# A guide to set up outbound AI voice agent for customer service

> Source: <https://dev.to/aharna/a-guide-to-set-up-outbound-ai-voice-agent-for-customer-service-32p0>
> Published: 2026-10-08 04:06:52+00:00

An outbound AI voice agent is software that places phone calls on a business's behalf, using speech recognition and generative AI to hold a real conversation instead of playing a fixed script. This guide walks through setting up an outbound AI voice agent end to end, using the AC service follow-up example throughout, and covers how to tell whether the calls are actually working once they're live.

An outbound AI voice agent places calls instead of waiting for a customer to call in. It's triggered by an event in another system—a CRM update, a rejected service ticket, a scheduling change—and it calls the customer with a defined goal.

This is different from an inbound agent, which answers calls that come in. It's also different from a traditional IVR or auto-dialer, which plays fixed menus or pre-recorded messages and can't handle an open-ended answer.

Common enterprise use cases include:

The AC service example used throughout this guide falls into the third category: a post-service re-engagement call that confirms whether the customer still needs help.

Here's the scenario. A field team rejects a customer's air conditioner service request—the part wasn't in stock, or the technician had to reschedule. Instead of the request sitting untouched, an outbound AI voice agent calls the customer directly: is the service still needed, or has it been handled another way?

This is a good first use case for outbound AI because it's low-stakes, structured, and happens at volume. The agent doesn't need to negotiate a contract or resolve a billing dispute; it needs a yes, a no, or an escalation. That's easy to script, easy to test, and easy to measure.

Before configuring anything, have two things ready:

Everything else in the setup builds on these two.

The steps below are platform-agnostic, but the specifics are shown using DronaHQ's voice agent as the working example.

Start inside [DronaHQ's voice agent builder](https://www.dronahq.ai/voice) and connect telephony through a SIP trunk. A Twilio Elastic SIP Trunk works if you already run call infrastructure on Twilio. Once the trunk is connected, the number shows up in the platform's **Phone Numbers** list, and you route it to your agent under **Call Configuration → Outbound Settings**.

From there, go to the admin console and click **Create Agent**.

**Important:** Each phone number is tied to one agent and one live call at a time. If you're planning to call a large customer list in a short window, you'll want more than one number, or the campaign will just take longer to work through. Figure out your expected call volume before you commit to a single number.

With the agent created, set up the core pieces:

The first message can be static, but for anything customer-facing, use placeholders instead. A generic opener sounds like a robocall the second the customer hears it.

Something like:

"Hi {{customer_name}}, this is {{company_name}} calling about your air conditioner service request. We wanted to check, is the service still needed, or has this been resolved?"

works better because it names the customer and the actual request, so the call feels like a real follow-up instead of a mass dial.

For the instructions, don't just write "handle the customer's response." Spell out each branch explicitly:

Agents that are only told the goal of the call, without the branches, tend to over-explain or loop back on themselves when the conversation goes slightly off script. Writing out the branches ahead of time avoids that.

The agent needs access to the systems that hold customer data or receive call outcomes, usually through a webhook or a set of connected tools.

In DronaHQ, this is the **Tool Builder and Connector Library**, with pre-built integrations with tools like Google Sheets, Slack, Notion, and Gmail, plus broader access through MCP and Composio for anything not natively supported.

Connect at least one destination **before** you launch anything, not after. It's tempting to run a campaign first and wire up Slack or a tracking sheet later, but then you're stuck manually pulling outcomes from the call logs for however long that gap lasts.

For the AC example, connect a Slack channel or a tracking sheet upfront, so every call outcome lands somewhere your team can act on it immediately.

With the agent built and tools connected, set up a campaign to actually place the calls:

`mobile_number` or `customer_name`.
That calling window matters more than it looks. As someone running a customer support team, you'd want the agent calling during hours when people are actually awake and likely to pick up, not at 6 AM or late at night.

Set the window to something like **9 AM to 7 PM in the customer's timezone**, and let auto-retry pick up the ones who miss the first call instead of trying to time everything perfectly on the first attempt.

Also, double-check the agent is **published** before you launch. Campaigns only pull in published agents and published changes, so if you tested a script tweak and forgot to publish it, the campaign runs on the older version without telling you.

Once the campaign is running, don't just let it finish and check back later. Open the **Campaign Overview** page early and watch the pick-up rate for the first batch of calls.

If it's unusually low, that's often a sign the calling window is off, or a chunk of numbers in the CSV are bad. It's cheaper to catch that after twenty calls than after two thousand.

The overview gives you:

Spot-check a few transcripts on calls marked as completed, not just the failed ones, to confirm the agent is actually closing conversations the way you intended, not just technically finishing them.

For the AC example, this is where you'd see how many customers:

—and you can do that without listening to a single call yourself.

Setting up the agent and running the campaign is half the work. The other half is what happens after each call ends, and this is where most outbound AI evaluations fall short because a transcript by itself doesn't tell you whether the program is working.

Post-call analysis is best understood as four layers, each doing a different job:

A turn-by-turn record of what was said, plus a technical log—connection time, agent decisions, system events—used to debug when something goes wrong.

Routing the outcome of a call into the systems your team already uses, like a Slack alert or a Google Sheets row, without anyone copying data by hand.

Pulling defined fields out of the conversation, like disposition, reject reason, and sentiment, using a fixed schema instead of free-text notes.

A dashboard built on top of that structured data, so trends show up without exporting anything.

Applied to the AC service example, every completed call produces a structured record such as:

```
disposition: service_still_needed
reject_reason: part_unavailable
sentiment: neutral
```

That record can trigger a Slack alert to the field-service team or log a row in a tracking sheet automatically.

Here's how to actually wire that up instead of leaving raw transcripts sitting in the call log:

Set up a schema for the fields you actually want to extract: disposition, reject reason, sentiment, or whatever drives a decision downstream. This runs as part of the call, not as a separate step, so the data is ready the moment the call ends.

Have the agent fire a webhook once the call ends and the structured output is ready, instead of someone opening the call log to check. This is what turns "the data exists somewhere in the platform" into "the data shows up where you need it."

Point that webhook at an automation that runs on receipt, and have it write the structured fields into a database. A Google Sheet works fine to start—it's low effort and everyone on the team already knows how to open it. Move to a proper database once volume makes a spreadsheet painful to query.

Once outcomes are landing in one place consistently, connect a dashboard to it. This is where connect rate, disposition mix, and reject reasons over time actually become visible, instead of numbers you'd have to calculate by hand from the call log each week.

**Do this before you scale up call volume, not after.** It's a lot easier to build the pipeline against a hundred calls a day than to retrofit it once you're running multiple campaigns and nobody's tracking outcomes consistently.

| Dimension | Manual Call Review | Structured Post-Call Analysis | 
|---|---|---|
| Transcript availability | Often audio-only, transcribed on request | Every call, turn by turn | 
| Time to insight | Hours to days | Seconds after the call ends | 
| Coverage | A sample of calls | 100% of call volume | 
| Routing to other tools | Manual copy-paste | Automated via connected tools | 
| Data format | Free-text notes | Structured, consistent fields | 
| Dashboard | Rebuilt from exports each time | Built once, reused ongoing | 

The practical difference is **coverage**. Reviewing a sample of calls was always a compromise. A structured pipeline reviews all of them, which matters most in workflows where a missed rejection reason has a real operational cost.

| Dimension | Manual Call List | Traditional IVR / Auto-dialer | Outbound AI Voice Agent | 
|---|---|---|---|
| Setup effort | Low, but ongoing staffing cost | Moderate, fixed menu logic | Moderate, instructions + integration setup | 
| Personalization | High, but inconsistent | None, fixed prompts only | High, placeholder-driven and dynamic | 
| Cost at scale | Scales with headcount | Cheap per call, low value | Scales with usage, not headcount | 
| Escalation handling | Native, it's a human | Poor, dead-ends often | Configurable transfer rules | 
| Data capture | Manual notes, inconsistent | Minimal, keypress-based | Structured, per-call, automated | 

Voice AI is moving from a channel that talks to one interface into a larger agentic workflow. The call itself is rarely where the value shows up; it's what happens right after: a record updated, a team alerted, a request closed out, without anyone touching a spreadsheet.

That shift also changes what's worth measuring. Instead of aggregate stats like average handle time, teams get semantic data:

The next step past this is **agentic follow-through**, where a structured call outcome doesn't just populate a dashboard—it triggers the next action automatically, whether that's a re-dispatch, a refund check, or a CRM update.

Setting up an outbound AI voice agent isn't complicated once the pieces are in order: a number, agent instructions, a few tools, and a campaign to run it against.

What separates a working program from a stalled one is usually what happens **after the calls**—whether the outcomes are structured enough to act on.

Start narrow, with one use case like the AC service follow-up, check the results, then expand.

If you're building this out further, the next useful read is [how to measure ROI on voice AI](https://www.dronahq.com/measure-voice-ai-roi/) programs once they're past the pilot stage. Voice agents will keep getting better at holding a conversation; the bigger gap, for most enterprises, is still what happens in the seconds after the call ends.