AI agents do not just answer. They act.
DDAI reviews agentic AI workflows before deployment, procurement, or scale. We assess whether agents can use tools safely, follow workflow boundaries, respect permission limits, escalate correctly, and produce the evidence needed for governance and assurance.
The agentic risk
The risk is no longer only the answer. It is the action.
A chatbot can give a poor answer. An agent can call a tool, update a record, trigger a workflow, send a message, change a configuration, query a database, or take action inside a business process. That changes the control problem. Agentic systems need clear authority boundaries, tool permissions, workflow states, human approval checkpoints, monitoring, logging, and evidence.
What we review
Tool access and permission scope
We review what tools the agent can call, what each tool can access, which actions require approval, and whether the permission model is proportionate.
Workflow states
We review the workflow path, branching logic, escalation points, failure states, retry behaviour, and whether the agent can skip or distort required steps.
Delegation and multi-agent workflows
Where agents pass work to other agents, we review the trust assumptions, context transfer, authority boundaries, and risks of inherited errors.
Human oversight
We assess whether human review is placed at the right points and whether approval, override, and escalation records are preserved.
Data flow and residency
We review what data enters the workflow, where it is sent, what is stored, what leaves the system, and whether region or residency expectations are documented.
Evidence capture
We identify which runtime decisions, tool calls, policy checks, approvals, and incidents need to be recorded for governance, audit, and procurement review.
Review outputs
Agentic workflow risk map
Tool permission review
Workflow state and escalation review
Human oversight gap analysis
Runtime governance evidence requirements
Security and governance findings
Remediation roadmap
Evidary-ready evidence schema
Typical findings
Tool permissions broader than needed
Missing approval checkpoint before high-impact action
Weak separation between planning and execution
Insufficient evidence of human oversight
Unclear model or provider dependency
Incomplete logging of tool calls
Missing policy checks before external action
No record of why an agent action was allowed, blocked, or escalated
Govern the workflow, not only the model.
DDAI can help you review agentic workflows before they become operational infrastructure.
Review an agentic workflow