Switchkit controlled workflow
Set Up, Analyze, Import & Review, Build, Approve, and Publish were validated end to end with explicit human gates, private-before-publish state, bounded tools, and recoverable transitions.
Applied Lab
DemonstratingAgentic Workflows
Agentic Workflows explores how humans, agents, tools, data, and enterprise systems coordinate work.
What I’m exploring
What happens when AI systems move from answering questions to participating in enterprise workflows and taking governed actions?
Useful agents need more than model intelligence. They require trusted context, controlled tools, explicit workflow state, evaluation, escalation, and human authority proportionate to the consequences of action.
Framework → Lab
A focused test of connected enterprise capabilities
The capability model describes what an enterprise needs. This Lab creates practical evidence across the highlighted areas below.
AI & Automation
Defines where agents fit across the automation continuum.
Governance
Defines decision rights, oversight, policy, and accountable owners.
Architecture
Provides orchestration, integration, tool, and system boundaries.
Data
Provides authorized enterprise context, provenance, and memory boundaries.
Security
Deepens identity, permissions, containment, and action-control concerns.
Execution
Connects experiments to reliable implementation and operation.
What I’ve applied
Concrete practices and inspectable evidence
The work combines implementation patterns, controls, evaluation, and current product evidence; it does not rely on expertise claims alone.
Human → Agent
A person delegates a bounded task with an explicit goal and completion boundary.
Human → Agent → Tool
The agent invokes an approved tool within constrained permissions and parameters.
Agent → Workflow
Probabilistic reasoning participates within explicit deterministic states and checks.
Agent → Agent
Specialized agents coordinate bounded responsibilities without obscuring ownership.
What I’m learning
Applied depth comes from operating constraints
These are working lessons for technology leaders evaluating how AI moves from experiments into secure, governed production work.
- 01Greater autonomy is not inherently greater maturity.
- 02Tool access and explicit workflow state are as important as model behavior.
- 03Deterministic first, with model escalation only when ambiguity requires it, improves cost, speed, repeatability, and governance.
- 04Human approval and private-before-publish boundaries make consequential work reviewable.
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