Sources
CRM, AWS / CloudWatch, GitHub, databases, email, and internal systems.
The platform for autonomous agent systems
Build systems of autonomous agents.
Design agents with tools, knowledge, and permissions. Connect them into operational workflows. Run them in production and continuously improve them through learning loops.
Source · Alert receivedAWS / CloudWatchMonitoring → incident trigger
Agent · InvestigateIncident AgentDiagnose with runbooks + history
SkillNode ManagementInspect · drain · restart · restore
Agent · ResolveDeveloper AgentPrepare a fix · request deploy approval
Agent · VerifyVerifier AgentRecovered → close / unresolved → engineer
Outer learning loopEvaluate outcomes → improve agents, skills & routing → next execution
Three stages. One system.
From agent design to continuous improvement.
OpenStation is a platform for designing, running, and improving systems of autonomous agents.
Stage 01
Build Agents
Define what each agent knows, what it can do, and what it is allowed to do.
- Roles and prompts
- Tools and skills
- Internal knowledge and memory
- Permissions and approval policies
- Models and runtimes
- Environment and secrets
Example configuration
Incident Agent
- Role
- Investigate alerts and restore service.
- Knowledge & memory
- Architecture · runbooks · incident history
- Tools & skills
- AWS · Node Management · GitHub
- Permissions
- Read infrastructure · restart services
Deploy → human approval required - Model & runtime
- Choose the model and agent runtime for the role.
- Environment & secrets
- Workspace-scoped credentials and execution environment.
Stage 02
Build the System
Connect agents to each other and to the systems around them. Design the complete operational workflow on the system canvas.
Use handlers to route events, automations to coordinate work, and triggers to start runs. Add human approvals and escalation wherever judgment is needed.
Agent connections
Incident → Developer → Verifier. Shared context, clear roles, and explicit handoffs.
Events & automations
Alert received, ticket created, pull request opened, or a scheduled task.
People & approvals
Review a deployment, approve an action, or take over an unresolved incident.
Stage 03
Run & Improve
Run the system in production. Agents respond to events, use tools, collaborate, and bring people in when needed.
Capture decisions, tool calls, actions, and results. Evaluate outcomes and use learning loops to improve prompts, knowledge, skills, routing, automations, permissions, and evaluation rules.
Two loops, working together
Operational loop
The system performs its job.
- Alert
- Investigate
- Diagnose
- Resolve
- Verify
Outer learning loop
Execution → Evaluate → Learn → Improve → Next execution
Feed improvements back into the agents and the system. ↶Use cases
Build for real operational work.
Different outcomes. The same model: agents, connected workflows, and learning loops.
Autonomous incident resolution
Connect monitoring to an Incident Agent with Node Management skills, then Developer and Verifier agents. Diagnose and resolve infrastructure incidents, escalate when needed, and improve remediation through an outer learning loop.
Software Factory
Turn requirements and issues into tested software changes. Triage, Developer, and Reviewer agents implement, test, and prepare changes for approved deployment. Learn from reviews, failures, and successful changes to improve the development harness.
Support Operations
Connect incoming tickets to a Support Agent with CRM access, company knowledge, and internal APIs. Resolve customer issues or escalate with context, then use outcomes to improve future responses.
Inside a Software Factory
- Issue
- Triage
- Developer
- Reviewer
- Tests
- Approved deploy
Platform features
Visible. Traceable. Manageable.
The capabilities that support your systems across all three stages.
Administration & observability
See agent runs, active workflows, decisions, tool calls, errors, approvals, incident history, learning-loop changes, and system status in the Admin App.
Permissions & control
Choose which actions are automated, restricted by policy, subject to human approval, or prohibited. Introduce autonomy at the pace your team needs.
Deployment
Run in the cloud or in a dedicated customer environment, including a private VPC or on-premise. Keep agents close to the infrastructure and knowledge they need.
Internal knowledge & memory
Give agents company documentation, architecture, runbooks, repositories, and previous incidents. Keep persistent context inspectable and versionable.
Skills & tools
Wrap APIs and internal systems into reusable capabilities. Agents decide what should happen; skills define how operations such as inspecting or restarting a node are performed.
Multi-agent systems
Give each agent its own role, knowledge, skills, permissions, model, and runtime. Coordinate specialized agents through shared artifacts and explicit handoffs.
Principles
Infrastructure agents and people can understand.
Bring your own intelligence
Models change quickly. The station keeps the environment portable.
Files before databases
Readable files make memory inspectable, versionable, and agent-friendly.
Explicit permissions
Powerful agents need clear access boundaries and auditable grants.
Local first, remote when needed
Run close to your own machine, then move online for persistence.
Build with OpenStation
Build your first agent system.
Start with a clear role. Connect a real workflow. Improve with every run.