Interpret and plan
A model selects possible next steps using the task, context and instructions. Model output can be wrong and requires evaluation.
AGENT FOUNDATIONSFrom a goal to
From a goal to
coordinated action.
An agent combines a model, instructions, information and tools to progress a defined task. Its autonomy depends on the permissions and limits around it.
Act through systems
Approved interfaces let software retrieve records or take scoped actions. Read access and permission to change a record are separate decisions.
Check and stop
Policies, human checkpoints and execution limits constrain the workflow. Logs support investigation and improvement after each run.
A SIMPLE REFERENCE WORKFLOWMake each step
Make each step
visible.
This demonstration walks through a fictional service request. It runs entirely in your browser and does not invoke an AI model.
Choose the scenario to see how a policy exception changes the handoff.
Illustrative architecture, independent of any provider.
- Receive the request
- Retrieve approved knowledge
- Check the action policy
- Prepare a response
- Record the outcome
Ready. Start the demonstration.
When a fixed workflow is enough
A predictable sequence with stable rules may be better served by conventional automation. Agents become useful when interpreting varied requests or selecting among approved actions matters. Start with the simplest architecture that meets the requirement.
When agents collaborate
Separate agents can handle distinct roles, with an orchestrator coordinating the task. More agents also create more handoffs, permissions and failure modes. Define what each agent may delegate, and preserve a record of decisions.
Design for imperfect execution
Test missing data, conflicting instructions, unavailable tools and malicious content. A failed action should leave a clear status, avoid uncontrolled retries and reach an owner who can resolve it.
Build the governance checklist →Turn a workflow into a plan.
Start with the task. Make the assumptions visible.