Private runtime
Agents run where your IP is allowed to live
Deploy agent sessions inside approved infrastructure instead of pushing proprietary context through unmanaged public tools.
Private AI agents for serious engineering work.
Agent platforms belong inside the engineering boundary.
ControlStackAI gives engineering teams a private runtime for long-running agents that operate MATLAB, Simulink, requirements, source control, and verification workflows. Not another chat window. Reviewable engineering output from agents that understand your toolchain and constraints.
Built for
Controls and dynamics: model analysis, linearization, trim, simulation review, test evidence.
Regulated teams: private deployments, audit trails, human checkpoints, artifact history.
Engineering context: agents grounded in repos, requirements, models, docs, tickets, and team conventions.
Platform layer
A private control plane for agentic engineering.
Coordinate long-running agent work on your infrastructure, with durable state, scoped tool permissions, checkpoints, traces, and review gates that match how engineering organizations actually operate.
Context layer
Ground work in the artifacts engineers trust
Connect repos, requirements, models, design notes, test evidence, issue history, and local operating procedures.
Inference control
Route work across approved model backends
Prepare for mixed commercial, private, and local inference while preserving policy, cost visibility, and traceability.
Engineering workflows
Designed for models, evidence, and toolchains.
The first focus is engineering work where correctness, provenance, and reviewability matter: controls, avionics, robotics, industrial automation, flight dynamics, and verification-heavy teams.
MATLAB and Simulink
Inspect models, run scripts, summarize results, and generate reviewable technical artifacts.
Controls workflows
Support trim, linearization, controller iteration, simulation evidence, and technical writeups.
Requirements and V&V
Link agent output back to source requirements, test plans, tickets, and acceptance criteria.
Team memory
Preserve decisions, constraints, interfaces, and procedures so agents improve with context.
Engineering tools were built for human operators. Agentic AI needs a platform layer that can operate those tools, preserve reviewability, and respect the infrastructure boundaries serious teams live inside.
Read the manifestoOperating model
Agents that leave an audit trail, not a mystery.
01
Plan against real context
Agents read the repo, model structure, requirements, constraints, and prior decisions before touching work.
02
Execute through approved tools
Runtime permissions keep each agent inside allowed commands, files, systems, and approval gates.
03
Return reviewable artifacts
Engineers get diffs, reports, test output, traces, and provenance instead of opaque chat transcripts.
Early access
Design partners before public launch.
We are opening a small number of design-partner conversations with engineering teams that need agentic work to stay inside their boundary.