Agent workflow and task-state design
Problem we solve
Agent automation fails when tools, permissions, approval points, and recovery behavior are undefined. Teams need an explicit operating model before an agent can take action.
What Raum builds
Raum Network develops agent workflows that connect models to business tools, APIs, data, and operator review. We define what automation may do, what needs approval, and how actions remain visible.
Architecture and technology
API, system, and data integrations
Permission scopes, approvals, and audit records
Monitoring, retries, exception handling, and operator controls
Use cases
Operations workflows with human approval gates
Research, triage, and knowledge-routing assistants
Tool-connected agents for internal product teams
Delivery process
Identify repeatable task and responsible operator
Set boundaries for read, write, approval, and execution actions
Build integration path with logs and test scenarios
Review exceptions before broader rollout
Engineering evidence
Every action has defined source, permission, and owner
Approval points remain visible to operators
Failure paths can pause, retry, or hand work back to people
SERVICE FAQ
Questions before scope starts.
Q.001Can an AI agent execute actions without approval?
Only where scope explicitly permits it. High-impact actions should have defined permissions, controls, and a recovery path.
Q.002How do you measure an agent workflow?
We establish task completion, handoff quality, exception rate, and operator-review criteria before expanding automation.
START WITH CONTEXT
Bring product, system, and delivery constraints.
We will use them to define a technical scope, practical milestones, and next engineering step.