01
You can't define its limits
Access lets the agent reach a system, but does not establish which data and actions are actually permitted.
Why enterprise agents stall
Enterprise agents have an authority and accountability gap
Agents can connect to tools and complete work. The harder question is whether an organisation can define their authority, enforce its policies during execution and show what happened afterward.
Bounded authority·Runtime control·Accountable evidence
The production gap
The agent works. You still can't trust it to act.
02
You find out after it acted
When policies sit outside execution, teams discover violations after the agent has already acted.
03
You can't show what happened
When risk or audit asks what the agent did and why, no one has a complete answer.
What changes
Know the agent. Control the action. Retain the evidence.
Every agent has an identity. Every effectful action is reviewed before it runs, and read-only tools are reviewed where a rule or posture is configured for them. Policy applies to the reasoning and the execution, and every run leaves evidence.
01
Every agent has a job and an owner
Give each agent a defined purpose, identity and accountable owner before it touches a business system.
02
Permission is never a blanket yes
Every effectful action is checked against the remit it runs under, the purpose it claims, the rule its author wrote for that tool and the real arguments, before authority is granted. Read-only tools are checked where a rule or posture is configured for them.
03
Policy protects reasoning and execution
Apply per-tool policy and sensitive-data controls at runtime. Where detection is configured, detected values are replaced before the model sees them, while authorised tools continue working.
04
Every run leaves evidence
Keep a clear record of what the agent attempted and completed, ready when risk or audit asks.
A controlled path to production
Start with one job. Expand authority from evidence.
01
Define
Give the agent a job, identity and the instructions it needs.
02
Separate
The agent holds no credentials and cannot reach a system itself. Every action it proposes goes to the tool gateway, which decides, holds the credential and acts.
03
Connect
Expose only the approved systems and tools the job actually needs.
04
Govern
Apply action permissions, policies and PII protection while the agent works.
05
Record
Keep a clear record of every attempt, block, fallback and outcome.
The operating principle
Control has to operate while the agent acts
Difinity gives each agent an identity, applies organisational policies and sensitive-data controls in the governed path, and records the systems, decisions, actions and outcome in one run record.
That turns governance from a document outside the workflow into infrastructure that helps the enterprise grant useful, bounded authority.
Questions
AI Agent Governance Questions
AI agent governance is the system of identities, permissions, policies, data controls and run evidence that determines what an agent may do and records what it did. Difinity puts those controls in the execution path, so governance changes agent behaviour during a run instead of only reporting on it afterward.
Traditional AI governance often focuses on models, data, risk assessments and lifecycle oversight. AI agent governance must also control runtime authority: which systems and tools an agent can use, what data it can access, which actions it can take and when it must stop or escalate.
Enterprises control AI agents by giving each one a defined job, a distinct identity and least-privilege access to approved systems, data and actions. Difinity evaluates authority action by action as the agent works, then blocks or routes anything outside the permissions and organisational policies that apply to that run.
Protect sensitive data before it reaches the agent or an external model. Difinity can detect and redact configured PII in the governed execution path and record which protections were applied. Coverage depends on the data paths and controls configured for that agent.
No. A governed agent can complete lower-risk actions autonomously within explicit boundaries. Difinity evaluates authority and policy at execution time, then routes exceptions or higher-risk actions to human review without allowing the agent to decide its own authority.
An AI agent run trail should record the agent identity, the assigned job, the connector, tool and arguments it proposed, the gateway decision, any approval asked for and answered, whether the action ran or failed, the kind of value each redaction replaced, and the run outcome. Difinity keeps these events in one accountable run record for investigation, review and compliance evidence.
Start with one agent and one bounded job
Bring the workflow, systems and actions involved. We will show how the agent can operate under your policies with accountable evidence afterward.