Build reliable agent-orchestrated engineering simulation workflows with AG2/AutoGen, validation gates, auditable artifacts, failure recovery, and a practical heat-diffusion example.
Key Features
- Build planner-reviewer-controller-executor workflows with AG2/AutoGen
- Apply validation gates, audit trails, and fail-fast recovery to simulations
- Follow a runnable heat-diffusion example from request to traceable report
Book Description
Engineering simulation workflows often lose more time to coordination than computation. Inputs are gathered across tools, assumptions drift between handoffs, and decisions become difficult to reconstruct. This practical FAST guide shows you how to add an agent-orchestration layer around trusted simulation tools without replacing the solver itself. Using a single two-dimensional heat-diffusion example, you will build a planner-reviewer-controller-executor workflow with AG2/AutoGen. You will define input contracts, create auditable artifacts such as simulation_request.json and plan.json, enforce validation gates before execution, run the simulation, validate results, and turn them into a decision-facing report. You will also work through failure modes, diagnostics, recovery paths, auditability, and orchestration variants. By the end of the book, you will understand how to make simulation workflows more predictable, traceable, and resilient, and how to extend the same pattern to parameter studies, CFD, FEA, cloud, and HPC environments.
What you will learn
- Map engineering simulations into explicit orchestration stages
- Define planner, reviewer, controller, and executor roles
- Implement an AG2/AutoGen workflow around a trusted simulation
- Validate requests, plans, tool calls, and simulation outputs
- Diagnose failures and apply repair, re-planning, or escalation
- Extend the pattern to CFD, FEA, parameter sweeps, cloud, and HPC
Who this book is for
This book is for ML/AI engineers, simulation engineers, and technical workflow owners who want to coordinate multi-step engineering simulations with agentic AI. Familiarity with Python and basic simulation workflows will help you follow the runnable examples. No prior AG2/AutoGen experience is required.
