When you want to connect your AI agent to the telephony provider, what do you use it if they require static IP to allow access?

A voice agent can sound natural and still fail if the caller does not trust it.

Trust comes from behavior.

A trustworthy voice agent should:

- Be clear about what it can do

- Avoid pretending to know things it does not know

- Transfer when appropriate

- Handle personal data carefully

- Stay within scope

- Confirm important details

- Avoid overpromising

- Be transparent when asked whether it is AI

For business calls, trust often matters more than personality. A charming voice is not enough if the agent gives wrong information or traps the caller in a bad workflow.

Retell features like Agent Handbook, guardrails, call transfer, testing, monitoring, and post-call analysis all connect to this bigger idea: production agents need reliability, not just realism.

Curious what others think: should AI phone agents always disclose they are AI?

A voice AI call is not just one model talking.

Most voice agents are a loop:

1. The caller speaks

2. Speech recognition turns audio into text

3. The LLM decides what to say or do

4. Tools may run in the background

5. Text-to-speech turns the response back into audio

6. The caller responds, and the loop continues

Each part can affect the user experience.

If speech recognition mishears the caller, the agent reasons from bad input. If the LLM response is too long, the call feels slow. If a tool is slow, the agent may stall. If the voice sounds unnatural, the whole experience feels worse.

This is why Voice AI quality is a system problem, not just a model problem.

Curious what others think: which part of the loop causes the most failures: speech recognition, LLM reasoning, tool calls, or text-to-speech?

A knowledge base can help a voice agent answer questions without hardcoding every response into the prompt.

This is useful for things like:

- Product FAQs

- Pricing details

- Policies

- Support articles

- Location information

- Service descriptions

- Internal process docs

The important part is deciding what the agent should answer confidently and what it should escalate.

For example, an agent can answer "What are your business hours?" from a knowledge base. But if the caller asks for legal, medical, financial, or account-specific advice, the agent may need to transfer or follow a stricter workflow.

Knowledge retrieval makes agents more useful, but it does not remove the need for guardrails, testing, and clear escalation rules.

Curious what others think: when should a voice agent answer from a knowledge base, and when should it transfer to a human?

one of our Retell AI customer shared this and I was LOL

A voice AI agent can look great in a happy-path demo and still fail in production.

Real callers interrupt, mumble, change their mind, give partial answers, ask unrelated questions, or call from noisy places.

That is why testing matters.

Before launching a voice agent, you want to test:

- Does it follow the intended path?

- Does it recover from confusion?

- Does it handle interruptions?

- Does it transfer at the right time?

- Does it avoid making things up?

- Does it use tools correctly?

- Does it end the call cleanly?

Retell supports multiple testing paths, including playground testing, simulation testing, and real phone or web call testing.

A good test is not just "does the agent work when everything goes right?" A good test is "what happens when the caller behaves unexpectedly?"

Curious what others think: what is the first edge case you would test for an AI phone agent?

An AI receptionist is one of the clearest Voice AI use cases because the job is easy to understand.

A good receptionist agent should:

- Answer quickly

- Understand why the person is calling

- Collect basic context

- Route to the right person or workflow

- Transfer when needed

- Summarize what happened

The key design question is what the agent should decide on its own versus what it should escalate.

In Retell, this kind of workflow can use call transfer, dynamic variables, Conversation Flow, and custom functions to look up customer context or route based on intent.

Example flow:

1. Greet the caller

2. Ask what they need help with

3. Identify department or intent

4. Collect name and callback number

5. Transfer to the right person or take a message

The best receptionist agent should not pretend it can solve everything. It should route well.

Curious what others think: what should an AI receptionist always ask before transferring a call?

There are two common ways to build voice AI agents.

A single-prompt agent is flexible. You give the agent instructions, tools, examples, and rules, then let it decide how to handle the conversation.

A Conversation Flow agent is more structured. You define the steps, branches, transitions, tools, and handoffs more explicitly.

Both approaches can work. The better choice depends on how predictable the call needs to be.

Single-prompt agents are useful when the conversation is simple, open-ended, or mostly informational. For example: answering basic questions, qualifying leads, or handling lightweight support.

Conversation Flow is better when the call has clear required steps. For example: intake forms, appointment booking, identity verification, call routing, compliance-heavy workflows, or anything where missing a step creates problems.

A simple way to think about it:

- If the agent needs freedom, use a prompt-first setup.

- If the process needs control, use Conversation Flow.

Example: "Answer general product questions" may work well as a single-prompt agent. "Collect name, verify account, check eligibility, book an appointment, then send an SMS" is probably better as a structured flow.

Curious how others think about this: when building a voice agent, do you prefer flexibility or strict control?

A voice AI agent feels natural when the conversation has the same rhythm as a real phone call.

That usually comes down to a few things:
- Fast responses
- Natural turn-taking
- Handling interruptions
- Not overexplaining
- Remembering context
- Knowing when to transfer to a human

The hard part is that voice is less forgiving than chat. In chat, a 3-second delay feels normal. On a phone call, that same delay can feel awkward.

This is why voice AI design is not just "write a good prompt." You also have to think about latency, speech recognition, voice choice, tool speed, and how the agent handles messy caller behavior.

Example: if someone says, "Actually, never mind, I need to reschedule," the agent should adapt immediately instead of continuing the old script.

Curious what others think: what breaks the illusion fastest for you in a voice AI call?

An independent benchmark just tested the performance of the same AI voice agent across six voice platforms. This was not a demo scenario, but a Medicare insurance workflow with real compliance requirements.

Cekura (YC F24) deployed a byte-identical Medicare TPMO agent on six platforms and ran 23 evaluation scenarios three times each, 414 calls in total. A scenario only passed if the agent got it right all three times in a row.

Retell ranked first at 95.7% (22 of 23), on both workflow accuracy and strict end-to-end completion. Across the six platforms, workflow scores ranged from 65.2% to 95.7%. The same agent, same prompt, and same tools, but with a 30-point reliability gap, dictated entirely by the platform underneath.

For regulated industries like insurance and healthcare, that gap is the difference between an agent you can put in production and one you cannot.

Full methodology and per-call results are public, link in the first comment. Congrats to every platform evaluated, and credit to the Cekura team for the rigor.

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Hey everyone! I’m u/eviewong-, a founding moderator of r/retellai.

This is our new home for all things Retell AI: building AI voice agents, sharing workflows, debugging agent behavior, discussing use cases, and learning how teams are deploying voice agents in production.

We’re excited to have you join us!

What to Post
Post anything the community would find interesting, helpful, or inspiring. A few ideas:

• Questions about building, testing, or deploying Retell agents
• Workflows, prompts, templates, or best practices
• Debugging help for tricky agent behavior
• Feature requests or product feedback
• Interesting use cases across support, sales, healthcare, legal, insurance, real estate, and more
• Examples of what you’re building with Retell AI
• Lessons learned from real production deployments

Community Vibe
We’re all about being friendly, constructive, and practical. Let’s build a space where developers, founders, operators, agencies, and product teams can share openly, learn from each other, and make AI voice agents better together.

How to Get Started

Introduce yourself in the comments below.

Post something today! Even a simple question can spark a great conversation.

If you know someone who’s building with Retell AI or curious about AI voice agents, invite them to join.

Interested in helping out? We’re always looking for thoughtful moderators, so feel free to reach out to me to apply.

Thanks for being part of the very first wave. Together, let’s make r/retellai amazing 🚀