{"id":1125,"date":"2026-07-23T05:18:25","date_gmt":"2026-07-23T05:18:25","guid":{"rendered":"https:\/\/www.plura.ai\/articles\/ai-voice-agent-real-experience"},"modified":"2026-07-23T05:18:25","modified_gmt":"2026-07-23T05:18:25","slug":"ai-voice-agent-real-experience","status":"publish","type":"post","link":"https:\/\/www.plura.ai\/articles\/ai-voice-agent-real-experience","title":{"rendered":"AI Voice Agent Real-World Experience: What Actually Works"},"content":{"rendered":"<p><em>Written by: Matt Beucler, CEO, Plura AI<\/em><\/p>\n<h2 id=\"key-takeaways\">Key lessons from real AI voice deployments<\/h2>\n<ul>\n<li>Real-world AI voice deployments succeed when latency stays under 500 ms, workflows stay narrow, interruptions are handled natively, and the platform controls its carrier stack.<sup data-disclaimer-id=\"24\" data-disclaimer-index=\"3\">3<\/sup><\/li>\n<li>Production failures most often occur at the integration layer, under real-world audio conditions, and from weak context retention rather than in the model itself.<\/li>\n<li>Narrow, high-volume workflows with 90-day measured rollouts consistently outperform broad deployments and deliver stronger containment rates.<\/li>\n<li>Owning the carrier stack enables direct caller ID, real-time DNC enforcement, native STIR\/SHAKEN, and carrier-level compliance support that third-party wrappers cannot match.<sup data-disclaimer-id=\"22\" data-disclaimer-index=\"1\">1<\/sup><\/li>\n<li>Plura AI delivers production-grade performance through its FCC-licensed audio bridging carrier and Stateful Conversation Database; <a href=\"https:\/\/www.plura.ai\/plura-webchat\" target=\"_blank\">see how Plura AI can power your next deployment<\/a>.<\/li>\n<\/ul>\n<h2>Why polished demos break in production<\/h2>\n<p>Lab conditions strip out the variables that break real deployments. Hamming&#8217;s analysis of 4M+ production calls shows that agents passing tests in development may convert at lower rates in production once real conditions are included.<sup data-disclaimer-id=\"24\" data-disclaimer-index=\"3\">3<\/sup> The gap comes from audio quality, accents, edge cases, integration failures, and adversarial caller behavior that test plans never cover.<\/p>\n<p><a href=\"https:\/\/www.destilabs.com\/blog\/ai-voice-agent-benchmark-2026\" target=\"_blank\" rel=\"noindex nofollow\">DestiLabs\u2019 2026 fleet telemetry across 10+ live deployments found a median P50 latency of 680 ms<\/a><sup data-disclaimer-id=\"24\" data-disclaimer-index=\"3\">3<\/sup>, which sits above the threshold at which callers start talking over the agent. That figure never appears in a demo. <a href=\"https:\/\/orvera.ai\/blog\/mistakes-deploying-ai-voice-agents-contact-centers\" target=\"_blank\" rel=\"noindex nofollow\">Contact-center deployments also reveal<\/a> that weak context retention forces callers to repeat information, which directly increases handle time and reduces containment rates. Once the speech recognition layer weakens under production audio conditions, every downstream step becomes less reliable.<\/p>\n<h2>Latency targets that keep callers engaged<\/h2>\n<p>Latency benchmarks set the operational bar at under 500 ms from the end of the caller\u2019s speech to the start of the agent\u2019s audible response for real-time conversational experiences. Latency under 800 ms works for most business use cases, while anything above 1,200 ms feels broken to callers. A 200 ms pause feels natural, while an 800 ms pause signals that something is wrong.<\/p>\n<p>Hitting those targets requires managing a component budget. Each component in the pipeline contributes to total latency: STT (speech-to-text) typically runs under 200 ms, the LLM (large language model) time-to-first-token can add up to 400 ms, TTS (text-to-speech) time-to-first-byte contributes another 150 ms, and network overhead accounts for roughly 100 ms. These components stack sequentially, so an optimized end-to-end budget must minimize latency at every stage, including VAD (voice activity detection) endpointing, STT, LLM, TTS, and network. Streaming TTS and starting audio playback on the first sentence provide the highest leverage for tail latency, per DestiLabs.<\/p>\n<p>Plura&#8217;s infrastructure runs on its own FCC-licensed audio bridging carrier rather than routing through a third-party CPaaS (communications platform as a service). That architectural decision removes one network hop and gives Plura direct control over audio routing, which matters when P95 latency determines whether a caller stays on the line.<\/p>\n<p><strong>Book a live demo with Plura to see production latency benchmarks on real call traffic: <a href=\"https:\/\/plura.ai\/ai-voice-demo\" target=\"_blank\" rel=\"noindex nofollow\">start here<\/a>.<\/strong><\/p>\n<h2>Narrow, high-volume workflows that actually scale<\/h2>\n<p><a href=\"https:\/\/callitdev.com\/en\/blog\/ai-voice-agents-contact-center-hybrid-model-2026\" target=\"_blank\" rel=\"noindex nofollow\">A 90-day measured rollout pattern<\/a> consistently outperforms broad deployments. Days 1-15 focus on scoping one bounded workflow and building a 100-300 conversation evaluation harness. Days 16-45 cover building, testing, and iterating. Days 46-75 pilot 5-15% of live traffic. Days 76-90 scale to 40-70% traffic share only if metrics hold against baseline. A 40-60% containment rate on the initial use case provides a strong starting benchmark before scaling.<\/p>\n<p>Organizations achieve the best results by starting with a narrow, high-volume, repeatable workflow where intent is predictable enough for the agent to resolve without human involvement, then expanding in phases. Automation should be avoided for highly emotional situations, complex non-standard workflows, and poorly defined processes. Plura&#8217;s <a href=\"https:\/\/plura.ai\/managed-workflows\" target=\"_blank\" rel=\"noindex nofollow\">managed workflows<\/a> use a no-code visual canvas that lets operators iterate conversation logic without engineering and deploy changes without redeploying the underlying AI.<\/p>\n<h2>Integrations that drive revenue, not just voice polish<\/h2>\n<p><a href=\"https:\/\/coval.ai\/blog\/voice-ai-agents-architecture-deployment-evaluation\" target=\"_blank\" rel=\"noindex nofollow\">Most production voice AI failures happen at the integration layer<\/a> rather than in the model itself. Unexpected API formats, mid-call drops, tool timeouts, or stale memory lookups create dead ends for callers. Voice quality is table stakes. CRM lookups, calendar connections, and workflow triggers determine whether a call produces a booked appointment or a dead end.<\/p>\n<p><a href=\"https:\/\/www.plura.ai\/guides\/ai-contact-centers-complete-guide\" target=\"_blank\">Solar and home services companies using AI agents with property data, energy usage estimates, and home valuations<\/a><sup data-disclaimer-id=\"24\" data-disclaimer-index=\"3\">3<\/sup> achieved 2x to 3x improvements in appointment set rates. The differentiator was not TTS quality. It was real-time data enrichment delivered during the live conversation. Plura&#8217;s <a href=\"https:\/\/plura.ai\/integrations\" target=\"_blank\" rel=\"noindex nofollow\">integrations<\/a> cover 50+ tools across CRM, calendar, payment, and data enrichment categories, with the AI reading and writing to the same Stateful Conversation Database across every channel.<\/p>\n<h2>Interruption handling that feels natural to callers<\/h2>\n<p>Barge-in recovery rates must exceed 90% in production, because rates below 75% lead to failed turn-taking when users interrupt mid-sentence or provide corrections in overlapping speech. Energy-based VAD alone is insufficient for reliable turn detection because it cannot distinguish thinking pauses from end-of-turn silence. That limitation results in agents interrupting users mid-sentence.<\/p>\n<p>Enterprise deployments that build dialog flows around interruptions, silence timeouts, and background noise from the start outperform those that treat these as edge cases. Teams define guardrails for every intent, including barge-in rules, silence policy such as reprompt versus transfer, and escalation criteria such as repeated ASR (automatic speech recognition) uncertainty or tool failure. Fallback paths need hard rules, not polite suggestions to the model.<\/p>\n<h2>Real audio conditions that stress-test your stack<\/h2>\n<p>ASR word error rate (WER) degrades under real-world noise, for example in caf\u00e9 or restaurant conditions and in car or hands-free environments. <a href=\"https:\/\/presenc.ai\/research\/voice-ai-call-agent-benchmarks-2026\" target=\"_blank\" rel=\"noindex nofollow\">WER on production calls runs approximately 4-8% on clear-line calls<\/a> and materially worse on noisy lines, accents, or technical vocabulary.<\/p>\n<p><a href=\"https:\/\/orvera.ai\/blog\/mistakes-deploying-ai-voice-agents-contact-centers\" target=\"_blank\" rel=\"noindex nofollow\">In live environments, background noise, speakerphones, weak mobile connections, overlapping speech, and inconsistent line quality<\/a> distort transcription quality and reduce confidence in intent detection. Once the first speech recognition layer weakens, every downstream step becomes less reliable. G.711 or Opus codecs at 16 kHz for speech recognition, plus echo cancellation operating within 50 ms, improve STT accuracy and avoid compounding latency in speakerphone scenarios. Plura&#8217;s carrier-grade infrastructure handles audio at the origination layer, not after the fact.<\/p>\n<p><strong>Book a live demo with Plura to test real-call audio handling against your actual call conditions: <a href=\"https:\/\/plura.ai\/ai-voice-demo\" target=\"_blank\" rel=\"noindex nofollow\">start here<\/a>.<\/strong><\/p>\n<h2>Continuous evaluation tied to business KPIs<\/h2>\n<p>Automated QA at 100% coverage is required to produce reliable containment rates, coaching decisions, and customer satisfaction scores, because manual review covers only 1-2% of interactions. <a href=\"https:\/\/orvera.ai\/blog\/ai-voice-agent-best-practices\" target=\"_blank\" rel=\"noindex nofollow\">AI voice agent performance optimization benefits from measuring outcomes at the intent level<\/a>, including resolution rate, escalation rate, repeat call rate, and average handling time per intent, rather than relying solely on aggregate averages.<\/p>\n<p>Enterprises following a structured 90-day deployment model can reduce inbound scheduling calls while maintaining high appointment confirmation and first-contact AI resolution rates. Plura runs every customer build like a CRO (conversion rate optimization) test, with iterative conversation engineering, real-call monitoring, and continuous workflow tuning. Every annual contract includes a 90-day opt-out window. Plura&#8217;s <a href=\"https:\/\/plura.ai\/business-intelligence\" target=\"_blank\" rel=\"noindex nofollow\">business intelligence<\/a> layer surfaces outcome-based metrics, not just dashboard summaries.<\/p>\n<h2>Carrier-stack control versus Twilio-based wrappers<\/h2>\n<p>Most AI voice tools are API resellers built on top of Twilio or another CPaaS.<sup data-disclaimer-id=\"25\" data-disclaimer-index=\"4\">4<\/sup> They rent the carrier layer, which means branded caller ID requires a third-party reseller, DNC (Do Not Call) scrubbing is bolted on after the fact, and STIR\/SHAKEN (the caller ID authentication framework established under the TRACED Act) is inherited from an upstream provider rather than applied at origination. Compliance infrastructure often shifts to the customer rather than sitting in the platform.<\/p>\n<p>Plura owns its FCC-licensed audio bridging carrier. The table below shows what that means operationally.<\/p>\n<table>\n<thead>\n<tr>\n<th>Criterion<\/th>\n<th>Owned Carrier Stack (Plura)<\/th>\n<th>Third-Party CPaaS Wrapper<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Branded caller ID issuance<\/td>\n<td>Direct from FCC-licensed carrier<\/td>\n<td>Requires third-party reseller<\/td>\n<\/tr>\n<tr>\n<td>Real-time DNC scrubbing<\/td>\n<td>Carrier-level enforcement<\/td>\n<td>Bolt-on after the fact<\/td>\n<\/tr>\n<tr>\n<td>STIR\/SHAKEN attestation<\/td>\n<td>Native at origination<\/td>\n<td>Inherited from upstream provider<\/td>\n<\/tr>\n<tr>\n<td>Compliance infrastructure<\/td>\n<td>SOC 2, HIPAA, ISO, TCPA, DNC built in<sup data-disclaimer-id=\"22\" data-disclaimer-index=\"1\">1<\/sup><\/td>\n<td>Customer-managed or third-party add-on<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Plura supports customer compliance across TCPA, DNC, HIPAA, SOC 2, and ISO frameworks.<sup data-disclaimer-id=\"22\" data-disclaimer-index=\"1\">1<\/sup> Customers remain responsible for their own regulatory obligations and the claims they make to their end users. Consult qualified counsel on your specific compliance posture.<\/p>\n<h3>Designing interruption handling in AI voice agents<\/h3>\n<p>Production-grade interruption handling requires three layers working together. First, VAD tuning matters because endpointing models that trigger too early cause the agent to cut off callers mid-sentence, while overly conservative settings add hundreds of milliseconds of dead air per turn, per Telnyx&#8217;s documented latency analysis. Second, barge-in recovery logic must meet the 90% threshold mentioned earlier to handle overlapping speech reliably.<sup data-disclaimer-id=\"25\" data-disclaimer-index=\"4\">4<\/sup> Third, deterministic escalation is required, because for regulated contact centers, escalation must use mandatory deterministic routing rules in the orchestration layer since negative constraints expressed as LLM instructions break under conversational pressure. Plura&#8217;s workflow engine separates deterministic routing from LLM language handling, giving operators hard guardrails on every intent boundary.<\/p>\n<h3>Missed-call recovery that preserves context<\/h3>\n<p><a href=\"https:\/\/plura.ai\/ai-voice-demo\" target=\"_blank\" rel=\"noindex nofollow\">Missed-call recovery<\/a> at scale requires more than a callback queue. <a href=\"https:\/\/autocallflow.com\/blog\/ai-voice-agents-complete-guide-autocallflow-voice-automation\" target=\"_blank\" rel=\"noindex nofollow\">Outbound voice automation improves missed-call recovery<\/a> through configurable retry and scheduling windows, automatic callback scheduling, voicemail handling, and SMS follow-up sequences. Plura&#8217;s Stateful Conversation Database means the agent that texts a lead at 9 a.m. picks up the call at noon already knowing what was said, which eliminates the re-introduction problem that kills conversion on second contact. <a href=\"https:\/\/plura.ai\/ai-voice-demo\" target=\"_blank\" rel=\"noindex nofollow\">Plura contacts leads from websites, Google Business Profiles, or ad campaigns within 60 seconds via SMS or voice call<\/a><sup data-disclaimer-id=\"24\" data-disclaimer-index=\"3\">3<\/sup>, and <a href=\"https:\/\/plura.ai\/ai-voice-demo\" target=\"_blank\" rel=\"noindex nofollow\">handles 3x to 5x call volume during peak seasons without temporary staff<\/a><sup data-disclaimer-id=\"24\" data-disclaimer-index=\"3\">3<\/sup>.<\/p>\n<h3>Enterprise AI voice agent compliance landscape<\/h3>\n<p>Enterprise compliance for <a href=\"https:\/\/plura.ai\/ai-voice-demo\" target=\"_blank\" rel=\"noindex nofollow\">AI voice agents<\/a> spans several overlapping frameworks. The FCC&#8217;s February 2024 Declaratory Ruling (FCC-24-17) <a href=\"https:\/\/thoughtly.com\/blog\/tcpa-ai-outbound-calling-compliance-checklist\" target=\"_blank\" rel=\"noindex nofollow\">confirmed that AI-generated voices qualify as &#8220;artificial or prerecorded voice&#8221; under the TCPA (47 U.S.C. \u00a7 227)<\/a><sup data-disclaimer-id=\"23\" data-disclaimer-index=\"2\">2<\/sup>, which subjects them to consent requirements regardless of how natural the voice sounds. The FCC\u2019s one-to-one consent rule, <a href=\"https:\/\/www.adotat.com\/2026\/01\/eleventh-circuit-vacates-tcpa-one-to-one-consent-rule-immediately-following-fcc-postponing-the-effective-date\/\" target=\"_blank\" rel=\"noindex nofollow\">which was scheduled to become effective January 27, 2026, was vacated by the Eleventh Circuit Court of Appeals and never took effect<\/a>. HIPAA compliance for voice AI in healthcare requires Business Associate Agreements, encryption of voice recordings and transcripts at rest and in transit, role-based access controls with audit logging for PHI, and defined data retention policies.<sup data-disclaimer-id=\"23\" data-disclaimer-index=\"2\">2<\/sup> Plura&#8217;s compliance engine supports real-time DNC scrubbing, immutable consent logging, quiet-hours rules via time-zone detection, and STIR\/SHAKEN authentication on every outbound call. Operators should consult qualified counsel on their specific obligations under applicable federal and state law.<\/p>\n<h2>Conclusion: Infrastructure separates demos from durable deployments<\/h2>\n<p>The seven production lessons above share a common thread. The gap between a polished demo and a reliable enterprise deployment is an infrastructure problem, not a model problem. Latency targets require owned carrier-grade telephony, not a CPaaS rental. Narrow workflows with 90-day measured rollouts outperform broad deployments. Interruption handling requires deterministic guardrails, not LLM suggestions. Real audio conditions degrade WER in ways that clean test sets never reveal. Compliance infrastructure built at the carrier level operates very differently from compliance bolted on after the fact.<\/p>\n<p>Plura&#8217;s FCC-licensed audio bridging carrier and Stateful Conversation Database address each of these failure modes at the infrastructure layer. <a href=\"https:\/\/plura.ai\/compare\" target=\"_blank\" rel=\"noindex nofollow\">A major insurance carrier spent 14 months and over $600K building a custom AI voice agent on Twilio before switching to Plura and deploying a more capable system in 3 weeks<\/a><sup data-disclaimer-id=\"24\" data-disclaimer-index=\"3\">3<\/sup>. The math behind that decision is available in real time.<\/p>\n<p><strong>Run your numbers through Plura&#8217;s calculator to check your ROI in real time: <a href=\"https:\/\/plura.ai\/calculator\" target=\"_blank\">calculate your ROI now<\/a>.<\/strong><\/p>\n<hr>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What is the difference between a demo AI voice agent and a production-ready one?<\/h3>\n<p>A demo AI voice agent runs under controlled conditions such as clean audio, predictable caller behavior, no concurrent load, and no live integrations. A production-ready AI voice agent handles background noise, packet loss, caller interruptions, accent variation, and API failures simultaneously while maintaining sub-800 ms response latency across thousands of concurrent calls. The infrastructure underneath the model determines production readiness. That includes owned carrier-grade telephony for audio routing, streaming ASR and TTS pipelines to hit latency targets, deterministic escalation logic for edge cases, and a stateful conversation database that preserves context across channels. Platforms built as wrappers on third-party CPaaS providers inherit the latency, compliance posture, and caller ID reputation of their upstream vendor. Platforms that own the carrier stack control those variables directly.<\/p>\n<h3>How does Plura AI handle compliance for regulated industries like healthcare and financial services?<\/h3>\n<p>Plura supports customer compliance across TCPA, DNC, HIPAA, SOC 2, ISO, and GDPR frameworks.<sup data-disclaimer-id=\"22\" data-disclaimer-index=\"1\">1<\/sup> Every outbound contact is checked against federal and state DNC registries in real time before dial. Consent records are timestamped and immutable. Quiet-hours rules enforce automatically through time-zone detection. STIR\/SHAKEN caller ID authentication runs on every outbound voice call through Plura&#8217;s FCC-licensed carrier, so attestation happens at origination rather than being inherited from an upstream provider. For healthcare deployments, Plura&#8217;s architecture supports HIPAA-aligned encryption, access controls, and audit logging for protected health information. Customers remain responsible for their own regulatory obligations, certifications, and the claims they make to their end users. Operators in regulated industries should consult qualified counsel on their specific compliance posture under applicable federal and state law.<\/p>\n<h3>Why does owning the carrier stack matter for AI voice agent performance?<\/h3>\n<p>Most AI voice platforms route calls through a third-party CPaaS like Twilio. That structure means branded caller ID requires a reseller, DNC scrubbing is added after the fact, STIR\/SHAKEN attestation is inherited from the upstream provider, and compliance infrastructure often sits outside the platform. Plura owns its FCC-licensed audio bridging carrier. Voice originates on Plura&#8217;s domestic infrastructure, which enables direct issuance of branded caller ID, carrier-level DNC enforcement, native STIR\/SHAKEN attestation at origination, and compliance support built into the platform rather than bolted on. The practical result is higher pickup rates because calls present with the company&#8217;s name rather than &#8220;Spam Likely,&#8221; lower per-minute economics because there is no CPaaS markup in the path, and a compliance posture that operates at the carrier level rather than depending on a third-party add-on.<\/p>\n<h3>What workflow design approach produces the best results for high-volume AI voice agent deployments?<\/h3>\n<p>The pattern that consistently outperforms broad deployments is a phased rollout starting with one narrow, high-volume, repeatable workflow where intent is predictable enough for the agent to resolve without human involvement. The first workflow should handle only a small number of tasks, such as greeting, qualifying, and booking a callback, so teams can measure contact rate, qualification completeness, and conversion before expanding. After the first workflow stabilizes at a strong containment rate, operators expand horizontally to adjacent low-risk use cases while writing structured outcomes back to the CRM. The standard enterprise approach follows the phased rollout model described earlier, starting with a narrow workflow, piloting on a small traffic percentage, and scaling only when metrics hold against baseline. Plura&#8217;s no-code workflow builder supports this pattern with a visual canvas that lets operators iterate conversation logic and deploy changes without engineering involvement.<\/p>\n<h3>How does Plura AI&#8217;s Stateful Conversation Database improve missed-call recovery?<\/h3>\n<p>Most AI voice platforms treat each call as an isolated event. If a lead texts at 9 a.m. and calls at noon, the voice agent has no memory of the earlier conversation. Plura&#8217;s Stateful Conversation Database keys every interaction to a customer token, whether that is a phone number, email, or ID, and persists the full conversation history across voice, SMS, RCS, and webchat. The agent that texted a lead already knows what was offered, what was accepted, what was declined, and what is still open when the call comes in. For missed-call recovery specifically, this means outbound follow-up calls carry full context from the original inbound attempt, which eliminates the re-introduction problem that reduces conversion on second contact. Plura contacts leads within 60 seconds via SMS or voice call and handles peak-season volume spikes without temporary staffing.<\/p>\n<hr data-disclaimer-divider=\"true\">\n<div data-disclaimer-footer=\"true\">\n<p data-disclaimer-id=\"22\" data-disclaimer-type=\"content_based\"><sup data-disclaimer-index=\"1\">1<\/sup> Plura AI maintains SOC 2, HIPAA, ISO, and GDPR posture as part of its platform infrastructure. References to compliance frameworks in this article describe Plura\u2019s platform capabilities and do not constitute a guarantee that any customer using Plura will themselves be compliant with applicable laws or standards. Customers remain solely responsible for their own regulatory obligations, certifications, consent management, recordkeeping, and the claims they make to their own end users. Consult qualified legal counsel for guidance specific to your use case.<\/p>\n<p data-disclaimer-id=\"23\" data-disclaimer-type=\"content_based\"><sup data-disclaimer-index=\"2\">2<\/sup> This article describes regulatory frameworks at a general level and does not constitute legal advice. Laws and regulations vary by jurisdiction, change over time, and apply differently depending on facts and circumstances. Readers should consult qualified legal counsel before making compliance decisions.<\/p>\n<p data-disclaimer-id=\"24\" data-disclaimer-type=\"content_based\"><sup data-disclaimer-index=\"3\">3<\/sup> Performance figures, customer outcomes, and industry statistics referenced in this article are drawn from cited third-party sources or Plura customer case studies. Individual results vary based on implementation, use case, industry, audience, and execution. Past or aggregate performance is not a guarantee of future results.<\/p>\n<p data-disclaimer-id=\"25\" data-disclaimer-type=\"content_based\"><sup data-disclaimer-index=\"4\">4<\/sup> References to third-party products, services, companies, or research are made for informational and comparative purposes only. Plura AI is not affiliated with, endorsed by, or sponsored by any third party named in this article unless explicitly stated. Trademarks and product names referenced remain the property of their respective owners.<\/p>\n<p data-disclaimer-id=\"21\" data-disclaimer-type=\"fixed\">This article is provided for informational purposes only and reflects Plura AI\u2019s understanding at the time of publication. Product capabilities, integrations, and specifications are subject to change. For the most current information, visit plura.ai.<\/p>\n<p data-disclaimer-id=\"27\" data-disclaimer-type=\"fixed\">This article was produced with the assistance of AI tools and reviewed by Plura AI prior to publication.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Real deployments reveal what makes AI voice agents succeed or fail. See production lessons and how Plura AI delivers results at scale.<\/p>\n","protected":false},"author":106,"featured_media":1124,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[8],"tags":[],"class_list":["post-1125","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-voice-agents"],"_links":{"self":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/posts\/1125","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/comments?post=1125"}],"version-history":[{"count":0,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/posts\/1125\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/media\/1124"}],"wp:attachment":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/media?parent=1125"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/categories?post=1125"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/tags?post=1125"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}