Agentic AI Governance
Cost Estimation Calculator
Welcome to our proprietary total cost of ownership estimation calculator designed to help AI teams gain an understanding of governance cost considerations in agentic AI systems.
How to Use the Calculator
Input Variable
Modify your inputs by using the drop-down options, on/off toggles, and sliders in the left column.
Output Summary
Review the Executive Summary of your output on the right side of the calculator to understand the impact of your inputs.
Detailed Understanding
Use the sub-menu under the calculator title to switch your output calculations to more granular views for a deeper understanding of the impact.
This calculator is best viewed on a full monitor; not suitable for viewing on a mobile device.
Agentic AI Governance Layers
L1
Consumer & Application
User interfaces, external APIs, audit viewer, operations dashboard
L2
Human-in-the-Loop (HITL)
Approval queues, role-gated review consoles, override/veto, SLA enforcement
L3
Inline Governance
Prompt shielding, output validation, PII redaction, policy enforcement, action gating
L4
Semantic Orchestration
Intent classification, task decomposition, agent routing, context and state management
L5
Domain Orchestrators (Tier 1)
Domain-specific workflow coordination across compliance, risk, trading, and operations
L6
Specialist Agents (Tier 2)
Fine-grained task execution: AML detection, fraud analysis, regulatory filing, reconciliation
L7
Foundation Model & Inference
LLM inference, embedding generation, model versioning, fallback, inference gateway
L8
Tool & Integration (MCP)
Standardized tool access: code execution, SQL, APIs, document store, financial calculations
L9
Knowledge, Memory & Retrieval
Vector RAG, episodic memory, structured knowledge base, conversation history
L10
Security, Identity & Secrets
IAM, secrets management, zero-trust networking, encryption, DLP, SIEM feed
L11
Enterprise Data Sources
[Core banking], market data, CRM, regulatory databases, document repo, data lake
[Note: This model is based on the financial sector.]
RAIL
Third-Party Observability, MRM & Audit
Cross-cutting tracing, metrics, model risk management, audit logging, compliance reporting
KEY DATA FLOWS
Calculator Explainability Support
​​Agent Request Flow (downward):
A request enters at L1, is policy-checked at L3, semantically decomposed at L4, delegated through L5 domain orchestrators to L6 specialist agents, which call L7 foundation models with tools from L8, grounding responses in knowledge from L9, all secured by L10 and drawing on data from L11.
​
Response Flow (upward):
All responses traverse L3 inline governance on the way back up, ensuring output validation, PII checking, and hallucination scoring before any result reaches a user or downstream system.
​
HITL Escalation:
The Sensitive Action Gatekeeper in L3 and the Re-planner in L4 both have escalation paths directly into L2, where human reviewers can approve, reject, annotate, or veto any agent output before it proceeds.
​
Observability Signals:
Every layer emits distributed traces, structured logs, and metrics to the side-rail observability stack. This signal stream is independent of the main request path to avoid latency coupling.