Agentic RACI Framework
Human, AI and System accountability matrix for governed agentic enterprises.
Agentic RACI is an accountability model for organizations where AI agents act alongside people. It extends RACI so that the agent is Responsible, a named human is Accountable, the governance layer is Consulted and the audit log is Informed, and it adds two roles: the policy object that authorises the agent, and the documented human escalation path.
The Problem
Traditional RACI was designed for human actors only. AI agents making decisions at machine speed require a redefined accountability model.
Agentic RACI: 6 Roles
| R — Responsible (Agent) | The AI agent executing the task; defined in the Registry |
| A — Accountable (Human) | Named human principal; bears organisational responsibility |
| C — Consulted (Governance) | Policy gateway that validates every action before execution |
| I — Informed (Log) | Immutable, tamper-protected audit record of every action |
| +A — Authorising | Policy object granting the agent authority in this context |
| +A2 — Escalation | Documented human intervention triggered at governance boundary |
Traditional vs Agentic RACI
- Static project assignment → Dynamic assignment by task context
- Accountable = one person → Accountable = human + agent + system log
- Manual document audit → Audit via immutable system-generated logs
- Passive consultation → Governance layer actively enforces consultation rules
4-Step Implementation (under 4 weeks)
Task Inventory → RACI Matrix Design → Policy Object Encoding → Runtime Validation.
Frequently asked questions
Who is accountable when an AI agent makes a decision?
A named human principal. The agent is Responsible for executing the task, but organizational accountability stays with a person. In Agentic RACI accountability is carried jointly by the human, the agent's defined authority and the system log that records every action.
How is Agentic RACI different from traditional RACI?
Traditional RACI assigns static roles to people on a project. Agentic RACI assigns roles dynamically by task context, treats accountability as human plus agent plus system log, audits through immutable system-generated logs instead of documents, and has a governance layer that actively enforces consultation rules.
What are the six roles in Agentic RACI?
Responsible (the AI agent executing the task), Accountable (the named human principal), Consulted (the policy gateway that validates every action), Informed (the immutable audit log), Authorising (the policy object that grants the agent authority in this context) and Escalation (documented human intervention at a governance boundary).
How long does implementing Agentic RACI take?
Under four weeks, in four steps: task inventory, RACI matrix design, policy object encoding and runtime validation.
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