ETHICS & REGULATION
“ You need to know exactly which version of the model was used, who approved it and when,” Chris explains.
“ It must be properly versioned and you need assurance it wasn’ t altered before going into production.
“ Then you have to log what it does like it was a user. You need to have things like explainable AI or reasoning systems that actually have an explanation as to why it made its decision with some level of confidence.”
This means that, without such governance mechanisms, organisations face not only security and compliance risks but also the possibility of losing control over their AI deployments as they scale.
Why AI agent management can’ t wait The simplicity of building agents means their proliferation within organisations will happen faster than many anticipate – making establishing agent lifecycle management solutions a critical present-day need, rather than a future consideration.
“ People today are only getting a grip of how fast agents are going to grow,” Chris warns.
“ Once you see how simple it is to build an agent, you can build one in an hour or two, do testing for another day. Now, it solves a little problem within a workflow that used to be problematic, slow or required lots of coding and was suboptimal.”
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