Handle Subscription & Billing Questions with an AI Agent

This is exactly the gap Macha closes. Macha is an AI agent layer that sits on top of the helpdesk you already use — Zendesk, Freshdesk, Gorgias, Front — and connects it to the systems where the real answers live. For subscription and billing questions, that means a billing agent that reads the incoming ticket, pulls the customer's record and payments from Stripe, looks up the relevant plan policy in Notion, and hands your human a complete, drafted reply to review. No tab-switching, no copy-paste, no guessing at the proration math.

This post walks through building that exact agent — the pre-built "Handle subscription billing questions" use case — and where to keep a human firmly in the loop.

The problem with billing tickets specifically

Most support automation advice treats every ticket the same. Billing is different in three ways that matter:

• The answer is data, not a doc. "What's my renewal date?" can't be answered from a help-center article — it's a live lookup against the customer's actual subscription. A bot that can only search your knowledge base is, as one industry guide bluntly puts it, "just a smarter FAQ."
• Mistakes cost money and trust. Quote the wrong proration, promise a refund that doesn't apply, or confirm a plan change that doesn't match policy, and you've created a billing dispute — the single fastest way to lose a paying customer.
• The work spans systems. The question arrives in Zendesk, the data lives in Stripe, and the policy lives in a Notion or Confluence wiki. No single tool has all three.

The scale of the problem is well documented. Fini's 2026 guide on AI platforms for refunds, returns and billing disputes reports that billing tickets account for 34% of total support volume and that a single manual refund consumes about 14 minutes of agent time. The same guide benchmarks autonomous resolution across vendors on these flows — Ada around 74%, Intercom Fin 51% on average (72% for top performers), Zendesk AI 60–70% on deterministic ticket types, and Decagon in the 70–85% range — with Fini itself claiming 98% accuracy with zero hallucinations on billing actions (vendor-reported figures, so treat them as directional rather than guaranteed). But the guides are clear about why any of those numbers are possible: the gains come almost entirely from agents that can read the billing system directly. That's the bar. An agent that can't fetch an invoice status or a customer's plan is not going to move those numbers.

What the Macha billing agent does

Here's the workflow from the use case, step by step:

1. Trigger — a Zendesk ticket is created (or a customer message arrives) and the agent detects it's about subscription or billing.
2. Look up the customer — Macha calls Stripe's Get Customer and List Payments to find the exact account, current plan, and recent charges.
3. Find the policy — Macha runs Search Pages against your Notion workspace to pull the relevant rule: plan-switching, proration, refund eligibility, renewal timing.
4. Draft the reply — Macha composes a summary that combines the live billing data with the policy, and presents it to the human agent: "Want me to draft a reply with these details?"

A real example from the use case: a customer asks if they can switch from monthly to annual. The agent finds them on Pro Monthly at \$29/mo, active since Jan 2025 (Stripe), finds that annual Pro is \$290/yr — a \$58 saving (Notion pricing page), and confirms that per policy, the change takes effect at the next billing date with pro-rated credit applied automatically. That's a complete, accurate answer assembled from two systems in the time it takes the human to read it.

The screenshot above is the agent I actually built for this — a "Billing Escalations" agent whose entire job is to detect and escalate billing tickets. Its instructions read: "You are an escalation manager for the billing team. Monitor support tickets and: 1. Identify tickets related to billing (invoice/bill copy requests, payment confirmations, making payments…)." Everything an agent does is defined in five blocks — Instructions, Triggers, Tools, Data Sources, Sub-Agents — and you'll notice the model selector showing the credit cost per message. Macha bills per AI action, and the cost scales with the model you pick (here GPT-5 at 3 credits/message; the default GPT-5.4 Mini is 1 credit), so you can run a cheap fast model for triage and reserve a stronger one for the gnarly disputes.

The tools that make it possible

The agent is only as good as what it can reach. Two connectors do the heavy lifting:

Stripe (connected with an API key) gives the agent natural-language access to your billing data — Get Customer, Search Customers, List Payments, Get Payment, and, when you allow it, Create Refund and Create Charge. That's the difference between an agent that describes your refund policy and one that can actually check whether a specific \$29 charge is eligible for one.

Notion (connected via OAuth) lets the agent Search Pages and Get Page across your workspace, so your plan rules, proration logic, and refund windows are pulled live — and because Notion can also Sync to Knowledge, those docs stay current without anyone re-uploading PDFs.

If your policies live in Confluence, Google Workspace, or a Help Center instead, the pattern is identical — swap the knowledge connector. The Zendesk connector itself syncs your Help Center as a live knowledge source, so for many teams the "policy" half of this is already wired in.

Keeping a human in the loop (and when not to)

Here's the honest part, and the part most "automate everything" pitches skip. Billing is precisely where you should not hand the keys over blindly.

The default for this use case is read, then draft — Macha gathers everything and proposes a reply, but a human approves it. For read-only actions (looking up a customer, listing payments, searching policy) that's pure speed with no risk. For write actions — issuing a refund, changing a plan, creating a charge — Macha supports an explicit confirmation step before anything touches the customer's money.

That confirmation card is the guardrail. The agent has decided what to do and assembled the parameters, but it pauses and waits for a human Confirm before executing. For a refund or a plan change, that's exactly the gate you want — the speed of automation on the lookup, a deliberate human decision on the money.

You can also run the agent purely as a draft assistant inside the chat tab — point it at a ticket and get the composed reply, suggested next actions and all, without it ever posting autonomously.

When NOT to fully automate billing:

• Dunning, chargebacks, and disputes — anything heading toward a payment processor's dispute system needs a human. Let the agent gather context; let a person decide.
• Refunds above a threshold — set a credit/dollar ceiling under which the agent can act with confirmation, and above which it must escalate.
• Cancellation "save" flows — retention is judgment-heavy and brand-sensitive; use the agent to surface the account context, not to negotiate.
• Anything that changes recurring revenue — plan upgrades/downgrades that alter MRR should at minimum pass through the confirmation card, and often through a named owner.

The rule of thumb: automate the lookup, gate the write, escalate the dispute. That keeps you on the high-value side of those benchmark numbers without betting your billing reputation on a model's judgment.

How this compares to the marketplace apps

If you've shopped for this before, you've seen the Zendesk-marketplace options, and it's worth being clear about the difference.

(Pricing for third-party apps is approximate and drawn from public listings as of June 2026 — verify on the vendor's page.) Sidebar apps put the data next to the agent; automation tools move records around. Macha is the only one of the three that does the reading, reasoning, and drafting — which is the part that actually costs your team time. It's also an additive layer: keep your sidebar app if you like it, and let Macha do the synthesis.

How this compares to the AI support agents

Marketplace apps aren't the only thing you'll be weighed against. If billing automation is the goal, the standalone AI-agent vendors — Intercom Fin, Fini, and the billing-native combos like Stripe + Chargebee's own automations — are the real shortlist. Here's the honest read.

The pattern is the same one that runs through our Intercom Fin AI agent guide and our Fini AI complete guide: the standalone agents are powerful, but they generally want to be (or replace) your support stack and bill per outcome. Macha takes the opposite stance — it sits on the helpdesk and billing tools you've already standardized on, and charges per action so a cheap read-only lookup stays cheap. For deflection-vs-resolution-vs-automation framing, our piece on automated resolution vs deflection is the deeper read.

Watch-outs before you ship

• Your policy has to be written down. The agent can only quote a proration rule that exists in Notion (or your Help Center). If your refund policy lives in three people's heads, fix that first — the agent will faithfully surface whatever it finds, including stale pages.
• Scope the Stripe key. Use a key with only the permissions the agent needs, and gate every write behind confirmation until you trust it on read.
• Match the model to the job. A cheap model is fine for triage and lookups; bump to a stronger model for ambiguous disputes. You control this per agent, and credits scale accordingly — see the pricing page.
• Identity matters. Make sure the agent matches the requester to the right Stripe customer (Zendesk Get Custom Fields can carry the customer ID) — never resolve by name alone on a shared-inbox ticket.

FAQ

Does Macha replace my helpdesk? No. Macha is an AI agent layer on top of Zendesk (or Freshdesk, Gorgias, Front). It reads and replies to tickets in your existing helpdesk — it doesn't replace it. See Macha on Zendesk.

Can the agent actually issue a refund, or just look one up? Both. Stripe's Create Refund and Create Charge are available as tools, but write actions sit behind an explicit confirmation card so a human approves before any money moves. Most teams start read-only and enable confirmed writes once they trust the agent.

Where does the agent get plan and policy answers? From your knowledge — Notion pages (live sync), your Zendesk Help Center, Confluence, or Google Workspace docs. It searches them at answer time, so it quotes your current policy, not a snapshot.

How much does it cost to run? Macha bills credits per AI action, and the per-message cost depends on the model you choose (e.g. a default lightweight model at 1 credit vs. a frontier model at several). There are no per-resolution fees. Start with a 7-day free trial, no credit card required; full plans are on the pricing page.

What if my billing isn't on Stripe? The pattern is connector-agnostic — the agent reads whatever billing or commerce system you connect and pairs it with your policy docs. Stripe and Shopify are live today; check integrations for the current list.

Get started

If billing tickets are eating your team's afternoons, this is the use case to start with — the data is structured, the policies are knowable, and the confirmation gate keeps the risky part safe. Spin up a 7-day free trial, no credit card required, connect Zendesk and Stripe, point the agent at your Notion policy pages, and let it draft the next "can I switch to annual?" reply for you to approve. Read the Stripe integration details or browse the full use-case library to see what else you can wire up.

Written by Abbas (Customer Support & AI, Macha) · Reviewed by Ankeet Guha (Co-founder & CTO) · Published 2026-06-24 · Last updated 2026-06-24.