Which agent approved this? Making multi-agent AI audit-ready - Webinar
For IT and AI leaders whose AI pilots stall before production: what it takes to run multi-agent AI in a real workflow, with governance and traceability built in from day one.
The pilot went well. The agents do what they're supposed to. Then someone from risk asks a simple question: which agent approved this, on what data, and who signed off?
That's where a lot of multi-agent AI stalls - in the space between the agents, where nobody designed for identity, access or traceability. Weiwei Feng calls it the four ways a workforce of agents breaks. On 12 November she takes one real workflow and puts it through all four:
A regulator asks which agent approved this - on what data, and who signed off. Could you answer?
One agent can call another - what stops it borrowing access it should never have?
The rules change next quarter - do you reconfigure, or rebuild?
When it goes wrong at 2 a.m., is a human in control - or locked out?
If any of these give you pause, this session is for you.
What you'll take away
A test you can run on your own AI initiatives the next morning: the four questions, and what a good answer sounds like.
What traceability by design looks like in a real workflow - who decided what, on what data, when.
Where your people need to stay in control, and how to spot that they're already locked out.
Who it's for
IT and AI leaders, CTOs and transformation leads in mid-sized and large organisations in regulated sectors. You have AI initiatives or a working demo - and you're the one who'll have to answer when audit asks how it decides.
Who's running it
Weiwei Feng is a former Global AI Director at Capgemini, where he contributed to a group-wide AI offering and getting enterprise AI into production. He now builds Stolonic, a multi-agent engine with identity, distributed security and traceability built in, designed from the business requirements with the domain expert on the team.
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