Agent Analytics Dashboard

High AI Agent system

An operations view built specifically for conversational agents: containment rate, escalation rate, average handle time, latency, opt-outs, booking conversion, and cost per resolved conversation. The single pane that tells you whether the agent is actually working, not just running.

Timeline 1-3 weeks

HMX Zone

ai agent system

High Agents system

Verified HMX-owned system details.

Timeline
1-3 weeks
Visual motif
Reasoning orbit
Live datum
A message is classified, noted, then handed to a human when needed.
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operating facts

Outcome

Operators can see agent health and ROI at a glance and catch regressions before customers do.

Main risk

A vanity metric (raw call count) hides the metrics that matter (containment quality, escalation correctness).

Prevention

Anchor the dashboard on outcome metrics, separate 'contained' from 'abandoned', and tie cost to resolved conversations.

Fallback

If provider data is incomplete, fall back to transcript-derived metrics and clearly mark which numbers are estimated.

system architecture

Agent Analytics Dashboard Architecture

the agent KPIs that matter
Pull events from the
Vapi
Retell
Human Escalation
Agent Handoff
  1. 01the agent KPIs that matter

    An operations view built specifically for conversational agents: containment rate, escalation rate, average handle time, latency, opt-outs, booking...

  2. 02Pull events from the

    Pull events from the voice/chat providers and the CRM into a single analytics store

  3. 03Vapi

    Vapi runs the bounded conversation step for Agent Analytics Dashboard while keeping tool use, transcripts, and escalation outcomes explicit.

  4. 04Retell

    Build the views and trend lines, segmented by channel, intent, and time of day

  5. 05Human Escalation

    If provider data is incomplete, fall back to transcript-derived metrics and clearly mark which numbers are estimated.

  6. 06Agent Handoff

    Operators can see agent health and ROI at a glance and catch regressions before customers do.

how it is built

  1. 01Define the agent KPIs that matter (containment, escalation, latency, conversion, opt-out, cost per convo)
  2. 02Pull events from the voice/chat providers and the CRM into a single analytics store
  3. 03Build the views and trend lines, segmented by channel, intent, and time of day
  4. 04Add threshold alerts so a containment drop or latency spike surfaces fast

architecture notes

Architecture overview

Agent Analytics Dashboard uses a bounded agent handoff layer for AI Agents. An operations view built specifically for conversational agents: containment rate, escalation rate, average handle time, latency, opt-outs, booking... The architecture connects the agent kpis that matter, vapi, retell, and agent handoff with an explicit control path.

  • Conversation layer: Define the agent KPIs that matter (containment, escalation, latency, conversion, opt-out, cost per convo)
  • Reasoning layer: Pull events from the voice/chat providers and the CRM into a single analytics store
  • Tools layer: Vapi runs the bounded conversation step for Agent Analytics Dashboard while keeping tool use, transcripts, and escalation outcomes explicit.
  • Records layer: Retell connects calls, messages, calendar work, or CRM writes while anchor the dashboard on outcome metrics, separate 'contained' from 'abandoned', and tie cost to resolved conversations.
  • Escalation layer: Operators can see agent health and ROI at a glance and catch regressions before customers do.

Data flow

  1. Define the agent KPIs that matter (containment, escalation, latency, conversion, opt-out, cost per convo)
  2. Pull events from the voice/chat providers and the CRM into a single analytics store
  3. Build the views and trend lines, segmented by channel, intent, and time of day
  4. Add threshold alerts so a containment drop or latency spike surfaces fast

Controls and fallbacks

  • A vanity metric (raw call count) hides the metrics that matter (containment quality, escalation correctness).
  • Anchor the dashboard on outcome metrics, separate 'contained' from 'abandoned', and tie cost to resolved conversations.
  • If provider data is incomplete, fall back to transcript-derived metrics and clearly mark which numbers are estimated.

Tools

  • Vapi
  • Retell
  • Bland
  • GoHighLevel
  • OpenAI

research basis

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