Agentic AI
An agent you can watch, question and stop.
Agentic AI takes a goal, plans the steps and calls business systems to carry them out. That makes it useful, and it makes the design around it essential. This demo shows the working loop on sample data.
How the demo works
Three steps, one decision.
- 01
Choose a sample invoice
Pick one of three invoices. Each one raises a different question for the agent.
- 02
Watch the agent work
It runs four checks in a fixed order, and every result is logged the way a production system would log it.
- 03
Read the decision
You see the outcome, the reasons for it, the owner who acts next and the human gate that applies.
Interactive demo
Accounts payable exception agent
The checks and policy run in your browser on fictional sample data. No live system is contacted and no language model is called. Each step is real logic, so the result is the same every time.
Choose a sample invoice
- Invoice
- INV-90117
- Amount
- $1,240
- Vendor
- Northwind Office Supplies
- Description
- Printer toner and paper, October delivery
Sample data only. No live system is contacted.
agent.trace / INV-90117
Ready- 1extract_fields
- 2check_vendor_master
- 3match_purchase_order
- 4search_ledger
- 5apply_policy
- 6record_decision
The decision and its reasons appear here when the checks finish.
Designing agents for production
What makes an agent safe to run in your business.
A demonstration shows that an agent can act. A production system also needs the controls below, designed in from the first build.
Scope
Name the systems, records and actions the agent may reach. Enforce the list with permissions, not with instructions alone.
Approval gates
Put people in front of consequential actions such as payments, customer messages and deletions.
Stop conditions
Define when the agent must halt and escalate: low confidence, missing data, conflicting sources, repeated failure.
Evaluation
Test normal work and the awkward edge cases before release, and again after every change to prompts, tools or models.
Audit trail
Record inputs, tool calls, results and the final decision, so any case can be reviewed months later.
Ownership
Name who answers for the agent's decisions and who can pause it, and test the pause.
What this demo is
- A working example of the agent loop: extract, check, decide, record.
- Deterministic logic with a written policy, so you can inspect every rule.
- A view of the human gates that a production agent must respect.
What it is not
- It does not connect to any accounting, ERP or banking system.
- It does not use a language model. A production agent would use one for extraction and planning, within the same checks.
- It does not describe a client result. Figures and outcomes depend on your data and your baseline.
Have a process you would like an agent to handle?
Tell us the workflow, the systems and the decisions involved. We will help you decide whether an agent is the right tool, and what guardrails it would need.