How are hosted services different from on-premise contact centers?

On-premise puts the PBX, ACD, and recording on the customer's hardware. Hosted services put all of it in the vendor's cloud; the customer connects via SIP trunks, WebRTC, and APIs. Hosted is now the dominant deployment model.

Does FutureAGI integrate with hosted contact center services?

FutureAGI does not replace the CCaaS platform. It evaluates the AI modules running inside it — voice agents, chat agents, summarization, intent classification — using ASRAccuracy, ConversationResolution, and CustomerAgentConversationQuality.

What Are Contact Center Hosted Services? FutureAGI Guide (2026)

What Are Contact Center Hosted Services?

Contact center hosted services are CCaaS deployments where the vendor runs the contact-center stack — ACD, IVR, recording, WFM, analytics — in their cloud, and customers consume it as a managed service rather than installing on-premise hardware. The major vendors are Genesys Cloud CX, Five9, NICE CXone, Talkdesk, Amazon Connect, and Cisco Webex Contact Center. AI features increasingly ship inside these hosted platforms — voice IVRs, chat agents, auto-summarization — and the LLM layer requires independent evaluation. FutureAGI evaluates that AI layer; the CCaaS platform handles the telephony.

Why It Matters in Production LLM and Agent Systems

Hosted contact-center services have moved from minority deployments in 2015 to dominant ones by 2026 because they remove most of the operational burden — no PBX rack, no telephony specialist on staff, no quarterly patch cycle on physical gear. Per-seat pricing replaces capex. Channel additions, IVR changes, and queue updates ship without a maintenance window. For mid-market enterprises, hosted is now the default.

The AI layer is where things get interesting. Every major CCaaS vendor has shipped AI modules inside their platform: Genesys “AI Experience,” Five9 “Genius AI,” NICE “Enlighten AI,” Talkdesk “Copilot,” Amazon Connect with Bedrock. These run LLMs against customer interactions to produce summaries, route calls, deflect FAQs, and grade quality. The question every platform owner faces in 2026 is: do these in-suite AI features actually work on my traffic, or just on the vendor’s demo data?

The pain shows up when AI claims meet production. A 90% summary-faithfulness claim from the vendor turns into a 73% measurement on the customer’s real transcripts. An “auto-resolution” feature flags 18% of unresolved calls as resolved. A voice-IVR deflection number quotes a 35% rate but customer-side measurement shows 22% — and the deflection counted some abandoned calls as deflected. Without independent evaluation, the platform owner has no defense against optimistic vendor numbers and no signal when the AI regresses.

How FutureAGI Handles Contact Center Hosted Services

FutureAGI’s approach is to evaluate the AI layer of hosted services using the same Dataset and fi.evals patterns that work on bespoke agent stacks. The pattern: export call logs and transcripts from the CCaaS platform via its API, load them into a FutureAGI Dataset with the vendor’s AI-generated outputs (summary, sentiment, intent, resolution flag) as columns, and attach ConversationResolution, CustomerAgentConversationQuality, Faithfulness, and ASRAccuracy as evaluators. The result is an independent measurement that can be compared to vendor-reported numbers.

A concrete example: an insurance carrier on Genesys Cloud CX uses Genesys “AI Experience” for auto-summary and intent classification. The team exports 5,000 daily calls into a FutureAGI Dataset, attaches Faithfulness to score the auto-summaries against transcripts, and finds 0.79 mean faithfulness — below the vendor’s claimed 0.91. The drill-down shows summaries omit policy numbers in 14% of cases. The carrier files a ticket with the vendor and adds a post-summary regex check via Agent Command Center’s post-guardrail to catch the omission until the upstream model is fixed. The CCaaS platform owns the AI; FutureAGI owns the verification.

For voice IVRs running inside hosted CCaaS, traceAI-livekit instruments the agent runtime when it is built on LiveKit, and ASRAccuracy plus AudioQualityEvaluator score the audio path. When the IVR is built into the CCaaS platform itself (not on LiveKit), FutureAGI runs offline against the call recordings.

How to Measure or Detect It

Hosted-service AI evaluation depends on getting the right exports out of the platform:

Vendor-claimed vs measured accuracy — independent measurement of every AI feature the CCaaS suite advertises.
Faithfulness — score auto-summary fidelity against the source transcript.
ConversationResolution — score auto-resolution flags against ground-truth review.
ASRAccuracy — sample voice-IVR transcripts against reference; track per language.
CustomerAgentConversationQuality — composite score for AI-handled sessions.
AI feature uptime / fallback rate — how often the in-suite AI failed open and routed to a human; high rates hide upstream model issues.

from fi.evals import Faithfulness, ConversationResolution

faith = Faithfulness()
resolution = ConversationResolution()

# Score every CCaaS auto-summary against the source transcript.
result = faith.evaluate(
    output=auto_summary,
    context=transcript_text,
)
print(result.score, result.reason)

Common Mistakes

Trusting the vendor benchmark. Re-measure on your traffic; vendor numbers come from their evaluation set, not yours.
No export pipeline for transcripts and AI outputs. If you cannot export, you cannot evaluate; require API export at procurement.
Treating CCaaS AI as a black box. Even if you cannot change the model, you can wrap a post-guardrail to catch its known failures.
One global accuracy number for AI features. Slice by language, intent, and tenant; degradation hides in segments.
Skipping ASR audit. Voice-IVR AI inherits all ASR errors; if the WER is bad, every downstream metric is meaningless.