Enterprise Agentic AI Adoption Framework
Agentic AI changes the risk profile — a mistake produces a real business action to reverse, not just a bad draft. This framework is how EPC Group's enterprise customers adopt agentic AI safely.
Frequently Asked Questions
What is agentic AI and how is it different from Copilot?
Agentic AI is AI that acts on the user's behalf: filing a ticket, updating a record, sending a message, executing a workflow. Traditional Copilot generates content the user reviews and acts on manually — human-in-the-loop on the final action. Agentic AI removes the human from the loop on the final action, at least for a defined class of low-risk actions. This changes the risk profile fundamentally: a Copilot mistake produces a bad draft; an agentic AI mistake produces an actual business action that has to be reversed.
What use cases fit agentic AI adoption first?
Six low-risk agentic use cases with proven adoption. (1) Meeting scheduling — agent finds mutually-available time and books, with human confirmation at the end. (2) Case-routing — support ticket categorization and routing to the correct queue. (3) Data pipeline monitoring — agent detects anomalies and creates ServiceNow tickets. (4) Software procurement pre-approval workflow — agent runs vendor risk check and routes to appropriate approver. (5) Expense report classification — agent categorizes line items per policy and flags exceptions. (6) Content moderation triage — agent applies first-pass classification, human reviews escalations. High-risk actions (money movement, PHI/PII disclosure, contract signature, HR decisions) stay human-only until the framework matures.
How does non-human identity work?
Every agent gets its own Entra Agent ID (Microsoft's emerging pattern for agent identity, part of the "Governed AI on Microsoft" reference architecture). The agent identity is: registered in Entra ID, assigned specific Microsoft Graph + third-party API scopes, subject to Conditional Access policies (only from approved network locations), subject to audit logging (every action the agent takes is attributed to the agent identity). This treats agents as first-class identities with the same access-review + credential-rotation discipline as human identities. Without agent identity discipline, agents become invisible actors + audit exposure.
What are the guardrails per action class?
Tiered guardrails aligned to action-class risk. TIER 1 — read-only queries: minimal guardrails (agent scope + audit log). TIER 2 — low-risk actions (case-routing, meeting scheduling, ticket creation): agent scope + audit log + rate limits + human review of aggregate patterns. TIER 3 — mid-risk actions (record updates, communications): agent scope + audit + rate limits + real-time human notification + reversibility SLA. TIER 4 — high-risk actions (money movement, contract signature, HR decisions): HUMAN-ONLY — no agentic delegation permitted. The framework never eliminates human-in-the-loop for high-risk actions; it eliminates human-in-the-loop only for actions where the risk is well-understood and reversible.
How do we handle an agentic AI incident?
The agentic incident response runbook has five phases. (1) Kill switch — every agent has a tenant-admin kill switch that stops all further agent actions. (2) Rollback — every action taken during the incident window is enumerated from the agent audit log; reversible actions are reversed. (3) Communication — affected users notified; regulatory bodies notified if the incident meets threshold. (4) Root cause — model behavior, prompt injection, credential compromise, or logic flaw. (5) Governance update — the incident results in an updated guardrail, a new blocklist, or a moved action from Tier N to Tier N+1. Every agentic AI production deployment needs this runbook before enablement.
What does an EPC Group agentic AI adoption engagement produce?
A 6-12 week fixed-fee engagement per use case. (1) Use case selection matrix (which use cases fit + risk-tier assignment). (2) Non-human identity architecture (Entra Agent ID + Conditional Access + audit). (3) Guardrails per action class. (4) Agentic incident response runbook. (5) 3 pilot use cases delivered (start low-risk to establish operating rhythm). (6) ROI measurement dashboard per agent (cost per resolved interaction vs human-alternative cost + accuracy vs manual baseline). (7) Governance integration with AI CoE + vCAIO.
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