When your AI agent hits a roadblock, will it stop?
What should an AI agent do when the normal route stops working? Joss Perold looks at why agents need to distinguish between obstacles they can solve and authority boundaries they must respect — and why failure handling needs to be designed into the system, not left to the model.
If Your AI Goes Down, Can the Business Keep Working?
If an AI-powered workflow stops, does the business stop with it? Chris Boshoff explores how fallback paths, degraded modes and resilience planning can keep critical work moving when AI is unavailable.
When your AI agent acts, who is accountable?
An AI agent can take an action in seconds. Accountability takes longer to work out. That gap is getting harder to ignore as agents move from answering questions to operating inside business systems.
Give Your AI Agent Authority You Can Take Back
Most conversations I have with leadership teams about AI agents eventually arrive at the same reassurance: if it goes wrong, we’ll switch it off. It’s a sensible instinct, and every system that can take consequential action should have that option.
Your AI governance policy can’t stop an AI agent
Most large organisations now have an AI policy. The harder question is whether their AI systems are technically capable of breaking it.
Muse Spark 1.3 and Enterprise AI Agents: Governance Has to Run With the Agent
There is a comfortable assumption forming around AI agents: as the models get better at following instructions, using tools and knowing when to pause for approval, enterprise risk will fall away with them.
AI research agents: why enterprises need to govern how answers are produced
A convincing AI answer can still come from a flawed process. That matters more as AI moves beyond answering individual questions and starts doing the research itself.
The question isn’t whether AI can act. It’s how much authority it should have.
A model can become very good at understanding instructions without knowing how authority works inside your organisation.
