Any system that takes action will sometimes take the wrong one - and agents are no exception. The difference between a manageable slip and a costly incident is not whether the agent errs, but whether your setup notices, contains and reverses it. Planning for failure is not pessimism; it is the price of letting software act on your behalf.
Why agents fail in a particular way
A traditional bot fails obviously - it stops. An agent can fail plausibly: it confidently does the wrong thing, because it acts on its best interpretation of an ambiguous situation. That is exactly why oversight matters more here than with rigid automation, a point we raise in agentic AI vs traditional automation.
Four guardrails worth having
- Approval gates - the agent proposes; a human approves anything irreversible or costly
- Limits and permissions - cap what it can touch (spend limits, read-only access, blocked actions)
- Logging - a full record of what it did and why, so you can trace any mistake
- A kill switch - a fast, obvious way to stop it and hand back to people
Match the safeguard to the stakes
Low-stakes tasks (sorting emails) can run with light oversight. High-stakes ones (payments, customer commitments) need approval gates and tight limits. The skill is grading your workflows by consequence, then applying oversight to match - the heart of AI risk management.
Have a way back
Before you deploy, answer one question for every action the agent can take: how do we undo this? If there is no clean answer, that action should require human approval. An agent you cannot correct is an agent you should not have automated.
The bottom line
AI agents fail - good businesses simply make failure cheap, visible and reversible. Building that safety net is a discipline in itself, and a core theme of the AI Risk Management and Security course at London School of Business UK. Enquire today.