AI Operations Intelligence
Actual token consumption per model per agent — not estimates. Data-driven model selection, agent optimization, and cost efficiency visibility across your entire AI stack.
The Visibility Gap
Your teams are deploying AI agents faster than you can measure their efficiency.
Cost-Blind Deployment
Teams deploy agents without knowing their token consumption or cost impact. There is no baseline for what 'efficient' looks like for each use case.
No Model Comparison
Teams default to premium models for every task. Without cross-model cost and performance data, there is no basis for right-sizing model selection.
Unmeasured Agent Efficiency
Agent token consumption varies 10x between implementations of the same task. Without per-agent metrics, optimization is guesswork.
Premium Models for Simple Tasks
Complex reasoning models are used for classification, summarization, and formatting tasks that could run on models costing 90% less.
What You Get
Cost per Agent, Model, and Use Case
Every AI request is attributed by agent identity, model used, and action type. See exactly which agents consume the most and where optimization opportunities exist.
Model Comparison
Run the same workload across different models and compare actual cost and token consumption. Data-driven model selection replaces default-to-premium habits.
Right-Sizing Recommendations
Identify where premium models are used for tasks that could run on cost-efficient alternatives. Policy can enforce model steering for low-priority workloads.
Optimization Trends
Track efficiency improvements over time. Measure the impact of model changes, prompt optimization, and agent refactoring on token consumption and cost.
Token Usage Breakdown
Every governed request records actual token consumption from the LLM provider's response.
| Metric | What It Measures |
|---|---|
| Input tokens | Tokens in the prompt (agent's request) |
| Output tokens | Tokens generated by the model (response) |
| Cached input tokens | Prompt cache hits (reduced cost) |
| Reasoning tokens | Tokens used for chain-of-thought reasoning |
| Total tokens | Aggregate consumption per request |
Token counts come from the LLM provider's actual response — not estimates.
Cost Attribution Dimensions
Tenant
Which customer or business unit
Agent
Which AI agent (by identity)
Model
Which LLM model was used
Provider
Which LLM provider
Action Type
Reasoning, tool execution, data retrieval, embedding
Classification
Data sensitivity of the governed action
Time Period
Hourly, daily, monthly aggregation
Anomaly Detection as Engineering Signal
Because token usage is tracked per-agent with identity attribution, unusual patterns are immediately visible.
Spend Spike
An agent suddenly consuming 10x normal tokens may indicate a prompt injection attack causing infinite loops, or a misconfigured prompt.
Model Escalation
An agent switching from a cost-efficient model to a premium model without authorization. Policy can enforce model boundaries.
Off-Hours Activity
Batch agents running outside scheduled windows. Unexpected activity during non-business hours as a potential compromise signal.
Cross-Tenant Anomaly
One tenant's spend deviating significantly from baseline. Early indicator of misconfiguration or unauthorized usage.
These patterns are governance-actionable — they can trigger policy responses (deny, require approval, alert) — not just dashboard notifications.
Optimize Your AI Operations
Stop guessing which models and agents are cost-effective. Start measuring.