Plan
Turn user intent into an inspectable workflow plan.
Agents decompose requests, identify data and tools, and expose the plan before high-risk action.
- Intent classification
- Context requirements
- Risk scoring
Agent orchestration
Run approved tool calls and retain each consequential step.
Move from chat responses to approved work execution with context packs, tool allowlists, policy filters, approvals, and full run traces.
Tool APIs
Workflow engines
CRM
ITSM
Finance systems
Approval queues
Act
Execute only through approved tools.
S/Runtime routes action through controlled connectors, approvals, and policy filters.
- Tool policy
- Approval gates
- Action logs
Learn
Measure what automation changed.
Governed runs connect to outcomes so teams improve agents, workflows, and controls over time.
- Outcome ledger
- Quality scoring
- Run analytics
Set the read, write, and review rules for this workflow.
Each run checks retrieval permissions, sensitive-data policy, tool scope, and approval requirements before it reaches a consequential action.
Tool allowlists and action scopes
Mapped to source permissions, context boundaries, action policy, and audit evidence so teams can prove how AI was used.
Human approvals for sensitive or high-impact steps
Mapped to source permissions, context boundaries, action policy, and audit evidence so teams can prove how AI was used.
Agent run tracing, reconciliation, and compensating-action evidence
Mapped to source permissions, context boundaries, action policy, and audit evidence so teams can prove how AI was used.
Model and prompt policy enforcement
Mapped to source permissions, context boundaries, action policy, and audit evidence so teams can prove how AI was used.
Segregation-of-duty checks for finance and regulated operations
Mapped to source permissions, context boundaries, action policy, and audit evidence so teams can prove how AI was used.
Measure the workflow against its baseline.
Use completed runs, exceptions, and review outcomes to measure “Workflow cycle time” against the agreed baseline.
Use completed runs, exceptions, and review outcomes to measure “Manual handoffs reduced” against the agreed baseline.
Use completed runs, exceptions, and review outcomes to measure “Policy exceptions prevented” against the agreed baseline.
Use completed runs, exceptions, and review outcomes to measure “Agent action quality” against the agreed baseline.
Architecture session
Bring one consequential workflow. Leave with a governed agent blueprint.
We will map the context, systems, decisions, controls, actions, and success measures together.