Agentic Control Plane

Every AI interaction - traced, explained, and under control.

Enterprise-Wide Observability

One dashboard for AI across your
entire business - teams, apps, and providers.

See usage, outcomes, and spend at a glance, then drill into any team, agent, or run.
  • Snapshot by department / team / app / provider
  • Cost, requests, and tokens at a glance
  • Leaderboards: top users, agents, personas
  • Provider & model mix - share and trends
  • One-click drilldowns: org → team → run

Cost & Token Insights

Know exactly where every dollar and token goes

Slice by model, team, user, persona, or agent - then drill into any run to understand what drove the cost.
  • KPI tiles: cost, requests, tools, tokens
  • Cost by LLM / user / persona / agent
  • Trends: volume & cost over time
  • Tokens: input vs. output, cache savings
  • Mix: provider share, agent vs. copilot, tool domains
  • Reliability: success rate, failed-run cost
  • Drilldowns: user & agent usage tables

AI Interaction Flow Tracer

Every agent run leaves a complete trace - from the first prompt to the final action.

See every tool call, every model invocation, every branch and retry, with full payload visibility and PII masking built in.
  • Persona → Prompt → Context → Tools → LLM → Result
  • Payload view with redaction & PII masking
  • Retries, fallbacks, branching and loops
  • Latency breakdown per step - exportable traces

AI Explainability

Make every AI decision transparent, auditable, and defensible

Every run includes a complete decision trace - reasoning chain, tool calls, evidence, and policy checks - so you can explain any outcome to any stakeholder.
  • Chain of thought and full reasoning trace
  • Tool call log with parameters and returned outputs
  • Prompt & context snapshots - full LLM input/output view with redaction
  • Run metadata: model, version, MCP tools, persona & scopes

Evaluators

Don't guess which model is best for your use case - prove it

Run side-by-side model evaluations on your own data and rank quality, cost, and reliability before you deploy.
  • A/B/C tests per use case
  • Metrics: accuracy, factuality, coherence, safety
  • Ops: tool calls, latency, tokens, $/result
  • Human ratings + ground-truth scoring
  • Leaderboards, recommendations, audit reports