Approved repo, tools, runtime, network and budget are applied before the session starts.
AI agent operations, governed
Run AI agents in workspaces your enterprise can control.
Stacklane gives every agent session a managed cloud workspace, policy boundary, spend record, and reusable team playbook.
Stacklane Control
Workspace
Engineering agents
Policy active
Budget guard
Repository scope
Sensitive action review
Platform
One governed layer for agents, models, code, and cloud runtime.
Stacklane is the operating surface between individual AI tools and enterprise control requirements.
Workspace layer
Launch consistent AI workspaces
Each user starts from an approved environment with the right repository, CLI tools, runtime image, network boundary, and workspace policy.
Control what agents can reach
Route access by role, project, model provider, repository, network destination, and task risk.
Turn good sessions into reusable assets
Prompts, skills, delivery templates, and operating procedures become company assets instead of private habits.
Governance
Make AI usage visible before it becomes unmanaged infrastructure.
The same platform that starts the work also records cost, policy, risk, and operational evidence.
Budget visibility
Understand usage by person, project, department, model, and agent workflow.
Sensitive action control
Review high-risk file, command, network, and model actions before they spread.
Audit-ready activity history
Keep session activity, tool calls, file access, and model requests traceable.
Project-level operating model
Manage members, templates, policies, and reusable outputs around the work itself.
Workflow
A controlled path for every agent session.
Users still move quickly, but every step runs through identity, workspace policy, budget routing, and audit capture.
The user chooses an approved project space and agent template.
Stacklane applies repositories, tools, runtime, budget, and access scope.
Model and tool usage is routed through policy instead of unmanaged credentials.
Outputs, logs, cost, and reusable practices stay with the organization.
Teams
Designed for the groups that make AI adoption real.
Engineering leaders
Standardize code generation, review, refactoring, testing, and documentation workspaces.
AI platform teams
Manage providers, API keys, templates, usage routing, and internal AI operating standards.
Security and finance
See the cost, access, risk, and evidence trail behind each agent session.
Pilot
Plan a governed AI workspace pilot.
We can map Stacklane to your team structure, model providers, repositories, access system, and compliance requirements.