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AI powered, human driven
Inventory, cost, risk and governance for all your enterprise agents.
A managed service that gives you one place to see every agent, how each one is performing, and what to change next.
Two of the three flagged agents sit in the lower-right quadrant. The third fails on a dimension this chart cannot show, which is why the register tracks six.
The problem
Custom agents built by internal teams. Agents embedded inside Salesforce, SAP, ServiceNow and Microsoft. Point solutions stood up by individual departments.
No one can list every agent in production today, custom-built or vendor-embedded, let alone who owns each one.
Accuracy, cost and latency are never measured consistently, so quality degradation goes unnoticed until it becomes a customer complaint.
PII exposure, hallucination, drift and unauthorized actions carry real regulatory and brand risk, invisible until something breaks.
The service
Reporting starts in week one. The infrastructure grows only as fast as you need it.
Complete register of every AI agent in use, custom-developed and third-party.
Accuracy, cost, latency, risk, drift and business outcome measured consistently for every agent, on day one.
A recurring report to CEO, CFO and CTO on risk exposure, agent performance and recommended changes.
As maturity grows, move from weekly reporting to live dashboards refreshed daily, then hourly.
Model right-sizing, workflow fixes, guardrails and performance improvements.
We build a single, living register of every agent in your enterprise.
Custom-developed agents
Third-party and vendor-embedded agents
How we build it
Structured discovery across IT, procurement and department leads
System and license audit across vendor platforms in use
Attribution of each agent to a department, client or product
Gaps flagged and closed as new agents are discovered
Performance and risk become comparable across the enterprise.
| Metric | Measurement approach |
|---|---|
| Accuracy | Human review, golden dataset, LLM-as-judge, business rule validation |
| Spend and cost | Platform consumption, model usage, infrastructure and license cost |
| Latency and reliability | Response and resolution time against SLA |
| Risk and compliance | PII exposure, policy violation, hallucination, unauthorized action |
| Business outcome | Time and effort saved, tickets resolved, invoices processed, cases closed, orders completed |
| Drift and change | Change in answer quality or behavior over time |
Stage 3 Weekly executive report
All 10 monitored agents, week of Jun 29 to Jul 5. Three are flagged, each for a different reason.
| Agent | Platform | Accuracy | Latency | Cost / wk | Status | Risk factor |
|---|---|---|---|---|---|---|
| Claims Intake | Custom | 96% | 4,200 ms | $3.1K | On track | — |
| Customer Service Bot | Custom | 95% | 3,800 ms | $2.8K | On track | — |
| Policy Renewal Assistant | Custom | 93% | 5,100 ms | $2.2K | On track | — |
| Document Extraction | ServiceNow | 97% | 2,900 ms | $1.9K | On track | — |
| Agent Performance Coach | Custom | 92% | 3,400 ms | $1.4K | On track | — |
| Billing Inquiry Bot | Custom | 94% | 3,600 ms | $2.0K | On track | — |
| Underwriting Support | Agentforce | 91% | 6,100 ms | $4.2K | Watch | Accuracy drifting toward threshold |
| Claims Fraud Triage | Custom | 88% | 7,200 ms | $5.6K | At risk | Accuracy below threshold |
| Vendor Invoice Matching | SAP Joule | 79% | 5,400 ms | $3.4K | At risk | PII exposure flagged in 3 sessions |
| Dealer / Partner Support | Agentforce | 90% | 4,800 ms | $2.6K | At risk | Attempted unauthorized system action |
Claims Fraud Triage for hallucination, Vendor Invoice Matching for PII exposure, Dealer / Partner Support for an unauthorized action. Each is routed to an owner before the next cycle.
Stage 4 Real-time analytics
Once the connectors are in, the weekly document becomes a live console. Every figure below traces back to an agent in the register, so a cost spike always has an owner.
Vendor-embedded platforms carry $12.1K a week, 41% of total spend, across four agents nobody built in-house.
Route low-confidence cases to human review, move classification to a mid-tier model
Cache retrieval results and cut redundant tool calls per session
Mask PII upstream and batch document requests
Enable prompt caching across the twenty most common intents
Stage 4 Analytics infrastructure
We start light with a human-curated weekly report, and grow the infrastructure only as fast as you need it.
Implementation principle. Do not wait for perfect observability. Start with the inventory plus sampled reviews, then add connectors where risk, spend or volume justifies the automation.
The service should not only report metrics. It should trigger ownership and change.
Update prompt and RAG source, add a golden test, route low-confidence cases to human review
Right-size the model, cache, batch, throttle, remove duplicated agent calls
Simplify the workflow, reduce tool calls, move heavy evaluation async
Limit privileges, add approvals, mask PII, enforce policy guardrails
Improve workflow fit, UI triggers, training and process ownership
Weekly cadence creates accountability. Analytics infrastructure creates continuous control.
Case study, contract review benchmarking
A client's contract review agent ran on a single high-cost model with no baseline to confirm the right choice had been made. Wizr's assurance harness replayed the same production contract set through nine alternatives, scoring each on accuracy, latency and cost per document under identical conditions.
| Model | Accuracy | Median latency | Cost / document |
|---|---|---|---|
| Claude Sonnet 4.5 (current agent) | 100% | 6,582 ms | 0.3425¢ |
| Kimi K2.5 (recommended switch) | 100% | 4,863 ms | 0.0684¢ |
| DeepSeek V3.2 | 100% | 7,044 ms | 0.0593¢ |
| Claude Sonnet 4.6 | 100% | 7,628 ms | 0.3416¢ |
| Qwen3-Coder-30B-A3B | 98% | 7,389 ms | 0.0154¢ |
| Claude Haiku 4.5 | 88% | 4,756 ms | 0.1140¢ |
Recommendation. Route the workflow to Kimi K2.5, with continuous re-benchmarking as new model releases land, and accuracy and drift monitored weekly.
Case study, Stellantis
One of the top automotive firms in the US.
The challenge
15,000 dealer inquiries a month handled manually, causing delays, falling dealer satisfaction and over $4.1mn in annual spend. An existing AI agent was already live, but with no baseline monitoring, resolution stayed stuck at roughly 25%.
The approach
The result
90-day rollout
A low-friction, services-led entry that can mature into a managed assurance platform.
0 to 30 days
31 to 60 days
61 to 90 days
Post 3 months
AI powered, human driven. Inventory, cost, risk and governance for all your enterprise agents.
