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How ActionSure works

ActionSure evaluates AI agents in a controlled, stateful simulation. The agent talks to synthetic customers, calls simulated business tools, and is judged by deterministic policy and workflow oracles. It measures whether each workflow completes safely or creates repeat-contact risk.

Process

The assurance loop

1

Scenario

Synthetic customer-service case with expected outcome and policy context.

2

Customer actor

Scripted, stressed, or adversarial customer behavior.

3

Agent under test

Your agent responds and calls tools.

4

Business state

ActionSure updates order, invoice, customer, ticket, and workflow state.

5

Oracles

Deterministic checks evaluate policy, privacy, workflow integrity, human fallback, and business-action safety.

6

Report

Replayable trace, failure classification, repeat-contact risk, and business impact.

Coverage

What we test

Stress testing

  • confused identity
  • missing order or invoice information
  • impatient customer
  • emotional escalation
  • multilingual or verbose messages
  • repeated requests
  • tool timeout or stale state

Business-action red teaming

  • verification bypass
  • amount inflation
  • duplicate refund or credit
  • fake manager approval
  • privacy extraction
  • prompt injection
  • approval bypass
  • stale-state manipulation

Contact-center workflow integrity

  • premature closure
  • missing human fallback
  • unresolved self-service loop
  • cold handoff / lost context
  • escalation without communication
  • repeat-contact risk

Deterministic oracles

LLMs can generate pressure. They should not be the final judge.

ActionSure may use LLMs to simulate difficult customers or discover new failure paths. Final pass/fail comes from trace evidence: tool calls, state, and policy rules.

  • LLM as attackeryes
  • LLM as stress actoryes
  • LLM as final judgeno

Architecture

Workflow-pack architecture

A shared core engine runs every test. Each business vertical plugs in as a workflow pack around that core. ActionSure starts with a mature refund and return pack; billing adjustments is a pilot-configurable second pack, and the architecture is designed to extend to other policy-governed workflows.

  • Refunds
  • Billing
  • Claims
  • Cancellations
  • Account changes

Core engine

  • runner
  • trace
  • adaptive actors
  • oracles
  • reports

Each workflow pack plugs into the same core engine.

Audience

Who it is for

Contact-center and CX operations leaders
AI customer-service teams
CX automation teams
QA and release teams
Trust and safety teams
Companies deploying refund, return, billing, claims, or support agents