Agent Analytics Review After Launch

AI Ops

A post-launch analytics layer that turns raw agent conversations into the metrics that matter, containment, escalation, resolution, drop-off, so you can actually see how the agent is performing and where to improve.

Build time 1 to 2 weeks

HMX Zone

ai agent case study

AI Ops

Verified HMX-owned case details.

Build time
1 to 2 weeks
Visual motif
Reasoning orbit
Architecture basis
Agent Analytics Review After Launch uses a bounded agent handoff layer for AI Agents. A post-launch analytics layer that turns raw agent conversations into the metrics that matter, containment, escalation, resolution, drop-off, so yo... The architecture connects agree the success metrics, conversation log warehouse, gpt-5-class intent +, and agent handoff with an explicit control path.
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outcomes

Containment seen
Clear view of what the agent handles without humans
Drop-off found
Exact points where conversations fail surfaced
Top intents
Most common reasons people reach out, ranked
Improvement loop
Each review produces concrete changes to ship

case architecture

Agent Analytics Review After Launch Architecture

Agree the success metrics
Aggregate conversations from
Conversation log warehouse
GPT-5-class intent +
Human Escalation
Agent Handoff
  1. 01Agree the success metrics

    A post-launch analytics layer that turns raw agent conversations into the metrics that matter, containment, escalation, resolution, drop-off, so yo...

  2. 02Aggregate conversations from

    Aggregate conversations from all channels with intent and sentiment tagging.

  3. 03Conversation log warehouse

    Conversation log warehouse runs the bounded conversation step for Agent Analytics Review After Launch while keeping tool use, transcripts, and escalation outcomes explicit.

  4. 04GPT-5-class intent +

    Compute the metrics and identify worst-performing intents and funnel drop-off points.

  5. 05Human Escalation

    When automation confidence is low, route the record to a manual owner with the source, stage, and last action attached.

  6. 06Agent Handoff

    Containment seen Clear view of what the agent handles without humans; Drop-off found Exact points where conversations fail surfaced; Top intents Mo...

problem and build

problem

The operating gap

After an agent goes live, teams fly blind: they don't know how many conversations the agent handled end to end, where people drop off, how often it escalated, or whether it's actually helping. Decisions get made on gut feel.

build

What gets built

An analytics layer aggregates every conversation into clear metrics: volume by channel, containment (handled without a human), escalation and handoff rate, resolution, average handle time, drop-off points, top intents, and sentiment. It surfaces these in a simple review (dashboard or scheduled report) with the worst-performing intents and funnels highlighted, so each review cycle produces concrete changes. This is reporting on the agent's behavior, not a generic BI dashboard build.

build steps

  1. 01Agree the success metrics that matter for this agent (containment, escalation, resolution, drop-off).
  2. 02Aggregate conversations from all channels with intent and sentiment tagging.
  3. 03Compute the metrics and identify worst-performing intents and funnel drop-off points.
  4. 04Present them in a scheduled review with the issues to fix called out.
  5. 05Translate findings into specific prompt, script, and routing changes each cycle.
  6. 06Track the metrics over time to confirm the agent is improving after each change.

architecture notes

Architecture layers

  • Conversation layer: Agree the success metrics that matter for this agent (containment, escalation, resolution, drop-off).
  • Reasoning layer: Aggregate conversations from all channels with intent and sentiment tagging.
  • Tools layer: Conversation log warehouse runs the bounded conversation step for Agent Analytics Review After Launch while keeping tool use, transcripts, and escalation outcomes explicit.
  • Records layer: GPT-5-class intent + sentiment tagging connects calls, messages, calendar work, or CRM writes while an analytics layer aggregates every conversation into clear metrics: volume by channel, containment (handled without a human), escalation and hando...
  • Escalation layer: Containment seen Clear view of what the agent handles without humans; Drop-off found Exact points where conversations fail surfaced; Top intents Mo...

Data flow

  1. Agree the success metrics that matter for this agent (containment, escalation, resolution, drop-off).
  2. Aggregate conversations from all channels with intent and sentiment tagging.
  3. Compute the metrics and identify worst-performing intents and funnel drop-off points.
  4. Present them in a scheduled review with the issues to fix called out.
  5. Translate findings into specific prompt, script, and routing changes each cycle.
  6. Track the metrics over time to confirm the agent is improving after each change.

Controls and fallbacks

  • After an agent goes live, teams fly blind: they don't know how many conversations the agent handled end to end, where people drop off, how often it...
  • An analytics layer aggregates every conversation into clear metrics: volume by channel, containment (handled without a human), escalation and hando...
  • When automation confidence is low, route the record to a manual owner with the source, stage, and last action attached.

Stack

  • Conversation log warehouse
  • GPT-5-class intent + sentiment tagging
  • Metrics layer (containment/escalation/resolution)
  • Scheduled report / lightweight dashboard
  • Vapi/Retell/Twilio data
  • Review cadence

research basis

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