AI Phone Agent News: Incredible Latest Updates You Can’t Miss

Updated: June 29, 2026 | Reading time: 18 min | Category: AI & Customer Experience

AI phone agents are no longer a future technology. They are answering calls today at Home Depot, wiping out contact center labor costs by $80 billion, and sparking fierce debate about job displacement, data privacy, and the limits of autonomous AI — all in the same week.

Quick Stats at a Glance

Metric	Figure	Source
Contact center labor costs reduced by conversational AI	$80 billion	Gartner, 2026
Cost per AI-handled call vs. human agent	$0.40 vs. $7–$12	Retell AI, 2026
AI agent software spend growth YoY	+139% (to $206.5B)	Gartner, 2026
L1–L2 support handled by AI in top deployments	77%	AssemblyAI, 2026

AI Phone Agent News Today: What’s Happening Right Now

Not long ago, calling a company’s support line felt like an endurance sport. You pressed 1, then 2, then 4, got transferred, re-explained your problem, and listened to the same jazz loop for twenty-three minutes. If you were lucky, a tired human finally picked up. If you were unlucky, you got disconnected.

Those days are ending — fast.

AI phone agents — intelligent voice systems powered by large language models (LLMs), real-time speech recognition, and natural language processing (NLP) — are replacing the clunky interactive voice response (IVR) menus that defined phone support for decades. They listen like a person. They understand context. They act, not just respond. And businesses of every size are taking notice.

“Think of the last time you called to check an order status at 11 PM on a Sunday. The old system put you on hold until Monday. The new system picks up on the first ring, finds your order, gives you a real-time update, and ends the call in under 90 seconds. That’s not a chatbot. That’s an AI phone agent.”

The news AI phone agent cycle is moving faster than most businesses can absorb. In June 2026 alone, the AI phone-call agent market crossed a decisive threshold — the race is no longer about conference demos. It is a full platform contest, with OpenAI, Google, and Microsoft shipping enterprise-grade infrastructure that organizations are deploying at scale right now.

AI agent software spending is forecast to reach $206.5 billion in 2026, up 139% from $86.4 billion in 2025 — the fastest-growing slice of enterprise software. Gartner forecasts conversational AI will reduce global contact center labor costs by $80 billion this year. At $0.40 per AI-handled call versus $7–$12 per human-agent call, the economics are no longer debatable. This is the daily reality of AI phone agent new in mid-2026: the technology is not arriving — it has arrived.

If you also follow AI Voice Agent News, you’ll notice that many of the latest AI phone agent updates are helping businesses handle customer calls more effectively, faster, and more naturally.

AI Phone Agent News on Twitter/X: The Conversation Happening Online

If you follow news AI phone agent on Twitter — now officially X — the debate is loud, opinionated, and surprisingly divided. On one side, technology leaders are posting real deployment results and ROI figures. On the other hand, frustrated consumers are sharing viral threads about AI calls that went wrong, agents that misunderstood their accent, or systems that refused to transfer them to a human.

In 2026, AI agents on X now maintain real-time social listening, monitoring trending conversations about brand experience and flagging AI-related customer complaints as they surface. The dominant voices on news AI phone agent on X include researchers posting findings from MIT CSAIL, founders sharing latency benchmarks, and CX leaders debating the right balance between automation and human touch.

Key accounts to follow include @OpenAI, @AnthropicAI, and @GoogleDeepMind for platform-level announcements, alongside independent researchers who post unfiltered deployment realities. Three representative threads from the past week illustrate the split in the conversation:

• @cxleader: “Home Depot’s AI phone agent pilot shows 4× faster resolution in 50 stores. The numbers are real. The question isn’t if — it’s when for your business.”
• @AIskeptic: “Tried 3 AI phone agents this week. Two couldn’t handle an accent. One tried to book me an appointment for a product I already have. Still waiting for the revolution.”
• @ResearcherMIT: “MIT CSAIL’s EnCompass framework adds systematic backtracking to AI agents. Fewer production failures. This is the kind of unglamorous work that actually makes agents deployable.”

The signal in the X noise is consistent: businesses deploying AI phone agents in narrow, well-defined use cases are reporting strong results. Those going broad without governance frameworks are quietly walking them back. Consequently, the most useful thing to track on social media right now is not the hype — it is the rollbacks.

Companies Replacing Workers With AI: The Honest Picture

The headline “companies replacing workers with AI” generates more heat than light. The reality in 2026 is more nuanced — and more instructive.

Cloudflare announced plans to cut 1,100 workers in May 2026, framing it as adapting to the “agentic AI era.” Meta announced plans to slash 10% of its workforce the same month, pivoting resources to AI. Amazon has shed tens of thousands of corporate positions since late 2025, with AI cited as a key enabler of leaner operations. In the contact center world specifically, 37% of business leaders anticipate replacing human workers with AI by end of 2026, per HRDive.

But the celebrated case studies are not holding up. Klarna famously claimed its AI chatbot did the work of 700 customer service agents — then quietly began rehiring human agents when the promised returns did not materialize. 55% of employers who made AI-driven cuts now report regretting that decision, according to HR Executive. Forrester’s 2026 Future of Work report estimates that roughly half of AI-attributed layoffs will be quietly reversed.

“By 2027, Gartner predicts that 50% of companies that cut customer service staff due to AI will rehire for similar functions — often under new job titles. The correction is already underway.” — Capitol Technology University analysis, May 2026

The more accurate framing: AI is displacing entry-level, high-volume, repetitive tasks — not whole departments. The workers who thrive are those who learn to supervise, audit, and refine AI agents. Only 9% of executives say they would replace their entire workforce with AI tools. The other 91% are building hybrid models — AI handling volume, humans handling nuance.

McKinsey’s Anthropic Economic Index puts consumer AI use at roughly 52% augmentation versus 45% automation, concentrated in higher-skill tasks. Business API deployments skew toward automation at around 75%. So the answer to “is AI replacing or augmenting workers?” depends entirely on who is in control of the workflow — a person or an automated system.

Does Home Depot Use AI Customer Service? Yes — and the Results Are Striking

If you have ever wondered whether Home Depot uses AI customer service, the answer as of April 22, 2026, is a definitive yes. The world’s largest home improvement retailer launched AI-powered phone agents built on Google Cloud’s Gemini Enterprise for Customer Experience platform, covering all incoming calls to U.S. stores.

The early results from a 50-store pilot are hard to argue with. The AI agent understands caller intent in under 10 seconds — getting customers to a resolution four times faster than the traditional phone menu system. When you call a Home Depot store now, instead of “press 1 for paint, press 2 for lumber,” the system simply asks: “How can I help?” The agent then routes, acts, or resolves — often without any further steps.

“Nobody likes getting trapped in a phone menu. When a customer calls us, they just want to get help as quickly as possible. Using customer service AI voice agents, we’re moving away from ‘Please listen to these options’ and toward ‘how can I help?'” — Jordan Broggi, EVP of Customer Experience, The Home Depot

The system’s capabilities go well beyond basic routing. The agent can check order status, confirm product availability, initiate service requests, send product links directly to a pre-filled cart, and even build a complete project shopping list based on a customer’s verbal description — all in real time. Multilingual support means customers can call in any language. Pilot store associates reported higher job satisfaction because routine calls were handled by the AI, freeing them to focus on in-person shoppers.

Home Depot plans to expand the system to all U.S. stores by end of 2026. Its rival Lowe’s deployed a similar AI-powered Intelligent Virtual Agent (IVA) in February 2026, focusing specifically on clearing in-store phone distractions so associates can focus on shoppers. Together, these two retail giants signal something important: AI phone agents have moved from pilot programs to core customer experience infrastructure in major enterprise environments.

One important nuance: more than 80% of customers still prefer human help when given the choice, according to Metrigy’s Customer Experience Optimization 2025–26 study. Home Depot’s system keeps a human-in-the-loop available at all times — there are no dead ends. That design choice, more than the AI technology itself, is what is driving the satisfaction numbers.

AI Agents Failing: The Problems No One’s Advertising

Not every deployment is a Home Depot success story. AI agents are failing in real-world environments in ways that are well-documented, often expensive, and sometimes embarrassing. The honest picture matters as much as the wins.

Up to 79% of US corporate executives have some type of AI agent in development, but Gartner predicts 40% of these projects will implode due to poor risk controls. The MIT NANDA research covering 300 public AI deployments found that 95% of generative AI pilots fail to deliver measurable P&L impact — not because the models are incapable, but because organizations did not understand how to design workflows that captured AI’s benefits while managing its failure modes.

In customer service specifically, AI hallucination is the most common and costly failure type. An AI agent at one e-commerce brand hallucinated a shippinag address and directed a customer to send three devices to a truck stop. Another told a customer a replacement product had already shipped — it had not. The customer waited, then followed up, then escalated. Ungrounded LLMs hallucinate in 15–30% of customer service responses, depending on query complexity, according to a Stanford HAI study. On the customer side, Zendesk’s CX Trends 2026 report found that 85% of customer service leaders say a single unresolved issue is enough to lose a customer. When an AI hallucinates, the ticket does not just stay open — the customer’s trust closes.

The four most common AI phone agent failure modes in 2026:

1. Hallucination under ambiguity. When callers ask vague questions without an order number or account detail, the agent fills the gap with plausible — but wrong — information rather than admitting ignorance.

2. Cascade failures in multi-step workflows. Each tool call in a chained workflow introduces independent error. A 95%-accurate component can produce a 70%-accurate outcome when several steps are linked. Genvocal’s 2026 research documents cases where agents restarted servers while three critical services were handling peak traffic — a “cascade the agent was never designed to model.”

3. Escalation collapse under load. Generative-only systems lack a hard “I don’t know” boundary. They generate convincing answers indefinitely, even after their grounding data runs out. This is how a customer ends up receiving confidently-stated wrong information about a policy that does not exist.

4. AI agent sprawl. Companies like DaVita report employees creating over 10,000 AI agents, each consuming tokens and introducing cybersecurity risk without central governance. This entirely self-inflicted issue — called “AI agent sprawl” — is increasingly cited by enterprise consultancies as the unintended consequence of giving employees no-code agent-building tools without governance guardrails.

The consistent fix across all four failure modes is Retrieval-Augmented Generation (RAG) with strict knowledge-base grounding, programmable escalation triggers, and real-time call monitoring. Platforms like Retell AI now ship with “Retell Guardrails” — a programmable layer that blocks specific topics, detects PII (personally identifiable information) in real time, and redacts it from logs before it can surface downstream.

AI Assistants Are Blabbing: The Privacy Risk Hiding in Plain Sight

One of the most under-reported stories in the news about AI phone agents is what happens to the data shared during a call. AI assistants are blabbing — not necessarily through malice, but through misconfigured backends, third-party trackers, and a persistent gap between what privacy policies say and what platforms actually do.

In February 2026, a popular AI chat app exposed 300 million private messages from 25 million users due to a misconfigured Google Firebase backend. The exposed conversations included discussions of illegal activities, mental health crises, and requests for self-harm assistance — the kinds of things users say to what they assume is a private system. Users treat AI chats like private journals or therapists. When those conversations are stored insecurely, they become an attractive target for attackers. Even without explicit names attached, long chat histories can reveal identities, locations, workplaces, and mental health struggles.

In March 2026, a Check Point Research discovery revealed a hidden outbound channel in ChatGPT’s code execution runtime that could silently exfiltrate user messages, uploaded files, and sensitive content via a single malicious prompt. A separate investigation published as LeakyLM found that if you used Perplexity, Grok, Claude, or ChatGPT while logged in before April 2026, your conversation URLs and potentially identifying data — including email hashes and advertising cookies — were transmitted to third-party networks including Meta, Google, and TikTok, regardless of whether you used private or incognito mode.

For businesses deploying AI phone agents, this has direct compliance consequences. Any platform handling healthcare calls must meet HIPAA standards. Financial services calls fall under SEC and FINRA data governance rules. Regulators in the EU and California now require AI phone agents to identify themselves at the start of every call and offer a human-transfer option. PII redaction and chain-of-custody call recording are no longer optional features — they are baseline requirements for any serious deployment.

The practical implication: before deploying any AI phone agent, your security team needs to verify three things. First, that customer audio and transcripts never leave your controlled environment unencrypted. Second, that the platform has a published, auditable data retention policy. Third, that PII redaction happens in real time during the call — not after the fact in post-call processing, when the data has already touched external infrastructure.

MIT AI Agents Research: What the Science Actually Says

While vendors publish ROI case studies, MIT AI agents researchers are doing something more valuable: stress-testing the underlying assumptions. The findings from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are both encouraging and sobering — and every business deploying voice AI should understand them.

In early 2026, MIT CSAIL researchers and startup Asari AI introduced EnCompass, a software framework that allows developers to add systematic search and backtracking to AI agent programs without rewriting large portions of code. The framework was presented at NeurIPS 2025 and directly addresses one of the core failure modes described above: when an AI agent takes a wrong step in a multi-turn workflow, EnCompass lets it retrace and recover rather than cascade into failure. This is the kind of unglamorous engineering that makes agents trustworthy at scale, not just impressive in demos.

Separately, MIT CSAIL’s 2025 AI Agent Index catalogued and evaluated hundreds of deployed AI agents, focusing on transparency, consistency, and behavioral accountability. The index found a striking absence of standardized governance frameworks across most commercial deployments. Its conclusion: transparency in AI development is not optional — it is foundational to building systems that can be trusted at scale.

In April 2026, MIT CSAIL hosted an Agentic AI Hackathon, explicitly focused on building agents that can plan, use tools, make decisions, and handle what goes wrong in production — a signal that the research community is now engineering for failure recovery, not just capability demonstration. The $5,000 grand prize went to a project judged on three questions: Does it actually work? Is the task worth doing? Would you use it again next week? — a refreshing counter to the industry habit of judging agents on demo performance alone.

The policy arm of MIT has also weighed in. A 2025 MIT CSAIL policy submission to the US government argued that AI leadership requires not just investment but algorithmic innovation — and that agentic AI systems must integrate real-world awareness, common-sense reasoning, and physical intelligence to move beyond statistical pattern matching. For businesses: the gap between what vendors promise and what MIT researchers verify is still real, but narrowing fast.

Futurism AI Agents: The Stories Making Headlines You Cannot Ignore

Futurism’s AI agents coverage has become one of the most-cited sources in the industry precisely because it covers both the wins and the spectacular misfires without pulling punches. Several recent Futurism stories carry direct implications for any business evaluating AI phone agents in 2026.

In May 2026, Futurism reported that bosses were alarmed to discover their companies had been overrun by out-of-control AI agents. The Ben & Jerry’s parent company Magnum Ice Cream found itself with redundant agents at every tier of its organizational structure. DaVita’s CIO told the Wall Street Journal that employees had created over 10,000 agents — generating cybersecurity risks, token cost overruns, and governance nightmares. FICO’s chief customer officer bragged that its 3,500 employees were creating dozens of new AI agents every day “at every tier of the hierarchical structure.” This phenomenon — called “AI agent sprawl” — is increasingly cited by enterprise consultancies as the unintended consequence of giving employees no-code agent-building tools without accompanying governance policies.

Also in May, Futurism covered the rise of AI debt collection agents — an ethically thorny use case where autonomous bots are calling debtors, sometimes on already-settled debts, and refusing to connect them to a human. One man in Seattle described being trapped in a conversation with an AI debt collector called Eve that would not escalate, would not acknowledge the debt was settled, and eventually engaged in quasi-roleplay before finally routing him to a human — who confirmed the debt had been cleared.

In June 2026, Futurism reported that more than half of Americans admit to actively trying to circumvent AI customer service agents, with the most common tactic being yelling “human!” or “person!” at the phone — and 17% resorting to swearing. A Parloa survey found that only 13.6% of consumers trust an AI to handle complex service requests today, and 30.4% have no trust at all. More than half of respondents said they were only willing to give an automated system three minutes before walking away.

A paper cited by Futurism in January 2026 argued mathematically that LLMs are incapable of carrying out agentic tasks beyond a certain complexity threshold — and that threshold is “pretty low.” Written by Vishal Sikka, a former CTO at SAP who studied under the pioneer who coined the term “artificial intelligence,” the paper found that “there is no way they can be reliable” beyond narrow, well-scoped tasks. OpenAI’s own scientists have acknowledged that model accuracy will “never” reach 100%. The implication for businesses is clear: AI phone agents should be scoped narrowly, governed tightly, and always paired with a reliable human escalation path.

What Exactly Is an AI Phone Agent?

An AI phone agent is a conversational AI system that combines three core technologies: real-time speech recognition, a language model for reasoning and generating responses, and text-to-speech (TTS) voice synthesis to talk back naturally. The result is a system that can hold a full, multi-turn phone conversation without a human on the line.

Unlike the old “press 1 for billing” phone trees, these agents understand full sentences. They figure out caller intent, pull up customer records from your CRM, complete actions, and — when a situation genuinely needs a human — execute a warm call transfer with a full transcript attached. No re-explaining. No hold music. Just an informed handoff.

The technology has matured dramatically. Leading platforms now respond in under 600 milliseconds — fast enough that the conversation feels genuinely natural, not robotic. Retell AI, for instance, maintained 580–620ms response latency across 80 stress-tested calls — including mid-sentence interruptions — without losing context once.

The Real Business Benefits — By the Numbers

Beyond the hype, the results businesses are seeing are concrete. Aircall customers using AI-powered call routing saw a 23% uplift in service level, with one customer cutting average human response time from 29 hours in 2025 to just 12 hours by January 2026. Enterprise deployments with Retell AI report an 80% reduction in call handling costs in healthcare and 85% containment rates in contact centers.

McKinsey estimates generative AI can raise customer-care productivity by 30–45% of current function costs. A Forrester study found AI-led service increased customer retention by 14%. For small businesses: 62% of inbound SMB calls go unanswered during peak hours. One business reported that 54% of their calls are now handled entirely by their AI agent, freeing human staff for the complex, empathy-driven work only they can do.

“If 2025 was the year AI phone agents proved they could work, 2026 is the year businesses proved they can’t afford not to use them.”

Top AI Phone Agent Platforms to Know in 2026

The vendor landscape has matured quickly. Here are six platforms leading the conversation, each suited to different team sizes, technical depths, and compliance requirements.

Retell AI — Developer-first, API-first platform with ~600ms latency, a drag-and-drop builder, and real-time Guardrails for PII redaction. Strong fit for SaaS, healthcare scheduling, and logistics. Enterprise deployments have achieved 85% containment rates and 90 NPS at scale.

Aircall AI Virtual Agent — No-code voice automation built for SMBs and mid-market. Natively CRM-integrated. Customers report a 23% uplift in service level and human response times cut from 29 hours to 12 hours in a single quarter.

Google CCAI — Chirp + Gemini 2.0 stack powering major enterprise deployments including Home Depot. Fewer than 50 lines of code to get a voice agent live. Best for organizations already running on Google Cloud infrastructure.

ElevenLabs — Premium voice quality, the widest accent library, and privacy-first VPC deployment. Ideal for global brands where voice identity and brand consistency matter most. Custom knowledge base integration available.

PolyAI — Enterprise-grade, contact-center hardened. Developer ADK launched April 2026 for hands-on customization alongside its managed service offering. Documented 85% containment rates in the most demanding contact center environments.

Chatbase — Omnichannel AI support merging phone, chat, and messaging into one platform. Reports 3× revenue lift, 68% ticket reduction, and doubled conversion rates in e-commerce deployments.

Where AI Phone Agents Deliver the Most Value: Key Use Cases

Not every call needs an AI agent. The highest-value scenarios share a clear pattern: high-volume, time-sensitive, and repetitive interactions that drain human teams fastest and cost the most to staff. Smart businesses target these workflows first — and the ROI shows up quickly.

24/7 inbound customer support. An AI phone agent answers on the first ring, at any hour. A logistics firm uses one to resolve order tracking queries at 2 AM, freeing its daytime staff entirely for complex escalations. Off-hours labor costs drop by over 60% in documented deployments.

Lead qualification and speed-to-lead. Speed-to-lead is everything in sales. AI voice agents engage inbound leads within seconds of first contact — qualifying them against defined criteria and booking appointments for a human closer before a competitor even picks up the phone.

Appointment scheduling. Healthcare providers, law firms, and service businesses use AI agents to handle appointment scheduling end-to-end. The agent confirms availability, books the slot, and syncs to the calendar in under two seconds — without any human involvement.

Intelligent IVR replacement. Instead of “Press 1 for Sales,” the agent asks: “How can I help you today?” It routes based on the actual spoken answer — connecting callers to the right specialist, not a general department, and reducing transfer rates significantly. Anker replaced its basic IVR with an AI agent and now delivers human-quality voice conversations at enterprise scale.

Automated ticket creation. B2B SaaS teams deploy voice agents to collect bug details directly over the phone, auto-categorizing and routing them into platforms like Zendesk or Salesforce. This saves service reps up to 1.5 hours per day in post-call paperwork.

How to Deploy an AI Phone Agent: A Step-by-Step Guide

Ready to move from evaluation to deployment? Industry guidance consistently points to six stages. The biggest predictor of success is starting narrow — one use case, one measurable metric, one contained pilot.

Step 1: Define your use case precisely. Pick one high-friction, high-volume call workflow — appointment scheduling, order status, or FAQ resolution. Define your success metric up front: first contact resolution (FCR), average handle time (AHT), or containment rate. Narrow scope always beats automating everything at once.

Step 2: Choose the right platform. Match the platform to your team’s technical depth. Developer-heavy teams should evaluate Retell AI or ElevenLabs. Ops-led teams without engineers should explore Aircall’s no-code AI Virtual Agent. Enterprise contact centers should look at PolyAI or Google CCAI.

Step 3: Design the conversation flow with RAG grounding. Map your greeting, intent detection, data collection, resolution paths, and escalation triggers. Use Retrieval-Augmented Generation (RAG) to constrain the AI to your approved knowledge base. This prevents AI hallucination in customer-facing contexts — if the answer is not in your knowledge base, the agent escalates rather than invents.

Step 4: Integrate with your CRM and back-end systems. Connect the agent to Salesforce, HubSpot, or your ticketing platform. A well-integrated agent pulls up the customer’s full history before saying a word, delivering personalized responses from the very first sentence.

Step 5: Test rigorously before going live. Run at least 50–100 test calls covering edge cases: callers who interrupt mid-sentence, go off-script, provide ambiguous answers, or ask to be transferred. Measure response latency, containment rate, hallucination rate, and escalation accuracy before opening to live traffic.

Step 6: Deploy gradually, then optimize with data. Start with a subset of call volume. Monitor FCR, AHT, and CSAT (customer satisfaction) scores weekly. Use the platform’s call analytics and debug console to identify where callers drop off or escalate unnecessarily, then refine the flow. Track your hallucination rate as a first-class metric alongside CSAT — not an afterthought.

What to Watch: The AI Phone Agent Story Is Far From Over

The industry is not slowing down. OpenAI’s reported AI-first smartphone — built with Qualcomm and MediaTek designing the chip — targets mass production by 2028 at 300–400 million annual units. Qualcomm CEO Cristiano Amon has made the vision explicit throughout 2026: AI agents will replace the mobile OS and apps as the primary interaction layer on smartphones, and hardware must be designed from scratch to support it.

Meanwhile, 60% of Fortune 500 voice AI deployments already involve at least two vendors, according to a June 2026 Gartner survey. The integration layer — provided by platforms like Genesys, NICE CXone, or Amazon Connect — is increasingly the real differentiator. Compliance, governance, and auditability are no longer differentiators; they are table stakes that every serious vendor delivers on day one.

For businesses still on the fence, the window to act as an early mover is narrowing. Businesses that deploy these tools now set the customer experience standard. The ones that wait will be playing catch-up against competitors who already answer every call in under a second, around the clock, at a fraction of the cost.

Final Thoughts: The Call Has Already Been Answered

The era of rigid IVR menus and 23-minute hold times is officially over. Today’s AI phone agents combine natural language understanding (NLU), real-time voice synthesis, CRM integration, and intelligent call escalation into a system that works harder, faster, and cheaper than any traditional contact center setup.

But the cautionary notes from MIT, Futurism, and the broader research community are equally real: agent sprawl, hallucination risk, data privacy exposure, and misaligned scope can turn a promising deployment into a costly rollback. The businesses winning with AI phone agent news right now are the ones treating them as narrow, governed, human-in-the-loop infrastructure — not magic boxes.

Whether you run a five-person clinic, a regional logistics company, or a global enterprise contact center, the approach is the same: start with one high-friction call workflow, define your metrics, govern tightly, and keep a human always one option away. The results will make the next step obvious.

Ready to Explore AI Phone Agents for Your Business?

Start with platforms like Aircall, Retell AI, or PolyAI — each offers free trials or demos so you can see live call performance before committing. Compare features, test latency, and measure what matters: your containment rate, your CSAT score, and your hallucination rate. The call is already ringing. The question is whether your business picks up.

Frequently Asked Questions About AI Phone Agents