Every fix tested on the conversations it was meant to fix, then proven on live traffic.
VoxMith is product analytics for AI agents.
It reads every conversation, finds what failed, tests the fix on real traffic, ships the fix and proves it worked.
It is continuous rather than a project. The agent changes, the models underneath it change, and the people using it find new ways to ask for things. An agent that was tuned six months ago is not tuned now.
Reads every conversation
Finds what failed
Failed
Tests the fix on real traffic
Ships the fix
Merged
Proves it worked
Stopped
Conversation quality
Wrong answers, missing knowledge and flows that confuse people. Each one traced to the prompt, the tool, the retrieval or the knowledge base that produced it, rather than treated as a general model problem.
Prompt patch
Traced to
Prompt
Tool
Retrieval
Knowledge base
Speed
Which stage is adding the delay on real sessions, and which lever shortens it. Latency is not one number. It is a budget split across transcription, model, tools and output, and only one line of it is usually the problem.
Where a smaller or faster model would hold quality
Transcription
Model
Tools
Output
Outcomes
Resolution and containment up, avoidable escalations and drop-offs down. These are the numbers your buyer is measured on, so they are the ones worth moving first.
Resolution
Containment
Avoidable escalations
Drop-offs
Cost per session
Fewer wasted turns and fewer failed sessions lower what every resolution costs. Usage data also shows where a smaller or faster model would hold quality.
Useful session
Useful session
3
Platform
Every candidate runs against the real conversations it was meant to fix. You see which one resolves the most and what it breaks elsewhere.
Step three is the one that matters. A recommendation without it is a model suggesting a prompt tweak, which anyone can get for free. Replayed against the sessions that actually failed, it becomes a comparison between options with a result attached.
Without replay
A suggested prompt tweak
Failures cluster by cause and rank by what they cost.
Failures cluster by cause and rank by what they cost. You see the biggest one first, not the loudest one.
Replay them against the sessions that failed
Replayed on failed sessions
Candidate A
Resolves the most
Candidate B
Candidate C
Breaks elsewhere
These are real conversations your users already had.
Simulated callers
Synthetic personas
Invented test cases
No simulated callers, no synthetic personas, no invented test cases.
You approve every change
This is a deliberate choice rather than a missing feature.
Automatic self-optimization sounds efficient until...
A regulated disclosure quietly gets rephrased.
A model decides a shorter answer is a better one.
Changes appear as markers on your performance charts, so when a number moves you can see which change it followed. That is the part teams usually cannot answer, and it is the reason agents plateau.
Once you can prove a change worked, you make the next one.
Performance Chart
A change
The number moves
Work out what yours cost:
What is AI agent optimization?
AI agent optimization is improving a deployed agent's accuracy, speed, outcomes and cost using evidence from real conversations rather than guesswork. It is continuous, because the agent, the models underneath it and the people using it all keep changing.
How do you improve an AI agent's performance?
Find the failures that cost the most, work out the cause, change the prompt, tool, flow or knowledge that caused it, and verify the change against the conversations that failed before shipping. Then watch that failure class on live traffic to confirm it stopped.
Does VoxMith change my agent automatically?
No. Every change arrives as a pull request, a Jira or Linear ticket, a downloadable skill or PR-ready code, with the evidence and replay results attached. You review it and apply it. Your agent only changes when you decide it does.
How do you know an optimization actually worked?
Two checks. Before it ships, each candidate fix is replayed against the real sessions that failed, so you see how many it resolves and what it breaks elsewhere. After it ships, VoxMith watches that failure class on live traffic and reports whether the rate fell.
How do you increase resolution rate for an AI agent?
Work from the failure causes, not the intents. Group every unresolved session by why it failed, rank the groups by what they cost, and fix the top one. Most of the gap usually sits in three or four causes, often a missing capability, an empty retrieval or context dropped between turns.
How do you reduce AI agent latency?
Find the stage carrying the delay before changing anything. Latency is a budget split across transcription, model time to first token, tool calls and output. Teams usually blame the model when a slow tool call is the problem. Stream everything, cache what repeats, and run tool calls in parallel.
What is a good response latency for an AI agent?
Under roughly 800 milliseconds for a conversational turn, based on published industry guidance. Past about a second in voice, callers start talking over the agent or assume the line has dropped. In chat the tolerance is higher but abandonment still rises.
Can VoxMith help reduce cost per session?
Yes, in two ways. Fewer wasted turns and fewer failed sessions lower the cost of every resolution. Separately, usage data shows where a smaller or faster model holds quality, so you are not paying frontier prices for work that does not need it.