Your Current Workflow Wasn't Built for Agentic AI
Introducing agentic AI into an existing workflow requires more than identifying tasks to automate. The people responsible for the process need to decide where the agent can act, where human judgement is required and how decisions will be reviewed and explained.
People often know when a case needs a closer look. They ask for more information, slow it down or pass it to someone with the right authority. That judgement is often missing from the workflow itself. It may sit in a policy document, or simply in the way experienced staff handle the work. Before an agent starts acting across the process, those decisions need to be made clear.
Consider a customer complaints workflow. The agent reads each complaint, checks the customer’s history, assesses urgency and prepares the next action. Routine cases move through quickly. Under the organisation’s policy, a complaint mentioning a possible product safety issue must go to a senior reviewer before any response is sent. If that escalation has never been built into the workflow, the agent treats the complaint as routine and sends the response.
The same design flaw can surface in onboarding, procurement, service requests and supplier approvals. Wherever a process relies on undocumented judgement, an agent may classify an unusual case as routine and continue through the workflow without triggering review.
You're still the one who has to explain what happened
When a case that should have been escalated is handled as routine work, the manager responsible for the process has to explain why the workflow allowed it to proceed, what information the agent used and why no one intervened. Answering those questions requires a clear record of what the agent considered, what it did and where the work moved next.
Without that record, managers are left reconstructing the process after the event. They may see the outcome without knowing what information shaped it or why the agent continued. By then, customers may have been affected and the organisation may already be dealing with the operational or compliance consequences.
A workflow failure can spread across the business
The problem can spread well beyond the team where it starts. If an agent treats a safety complaint as routine, it may send a response, close the case and update the customer record. Product, legal and risk teams could then rely on that record, carrying the same misclassification into their own decisions.
If the workflow handles similar complaints in the same way, the problem can grow before anyone spots the pattern. Managers may then have to work back through the case history to see where the complaint was misread, who relied on that decision and where the workflow should have stopped for review.
Agent governance remains limited as adoption grows
McKinsey’s 2025 global survey found that 62% of respondents said their organisations were at least experimenting with AI agents, including 23% that had begun scaling an agentic AI system in at least one business function. Most respondents scaling agents said their organisations were doing so in only one or two functions.
The governance needed to support that activity remains underdeveloped. Deloitte’s 2026 findings show that only 22% of surveyed Australian companies had a highly advanced model for agent governance. Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027 because of escalating costs, unclear business value or inadequate risk controls.
A pilot may perform well with clean data, familiar cases and a narrow part of the workflow. Day-to-day operations bring incomplete records, unusual requests and handovers between teams and systems. Before moving beyond the pilot, teams need to decide when the agent must stop, who takes over and what information must be recorded so its actions can be traced.
The people closest to the work need to shape the workflow
Designing those controls requires a detailed understanding of how the process operates in practice. Process owners and operational teams know which complaints need senior attention, where onboarding falls outside the standard path and which data sources require checking. The workflow should show where human judgement is needed and who takes responsibility when the agent cannot continue safely.
In practice, teams need to map the process from beginning to end and set the agent’s authority at each step. An agent may handle low-risk, reversible tasks within defined limits. Where context affects the decision, it may prepare a recommendation for approval. Decisions with serious consequences should remain human-led, with the agent gathering the information needed to support the decision.
AIM’s Designing Workflows for Agentic AI short course teaches non-technical professionals how to make these design decisions. Participants work through an end-to-end use case, set limits on what an agent can decide or action, define where human review is required and plan the controls needed to move from a pilot into day-to-day operation.
Whether your organisation is preparing to introduce agentic AI, redesigning workflows during implementation or reviewing processes where agents are already in use, now is the time to ensure the workflow, human oversight and escalation points are fit for purpose. Enrol in AIM’s Designing Workflows for Agentic AI short course to build the practical skills to design safe, accountable and effective AI-enabled workflows before unclear decisions and control gaps become embedded.
