS Code front-end. Work that improves reproducibility, visual

inspection, engineering honesty boundaries, or agent usability is especially in

scope.

Notes

• Public repo. No secrets are committed; runtime data (data/projects/),

virtual environments, node_modules, and embedded conda envs are gitignored.

• If your CAD env is not named aieng311, edit the -n aieng311 argument in

the MCP configs or point command directly at your interpreter — see

aieng-ui/backend/MCP_SETUP.md.

• A running backend at http://127.0.0.1:8000 enables live UI updates when an

agent drives a build; if it is down, the MCP server falls back to in-process

execution.