Workflow assurance
Assurance for AI customer-service agents that take business actions
ActionSure tests whether AI customer-service agents complete stateful workflows safely: refunds, credits, verification, escalation, handoff, and case closure. It finds unsafe actions, missing fallback, repeat-contact risk, and unresolved loops before customers do.
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.
AI agents are moving from answers to actions
AI customer-service agents are no longer just answering questions. They issue refunds and credits, escalate tickets, close cases, and handle sensitive customer information.
Generic AI testing checks model responses. It does not show whether those actions are safe, recoverable, policy-compliant, or likely to create repeat contacts.
Safe-sounding, wrong action
An agent can sound helpful while issuing the wrong refund, closing the wrong case, or skipping required verification.
Hidden workflow failures
The costly failures are operational: unresolved loops, premature closure, missed escalations, and cold handoffs.
No replayable evidence
Teams need trace-level proof: what happened, which tool was called, what changed, and why the outcome risks a repeat contact.
Contact-center assurance
Contact-center failures, not just AI failures
Most AI tests focus on model responses. ActionSure tests the operational failures contact-center leaders care about.
The workflow is the test
ActionSure watches workflow state, not just agent text
Every scenario runs through a full state machine: input, customer pressure, agent action under policy, and a replayable record of the result.
Scenario
Billing dispute with missing invoice details and an impatient customer.
Customer turn
Customer refuses re-verification and claims prior approval.
Tool call
Agent applies credit before checking policy constraints.
Evidence
Trace, oracle, business impact, and remediation become a regression case.
How it works
Every tool call, every state change, replayable
ActionSure records the full conversation and every oracle check. Findings are evidence, not opinions.
Replayable trace evidence
What ActionSure does
A controlled test lab for action-taking agents
Most teams can write scripted tests; the hard part is knowing what to test. ActionSure runs your agent through realistic and adversarial conversations against simulated business state, then judges the outcome with deterministic oracles.
Adaptive stress testing
Impatient, confused, angry, or uncooperative customers, and where they get trapped in unresolved loops.
Business-action red teaming
Verification bypass, amount inflation, fake approvals, duplicate refunds, and privacy probes.
Deterministic oracles
Pass/fail comes from state, tools, and policy — not LLM judgment.
Failure review and classification
Each non-PASS run is classified: real agent failure, framework bug, test artifact, or expected safe fallback.
Human-fallback checks
Flags high-stakes issues that stalled without escalation or a manual-review path.
Business impact
Quantifies leakage, false denials, avoidable escalations, and repeat-contact risk.
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.
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 to make final refund or credit decisions on day one. ActionSure can test both models: agents that take approved actions directly, and agents that gather context, check policy, document the issue, and prepare a human handoff.
Autonomous
The agent executes approved actions directly: issue refund, apply credit, waive fee, escalate, close.
Human approval required
The agent gathers context, verifies, checks eligibility, and recommends. Final money actions require human approval.
Recommendation only
The agent documents the issue, summarizes evidence, recommends a next action, and prepares a handoff. It does not execute final business actions.
Reusable coverage
From pilot report to regression suite
The first pilot does more than produce a one-time report. ActionSure turns discovered failures into replayable regression scenarios that rerun whenever prompts, models, tools, or policies change.
- rerun on prompt update
- rerun on model update
- rerun on tool change
- rerun on policy change
Workflow packs
Designed for policy-governed workflows
ActionSure starts with a mature refund and return pack. The assurance model is designed for any AI agent that takes business actions under policy constraints, and each new vertical is added as a workflow pack.
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 available as a pilot-configurable second pack. The workflow-pack architecture is designed to extend to other policy-governed business workflows.
Run a two-week pilot on one workflow
You provide the target workflow, tool surface, and policy rules. ActionSure 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.