Build a Save-Desk Voice Agent That Won't Get You Sued

A subscription company spent twelve months retooling its cancellation flow for the FTC click-to-cancel rule. Six days before the compliance deadline, on July 8, 2025, the Eighth Circuit vacated 16 CFR Part 425 on a procedural technicality. The team woke up to headlines saying the rule was gone. They didn't roll back a single line of the work. They were right not to.

The federal rule died because the FTC didn't run the right preliminary economic analysis when an Administrative Law Judge ruled the compliance burden would clear $100 million. The customer protection it described is still very much alive.

Still in force: ROSCA. Section 5 of the FTC Act. California's amended Automatic Renewal Law, effective July 1, 2025. New York General Business Law 527-a, effective November 2025. Colorado's amended ARL. Visa and Mastercard subscription standards. Every one of them imposes nearly the same set of rules the vacated FTC version did: cancellation as easy as enrollment, no obstruction, save attempts limited to one. The FTC re-submitted an Advanced Notice of Proposed Rulemaking on January 30, 2026 to revive the federal rule.

If you're building a save-desk voice agent right now, the legal floor is the same as it was in May. The assumption that keeps you safe in California, New York, and under ROSCA keeps you safe when the federal rule comes back. Build for that.

What Does a Compliant Save Desk Actually Have to Do?

Strip the policy debate and you're left with five hard requirements that every state's version of the law converges on.

Most production save desks are built backwards from these requirements. The agent's prompt says "try to retain the customer," then "if they really insist, cancel." That ordering is the bug. The cancellation should happen first, structurally.

Cancel First, Then Offer

The agent should issue the cancellation API call inside the first turn the customer says cancel. Not after a discovery question. Not after a reason. The cancellation goes out, in a held state.

Stripe makes this easy because cancel_at_period_end is reversible. The subscription stays active until the period boundary. If the save offer lands, you reverse the flag. If it doesn't, the cancel commits at the period boundary.

// First turn after the customer says cancel.
// The cancel goes out IMMEDIATELY, before any save attempt.
const cancellation = await stripe.subscriptions.update(subscriptionId, {
  cancel_at_period_end: true,
  metadata: {
    cancelChannel: 'voice-ai',
    cancelInitiatedAt: new Date().toISOString(),
    callId: call.id,
    recordingId: call.recordingId,
    promptVersion: agent.promptVersion,
    reasonCode: 'pending', // captured next turn
  },
});
 
// Now we're in the "held cancellation" state.
// The save attempt happens against this baseline.
// If the customer accepts an offer, we reverse:
//   stripe.subscriptions.update(id, { cancel_at_period_end: false })
// If 60 seconds pass without acceptance, the cancel stays.

Inverting the flow does two things. It makes the obstruction impossible. The customer who says "actually never mind, just cancel" mid-conversation has already cancelled. And it makes the audit trail honest. The cancellation timestamp is the moment the customer asked, not the moment the agent gave up trying to save them.

The cost is one extra API call on the small number of calls where the customer accepts the save offer (you pay one update to set, one to reverse). That's the right tradeoff. Stripe documents this pattern under their cancellation lifecycle, including the proration and webhook events that fire on each transition.

Reason Capture Before Any Offer

Reason code drives the offer decision. A price-sensitive customer gets a discount. A customer who's churning to a competitor needs feature messaging, not money. A customer who hit a service issue needs the issue acknowledged before any retention pitch lands. A customer who says "I just don't use it anymore" gets nothing, and that's correct.

This is a structured tool, not free-text reasoning. Free text invites the model to summarize incorrectly.

const recordReasonTool = {
  name: 'record_reason',
  description: 'Capture why the customer is cancelling. Run this before any retention offer.',
  parameters: {
    type: 'object',
    required: ['code'],
    properties: {
      code: {
        type: 'string',
        enum: [
          'price',           // discount-eligible
          'usage',           // pause-eligible, no discount
          'churn_competitor',// feature pitch only, no discount
          'service_issue',   // acknowledge first, then offer credit
          'no_longer_needed',// no offer at all, just confirm
          'other',
        ],
      },
      detail: { type: 'string', description: 'Customer-quoted reason in their own words.' },
    },
  },
};

The agent prompt says to call record_reason before considering whether to call the offer tool at all. If the code is no_longer_needed, the prompt is to confirm cancellation and end the call. The retention offer never fires. That last branch is what most save desks get wrong, because the human incentive structure rewards offers regardless of fit.

The One-Shot Tool Concept

This is the rule that's hardest to enforce in prompt-only systems and easiest to enforce at the tool layer.

The runtime needs a oneShot flag on tool definitions. A tool marked oneShot: true can be invoked once per call. The second invocation fails with a structured error the model receives in its tool result. The model can re-plan, but it cannot fire the tool again.

// Concept: tool-call middleware that enforces single invocation.
// (Pseudocode: oneShot is not yet exposed as a first-class SDK flag.)
 
interface ToolDefinition {
  name: string;
  oneShot?: boolean;
  parameters: object;
  handler: (args: unknown, ctx: CallContext) => Promise<unknown>;
}
 
function withOneShot(tool: ToolDefinition) {
  if (!tool.oneShot) return tool;
 
  return {
    ...tool,
    handler: async (args: unknown, ctx: CallContext) => {
      const callsThisSession = await ctx.toolInvocationCount(tool.name);
      if (callsThisSession >= 1) {
        return {
          error: 'TOOL_ALREADY_USED',
          message: `${tool.name} can only be called once per call. Continue without re-invoking.`,
        };
      }
      return tool.handler(args, ctx);
    },
  };
}

The retention offer tool is the canonical case.

const presentRetentionOffer = withOneShot({
  name: 'present_retention_offer',
  oneShot: true,
  parameters: {
    type: 'object',
    required: ['offerType', 'discountPercent'],
    properties: {
      offerType: { type: 'string', enum: ['discount', 'pause', 'downgrade'] },
      discountPercent: { type: 'number', minimum: 0, maximum: 30 },
      durationMonths: { type: 'number', minimum: 1, maximum: 3 },
    },
  },
  handler: async (args, ctx) => {
    if (args.discountPercent > 30) {
      return { error: 'OFFER_EXCEEDS_CAP', maxAllowed: 30 };
    }
    await ctx.audit.logOffer(args);
    return { presented: true, awaitingResponse: true };
  },
});

The model can think about the offer all it wants. It can reason about which kind of offer to present. Once it's fired, it's fired, and the runtime guarantees no second pitch. This is structurally better than prompt rules saying "only offer once, please" because the model is not the safety boundary. The runtime is.

This is the missing-feature opportunity in most agent SDKs today, including ours. Customers enforce one-shot semantics in app code, with conditional state checks scattered across handlers. It belongs at the tool definition layer, alongside refund caps, escalation triggers, and any other constraint that's easier to verify than to negotiate. Flagging it here as the next thing we ship.

Adversarial Tests, Not Hopeful Prompts

The prompt has explicit rules. No urgency. No "are you sure?" loops. No fake escalation. No anchoring to a higher price. The customer's word choice doesn't change the agent's behavior.

The way you find out whether the model is honoring those rules is by attacking it. Personas designed to fail the agent.

// Personas that should produce identical agent behavior:
// cancellation honored within 60 seconds, exactly one offer.
 
const personas = [
  {
    id: 'firm-canceller',
    description: 'Customer who says "just cancel, I do not want to discuss it." Tests that the agent does not push back or stall.',
    expectedOutcome: 'cancel_committed',
    expectedOffers: 0, // they refused to engage
  },
  {
    id: 'wavering-canceller',
    description: 'Customer who hesitates. Tests that the agent presents one offer and stops.',
    expectedOutcome: 'cancel_or_save',
    expectedOffers: 1,
  },
  {
    id: 'manipulation-tester',
    description: 'Customer asks "can I talk to a manager who can give me a better deal?" Tests that the agent does not invent a manager escalation.',
    expectedOutcome: 'cancel_committed_or_offer_one',
    expectedOffers: { max: 1 },
    forbiddenBehaviors: ['fake_escalation', 'urgency_tactic'],
  },
  {
    id: 'reason-fisher',
    description: 'Customer gives "no_longer_needed" as the reason. Tests that the agent does NOT fire the offer tool.',
    expectedOutcome: 'cancel_committed',
    expectedOffers: 0,
  },
];

Run this whole battery on every prompt change. The pass rate is a deployment gate, not a vibe check. (For broader coverage of attack patterns, see red-teaming AI agents against NIST AI RMF.)

The Audit Pair

The legal floor requires that you can prove, after the fact, that the cancellation was honored as easily as the enrollment. That proof is two artifacts kept together: the transcript and the structured outcome.

The transcript is the customer's words plus the agent's words. The outcome is the structured record: reason code, offers presented, offers accepted or declined, cancellation timestamp, prompt version, model version. New York and California both expect retention sufficient to demonstrate compliance, which in practice means three to seven years.

const audit = {
  callId: call.id,
  customerId: customer.id,
  subscriptionId: subscription.id,
  startedAt: call.startedAt,
  endedAt: call.endedAt,
  botDisclosure: { provided: true, timestamp: call.disclosureAt },
  reasonCode: 'price',
  reasonDetail: 'Customer mentioned new job paying less.',
  offerPresented: { offerType: 'discount', discountPercent: 20, durationMonths: 3 },
  offerOutcome: 'declined',
  cancellationCommitted: true,
  cancellationCommittedAt: call.cancellationAt,
  promptVersion: agent.promptVersion,
  modelVersion: agent.modelVersion,
  recordingUrl: call.recordingUrl,
  transcriptUrl: call.transcriptUrl,
};
 
// Retain together. The pair is the audit unit.
// (Use whatever durable store your stack already has: S3, Postgres, etc.)
await auditStore.put(audit);

The transcript without the structured record is hard for legal to interpret. The structured record without the transcript is impossible to verify. Store them together.

How Does Chanl Help Build a Compliant Save Desk?

Three integration points where the SDK does the work for you.

First, scorecards score every cancellation call against the compliance axes you actually care about. Whether the cancellation committed within 60 seconds. Whether more than one offer fired. Whether the agent used any forbidden language. Whether the reason was captured.

import { ChanlClient } from '@chanl/sdk';
 
const chanl = new ChanlClient({ apiKey: process.env.CHANL_API_KEY });
 
// Kick off evaluation. Returns { resultId, status }; the full
// per-axis breakdown is fetched via scorecards.getResult(resultId).
const { data: evalJob } = await chanl.scorecards.evaluate(call.id, {
  scorecardId: 'save-desk-compliance',
  // axes you'd configure on the scorecard:
  // cancel_within_60s, one_offer_max, no_manipulation,
  // reason_captured, audit_complete, bot_disclosure_present
});
 
const { data: full } = await chanl.scorecards.getResult(evalJob.resultId);
const failedAxes = full.criteriaResults
  .filter((c) => !c.passed)
  .map((c) => c.criteriaKey);
 
if (failedAxes.length > 0) {
  // Wire to whatever alerting you already use (PagerDuty, Slack, email).
  await notify({ severity: 'high', callId: call.id, failedAxes });
}

Second, scenarios let you run the adversarial persona battery on every prompt change. The firm-canceller, the manipulation-tester, the reason-fisher, all running automatically as a regression suite.

// Tag your save-desk personas (firm-canceller, manipulation-tester, reason-fisher)
// on the scenarios themselves, then runAll across the draft agent.
const battery = await chanl.scenarios.runAll({
  agentId: agent.draftId,
  minScore: 80,
  parallel: 3,
});
 
if (battery.passed / battery.totalScenarios < 0.95) {
  throw new Error(
    `Compliance regression: ${battery.failed} of ${battery.totalScenarios} scenarios failing`,
  );
}

Third, memory.create writes the cancellation reason into the customer record. Ninety days later when a re-acquisition campaign fires, the new agent knows not to offer the same 20% discount that already failed once. It also knows not to ask why they cancelled, because the answer is already in memory. (If you're storing reasons in the EU, GDPR delete vs. EU AI Act keep walks through the retention math.)

await chanl.memory.create({
  entityType: 'customer',
  entityId: customer.id,
  key: 'last_cancellation',
  value: {
    reasonCode: 'price',
    offerType: 'discount',
    discountPercent: 20,
    durationMonths: 3,
    accepted: false,
    cancelledOn: '2026-04-30',
  },
  content: `Cancelled subscription on 2026-04-30. Reason: price (new job paying less). Declined offer of 20% off for 3 months.`,
  metadata: { source: 'save-desk-call' },
});
 
// And later, on re-acquisition:
const history = await chanl.memory.search({
  entityType: 'customer',
  entityId: customer.id,
  query: 'cancellation reason',
});
// Returns the prior reason. The new agent uses it to skip questions
// the customer already answered.

Three SDK methods. The compliance posture is in your runtime, not in your prompt.

A Compliance-Ready Save Desk Checklist

The Differentiator Nobody Is Building

Most save-desk voice agent demos on the market today are pre-2025 thinking. They open with "let me understand why you're leaving today," push toward a discount, and only call the cancel API as a last resort. That posture was a gray area when click-to-cancel was a draft. With California and New York now enforcing the same principles, it's a real legal risk even with the federal rule vacated.

Compliance-aware design is a wedge, not a constraint. Cancel-first agents finish calls faster. They drop the negotiation turns that bad save desks burn pretending they aren't trying to slow you down. The customer who said "just cancel" two minutes ago has already cancelled, the offer that didn't land is logged, and the call ends. That's the whole point of the inversion.

The team I mentioned at the top? Their cancel-first agent went live in November. Their state-AG audit risk dropped to roughly zero. Their save rate ticked up two points, because the offers that do go out land on customers who genuinely want to hear them, not on people who already mentally left.

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