Your AI is working.
Now prove the work.
ViVE AI Ops ties every AI agent run to the business case it served. You see what the agent did, what it cost, where a person stepped back in, and whether the work itself got faster and cleaner.
Every layer reports green.
Nobody can say if the work got better.
The model platform shows tokens and latency. The agent framework shows runs and errors. The business system shows cases closed. Each one is right about its own layer. None of them connects an agent's retry to the customer who waited for it.
- Agent runs ✓ 20,919
- Latency ✓ 840 ms
- Error rate ✓ 1.6%
- Uptime ✓ 99.97%
- Did cases close faster? ?
- Where did people step back in? ?
- What did each outcome cost? ?
- Did last week's change help? ?
Figures are illustrative.
From one number on Monday
to the agent behind it.
Every view starts from the business case and goes down to the agent, the model and the step. The examples come from a demo claims workflow: three AI steps working inside a claims process.
How the AI did last week, in four numbers.
Claims served by AI, straight-through rate, AI cost per claim and people hours on hand-backs. Each number says whether its move is real or within normal variation, so nobody chases noise.
- Lists only what changed, such as a model switch or a new agent version
- Shows good news as well as bad
Five questions from the claim down to the agent.
Where does AI touch the work? Which AI step needs attention? Which agent drives it? Why? What does it cost the business? Each answer opens the right map, from the business process down to the agent flow.
- A guided path for leaders, deeper analysis for teams
- Follows loops between agents, not just single failures
- Where does AI touch the claim?3 AI steps · 98% of claims
- Which AI step needs attention?Damage assessment · 21% not straight through
- Which agent drives it?Validation agent · 27% of its runs not clean
- Why?
- What does it cost the business?
What the AI costs, against its own best weeks.
There is rarely a clean before-AI baseline. The Value Ledger measures each period against the AI's own best quartile, or a base you define, so the gap is something your team can actually close.
- AI cost, people hours on hand-backs, rework, cycle time
- Agents ranked by their gap, one click to the cases
What changed, what moved, and whether to trust it.
The Change Ledger finds every model swap, agent version and step change, then compares the 14 days before and after. Circuit Breaker flags an agent the day it drifts. Autonomy Dial and Audit Pack show where people still approve, and give the evidence.
- A model switch with 5.1× lower cost per run and fewer errors, found without anyone logging it
- Moves are shown as associations in time, not claimed as causes
| Measure | Before | After | Beyond normal? |
|---|---|---|---|
| Model cost per run | $0.0256 | $0.0050 | Yes · 5.1× lower |
| Errors | 1.64% | 0.95% | Yes |
| Run time | 16.4 s | 15.9 s | No |
Demo claims workflow with simulated data over two months. The views shown are simplified illustrations.
Three levels.
One case connects them.
Most tools see one of these levels. ViVE AI Ops reconstructs all three from the records they already leave, and joins them on the business key. Any case, whether a claim, an order or a loan file, can be followed from its first step to the model call that slowed it down, and back again.
Any workflow counts. Most organizations are automating traditional processes a few steps at a time, so AI and people share the same case for years. That shared work is where the cost and the risk sit, and where these three levels need to meet.
Useful before the first agent goes live.
Essential after.
Know what you are automating.
See how the work runs today: which steps carry the volume, the waiting and the rework. Put agents where they will change an outcome, not just a step.
See how AI actually takes part.
Where agents act, retry, loop back to each other or hand work to a person, and what each of those costs in time and money.
Prove the work got better.
Cycle time, straight-through rate and people hours, measured on the same cases before and after each change to a model, prompt or agent.
The numbers are computed.
Nothing on screen is made up.
When the subject is AI, the tool that reports on it has to be beyond question. These rules are in the product, not in a policy document.
Every sentence comes from data
Each line on a screen is a template filled from your records and reference tables. No language model writes the findings.
Associations, not causes
A move after a change is shown as a move after a change, with a test for whether it is beyond normal variation.
Good news too
Improvements sit beside regressions, so a team that fixed something can show it.
People hours, not headcount
Human work around AI is shown as hours on hand-backs. Cost in dollars appears only when you choose to supply rates.
Light to start.
Nothing changes in production.
ViVE AI Ops uses what your AI and your business systems already record. Read-only, no agents to install, no process redesign.
What we need
- The business case recordsCase or request ID, steps and timestamps from the system of record
- What your AI already emitsAgent runs, tool and model calls, with the case they worked on
- Optional: cost and SLA referencesModel prices, platform cost, team rates. Sensible defaults until you have them
Sources we read
AI telemetry and business records from the platforms teams already run, read as they are.
ViVE AI Ops is part of the RE-ViVE platform, not a separate install. Once AI telemetry is connected and tied to the business process, the AI Ops screens switch on, on the same SaaS, private cloud or on-premises deployment.
Product names are trademarks of their owners and are used only to identify data sources. No endorsement or partnership is implied.
One trace.
An answer for every leader.
Which agent to fix first
Ranked by business impact, not by error count.
"Which model, prompt or agent is holding up the work?"Is the work faster and cleaner?
Cycle time, rework and hand-backs on the cases they own.
"Did the AI actually shorten the queue?"Evidence, not assurances
Where people approve, where agents act alone, and the trail behind each.
"Show me what the agent did on this case."Cost per outcome
AI cost to the cent, per case, against the best weeks.
"What are we paying per resolved claim?"Bring one AI-enabled workflow.
See how the work actually runs.
We'll trace it from the business case to the agents and the models behind them, and show you what changed when your AI went live.