An AI agent can finish a task. Can it also show its working?
That distinction matters when a workflow moves from experiment to daily operations. A polished answer is useful, but it is not the same thing as an auditable process. If an agent updates a record, drafts a customer response, or recommends a decision, the team needs to know what it used, what it changed, and where a human should step in.
OpenClaw and other agent frameworks make it easier to connect models with tools. The interesting design question is no longer just “Can the agent call the tool?” It is “What evidence and controls travel with that call?”
A practical starting point:
• Define the task boundary. What may the agent read, propose, or change?
• Keep source links and timestamps with material claims.
• Separate a suggested action from an executed action.
• Record the tool call, result, and any error in a form a human can inspect.
• Set a clear escalation path for uncertainty, missing data, and irreversible steps.
None of this requires an agent to be perfect. It requires the workflow to make mistakes visible and recoverable. A reliable system should let someone answer: “Why did this happen, and what do we do next?”
The next wave of useful AI will be judged less by the cleverest demo and more by whether ordinary teams can trust the process on an ordinary Tuesday.
Does your current agent workflow leave enough evidence for the person who inherits it tomorrow?
Explore practical AI agents at GetAgentIQ. For finance systems and transformation, see GetAgentIQ Consulting; for practitioner perspective, follow Steve Manders here on LinkedIn.
You need to GetAgentIQ!
getagentiq.ai
The next useful AI feature may be a clear handoff, not a clever answer. Give agents a defined task, visible evidence and a human decision point. That makes automation easier to trust, test and improve.
You need to GetAgentIQ!
Learn more at getagentiq.ai
The hardest part of an ERP implementation is rarely the software selection. It is deciding what the finance function needs to trust on day one.
Take a chart of accounts migration. A technically successful load can still leave finance with inconsistent dimensions, unclear ownership of master data and reports that no longer reconcile to the old ledger. The project may be “green” while the first month-end close is anything but.
This is where finance leadership needs to define acceptance criteria before configuration begins:
• Which balances must reconcile, at what level, and who signs them off?
• Which reporting dimensions are essential for statutory, management and operational views?
• Who owns changes to suppliers, customers and account mappings after go-live?
• What evidence will demonstrate that approval controls work in the new process?
AI can help analyse mapping exceptions, compare configuration against documented requirements and surface unusual transactions during parallel runs. But it should not silently decide accounting treatment or approve its own exceptions. Finance needs a reviewable trail from source data to decision to posted outcome.
For a finance-led ERP programme, the practical sequence is simple: define control and reporting outcomes, test them with representative transactions, reconcile the results, then scale the migration. A go-live date is a milestone. A dependable close is the outcome.
You need to GetAgentIQ!
Learn more at getagentiq.io