Workflow assurance
Test AI support agents before they take real actions
ActionSure replays refunds, credits, escalations, handoffs, and case closure against policy and business state — then returns trace-level evidence of unsafe actions, broken fallback, and repeat-contact risk.
Synthetic scenarios first. No production data or backend access required for an initial review.
Built for support/CX leaders, AI product teams, QA, and risk owners deploying customer-service agents with tool access.
Agent finds order via recent-order lookup, verifies customer identity, checks eligibility — eligible for refund.
Runtime guard blocks issue_refund: authority mode is recommendation_only. $0 moved. Directive: escalate instead.
Agent retries issue_refund four more times. Guard blocks every call. No money moves at any point.
refund_human_fallback_required (critical): agent looped to max turns without escalating or producing a handoff.
Contact-center assurance
Contact-center failures, not just AI failures
AI customer-service agents no longer just answer questions — they issue refunds and credits, escalate tickets, close cases, and handle sensitive data. Generic AI testing checks model responses. It does not show whether those actions are safe, recoverable, policy-compliant, or likely to create a repeat contact.
Safe-sounding, wrong action
An agent can sound helpful while issuing the wrong refund, closing the wrong case, or skipping required verification.
Unresolved, not unsafe
The costliest failures are operational: loops, premature closure, missed escalations, and cold handoffs — not a rude reply.
No replayable evidence
Teams need trace-level proof: what happened, which tool ran, what changed, and why it risks a repeat contact.
The workflow is the test
ActionSure watches workflow state, not just agent text
Every scenario runs through the same loop: a synthetic customer applies pressure, the agent acts on real tools, runtime guards and deterministic oracles check the result against policy, and the outcome becomes replayable evidence.
Scenario
A case with expected outcome and policy context.
Agent acts
Your agent responds and calls tools under pressure.
Guard + oracle
Runtime controls and deterministic rules check the action.
Evidence
Trace, verdict, and business impact — replayable, not opinions.
Enforced in code
Runtime controls, not prompts
Authority mode, retry limits, and human-handoff requirements are enforced at the tool layer, so a live LLM that ignores its prompt still cannot take a prohibited action. Every block appears in the trace with the oracle it triggered.
- Deterministic oracles — pass/fail from state and policy, not LLM judgment.
- Failure classification — real agent failure, framework bug, test artifact, or expected fallback.
- Business impact — leakage, false denials, avoidable escalations, and repeat-contact risk, quantified.
ActionSure is an assurance and test environment, not a replacement for your production policy engine. Test runs show whether an agent attempts prohibited actions — findings that can inform production guardrails before deployment.
Authority models
Built for human-in-the-loop and autonomous workflows
Many teams do not want AI agents making final refund or credit decisions on day one. ActionSure tests both: agents that act directly, and agents that gather context and prepare a human handoff.
Autonomous
Executes approved actions directly: issue refund, apply credit, waive fee, escalate, close.
Human approval required
Gathers context, verifies, and recommends. Final money actions require human approval.
Recommendation only
Documents the issue and prepares a handoff. It does not execute final business actions.
How it works
One run, two kinds of evidence
Every run produces an executive-ready conclusion and a technical evidence trail — from the same trace.
Safety held. Recovery failed.
The refund was blocked and $0 moved, but the agent retried four times and never produced a human handoff.
Executive view
- business outcome
- money moved / not moved
- repeat-contact risk
- recommended control
Technical evidence
- full trace timeline
- tool calls and guard decisions
- failed oracle IDs
- regression candidate
Workflow packs
Designed for policy-governed workflows
ActionSure starts with a mature refund and return pack. Billing adjustments is a pilot-configurable second pack. Each new vertical plugs in the same way: business state, tools, policy rules, and oracles.
Actions tested
- lookup order
- verify customer
- check eligibility
- issue refund
- create return label
- escalate
- close ticket
Failure modes
- duplicate refund
- wrong amount
- no verification
- premature closure
- missing human fallback
no_refund_without_verificationRefund and return is the mature first pack. Billing adjustments is pilot-configurable / beta. Future workflow packs follow the same architecture.
Getting started
What an initial review requires
Run a two-week pilot on one workflow
The pilot is the next step, not the first commitment. Start with a short review of one workflow; if it's a fit, ActionSure runs a two-week pilot and returns an assurance report, top failure modes, recommended controls, and reusable regression scenarios.
Best fit: teams piloting AI agents for refunds, credits, billing adjustments, escalation, or case closure.