Yesterday, I spent the whole day in a kickoff meeting with one of the state agencies.
What started as a conventional system project has now moved in a very different direction.
Instead of simply delivering software, the system will have a live AI agent embedded inside it, operating within defined workflows, permissions, guardrails and kill-switch controls. From where I sit, those boundaries are not technical footnotes - they are control points, and control points are what make a system auditable, insurable and safe to capitalise.
The most interesting part is the level of acceptance.
In the past, getting VM access inside certain organisations - even for a small, isolated application - could be extremely difficult. That friction never appeared as a budget line, but it always showed up somewhere: delayed delivery, idle resources, a slower path to any return at all.
Today, some are already open to allowing a live AI agent to operate with sudoer privileges, and in tightly controlled cases, even full root access inside an isolated VM.
That is a significant shift.
It shows that the conversation is moving beyond whether AI is capable enough. The real questions are now about trust, control, security and how safely AI can operate inside real organisational infrastructure - which are precisely the questions we ask before signing off on any system that touches the numbers.
Once that trust is established, AI stops being just another application.
It becomes part of the operating layer - a standing item on the asset register rather than a cost written off at year end.
That distinction matters commercially. A conventional deployment depreciates from the day it goes live: relevance erodes, and the replacement cycle arrives on schedule. A deployment with an embedded intelligence layer behaves more like an appreciating asset. It accumulates context, workflows and institutional knowledge, so capability compounds and the yield on the original investment keeps widening.
Ultimately, our vision is to inject our own Agent AI and LLM intelligence layer into every client system, turning each deployment into an adaptive asset that grows in capability and value over time. For the finance function, that means a system that earns its keep across multiple budget cycles instead of one that needs to be re-justified every year.
#AgentAI #AIInfrastructure #EnterpriseAI #AIAutomation #LLM #DigitalTransformation #AIEngineering
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Gusto ko ang bahagi tungkol sa paksang ito dahil hindi masyadong teoretikal.
isolated application is the part I would forward to my boss.
Good write-up. getting VM access inside certain alone was worth the read.
この説明のところで少し考え込んでしまいました。
La explicación sobre esta parte es clara y fácil de seguir.