AI Agent Monitoring
What is AI agent monitoring?
It differs from uptime and application monitoring, which tell you the system responded.
Every dashboard you own
milliseconds
Success
The actual conversation
A confident, wrong answer
Failed
An agent can respond in 400 milliseconds with a confident, wrong answer and every dashboard you own will call that a success.
Agent failures do not
throw errors
Four things happen to each conversation, starting the moment it closes.
1
Capture every session, with no sampling
Each session is analysed when it ends:
Full turn-by-turn transcript
Tool calls
Retrievals
Metadata
Timing of every turn, for voice agents
Sampling is fine for latency, which is common. It is useless for quality, because the failures that cost the most are rare. A two percent sample misses almost all of them.
2
Detect failures the moment a session ends
Each session is scored against the outcome it was meant to reach. VoxMith flags:
Hallucinated answers
Workflow failures
Tool and API errors
Policy violations
Drop-offs
Escalations
Unmet requests
You can add your own rules in plain language and they become evaluators that run on every session.
3
Trace the cause, not the symptom
Knowing a session failed is half an answer. VoxMith attaches a cause from a fixed taxonomy:
The tool errored
The tool was never called
Retrieval came back empty
A parameter was wrong
The data was stale
The capability does not exist
The prompt left a gap
Latency ran long
One cause per failure, with the session attached as evidence.
4
Turn the failure into a fix
Recurring causes are grouped and ranked by what they cost. Each one arrives with a specific recommendation and the sessions that prove it.
One click sends it to GitHub as a pull request, or to Jira or Linear as a ticket, with the evidence attached.
Evals that run on production traffic
Most eval tooling runs before you ship, against cases you thought of in advance.
Before you ship
Cases you thought of in advance
Catches regressions on known problems and nothing else
You cannot write a test for a failure you have not seen yet.
On live traffic, with VoxMith
Every session is scored against your rules, continuously
A new failure mode shows up the week it appears, rather than the quarter someone notices
When a failure class is fixed, it becomes a standing check, and you find out if it comes back
This is the half of evaluation nobody covers. Pre-launch testing proves your agent handles what you imagined. Production evals tell you what your users actually did.
The metrics VoxMith monitors
Six numbers carry most of the signal. The first answers whether the
agent is doing its job. The rest explain why it moved.
Metric
Healthy range
Resolution rate
Share of sessions where the user got what they came for
Containment
Share finished without a human picking it up
Correction rate
How often the user had to put the agent right
Repetition rate
How often the user said the same thing twice
Drop-off rate
Share where the user left mid-session
Escalation rate
Handoffs to a human, split by avoidable and not
Tool error rate
Failed and silently empty tool calls
Response latency
Time to first token or first word, p50 and p95
Total spend divided by sessions that worked
Work out what yours cost
Voice and chat in one view
The same analysis runs on both, which matters when the same customer reaches you
through a chat widget on Monday and a phone line on Tuesday. Resolution, correction and
repetition mean the same thing in either channel.
Voice adds signals that have no equivalent in text, and
they are where voice agents actually fail:
Read the transcript of these sessions and every one looks fine. The failure is in the timing.
Alerts when a failure starts spreading
One bad session
Noise
A single bad session
Same cause, 40 times in a day
A release that went wrong
The same cause appearing forty times in a day
VoxMith alerts on the pattern, not the incident.
Threshold alerts
Set a threshold on any metric or failure cause and get told when it crosses.
Drift detection
After a prompt change or a model swap, drift detection compares the new traffic against the baseline so you learn whether the change helped before your users do.
Set up in about
five minutes
Install the SDK and swap your provider import. No infrastructure changes, no change to how your agent behaves.
1
Install the SDK
One code snippet, any framework
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pip install voxmith
2
Connect your agents
Automatic data collection starts
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from voxmith.ai.provider_openai import OpenAI
Your dashboard starts filling
As soon as sessions begin flowing.
Encrypted in transit and at rest. Personal data is handled automatically on ingestion. VoxMith never trains on customer data.
Encrypted by default
Data is encrypted in transit and at rest to protect sensitive conversation data.
Your data stays yours
Customer conversations are never used to train AI models.
Deploy your way
Run VoxMith in your own cloud, on-premises, or in air-gapped environments.
Access under control
Manage permissions with role-based access, audit logs, and configurable data retention and deletion.
AI agent monitoring FAQs
What is AI agent monitoring?
AI agent monitoring is the practice of checking every conversation a deployed agent has with real users to find the ones that failed, identify the cause and fix them. It differs from uptime monitoring, which tells you the system responded but not whether the user got what they came for.
How do you monitor an AI agent in production?
Instrument the agent so every session is captured, then analyse each one after it closes. Score it against the outcome it was meant to reach, classify any failure by cause, group recurring causes and alert when one rises. VoxMith does this with an SDK that installs in about five minutes.
Which metrics matter most for AI agent monitoring?
Resolution rate, containment, correction rate, repetition rate, drop-off rate and cost per resolved session. Resolution rate is the headline, because it answers whether the user got what they came for. The others explain why that number moved.
What is the difference between agent monitoring and observability?
Observability tells you what your system did: spans, latency, errors and traces. Monitoring in the VoxMith sense tells you what the user got. A session can have clean traces, a 200 response and no errors, and still leave the user without an answer.
Does VoxMith sample sessions or analyse every one?
Every session. Sampling works for performance metrics because latency is common. It fails for quality, because the failures that cost the most are rare, and a two percent sample will miss almost all of them.
What causes AI agent failures in production?
Most production failures trace to a handful of causes: a tool that errored, a tool that was never called, retrieval that came back empty, a wrong parameter, stale data, a capability the agent does not have, a gap in the prompt, or latency that ran long. Almost none of them raise an exception.
Is this component responsive?
Yes, the FAQ component is fully responsive and adapts seamlessly to desktop, tablet, and mobile screen sizes.
Is this component responsive?
Yes, the FAQ component is fully responsive and adapts seamlessly to desktop, tablet, and mobile screen sizes.
