Generational thinking for government IT: plan beyond the current system lifecycle and preserve future options by keeping business data and meaning independent of any one system.
A government form can be technically correct and still fail the resident.
If people abandon it because questions feel intrusive, confusing, or unsafe, that’s a service-delivery failure.
Completion rate may be one of government’s most useful architecture metrics.
Interesting security crossover: a technique popularized by AI prompt-injection research is now showing up in conventional phishing.
Security research rarely stays inside the category where it started.
AI security may end up teaching us things about ordinary systems too, not just protecting AI.
https://t.co/oibpygbeY5
Several models disappear from @GitHub Copilot today.
A useful reminder for enterprise AI architecture: models will change much faster than most enterprise systems.
Designing for model replacement isn’t future-proofing. It may simply be normal lifecycle management now.
An interesting agent design pattern from @Salesforce: don’t give the model thousands of tools.
Give it Discover → Describe → Dispatch.
As enterprise agent capabilities grow, maybe good architecture isn’t maximizing the tools in context. It’s designing a small, stable interface to a large capability surface.
https://t.co/ptX1We60tJ
Really interesting look at Uber’s software factory from @udaykiran.
One thing that stood out: as models become more interchangeable, organizational context may become the bigger differentiator.
The model matters. What the organization makes available to it may matter more.
https://t.co/y2RC6yhBlm
Digital identity looks like a technology problem until you try to implement it statewide.
Then it becomes enterprise architecture: shared capabilities, agency boundaries, governance, funding, trust, and adoption.
The hard part of a digital front door may not be building the door.
We’re spending a lot of time discussing what permissions AI agents should have.
The harder architecture question may be what happens after permission is granted.
Can we constrain the environment, attribute every action, observe behavior, intervene, and shut it down?
#agenticai
AI coding keeps making me wonder whether we’ve had software development backwards.
If agents can increasingly regenerate the implementation, maybe code becomes more disposable while intent, architecture, constraints, and acceptance criteria become the durable artifacts.
@piotrmiecz I’d like to see agent TEVV include the execution environment itself: identity, tool permissions, network reach, approval gates and provenance. Otherwise we may evaluate the intelligence while missing most of the operational risk.
Suppose you believe you’ve developed the first AGI.
Do you announce it immediately?
Or do you first use it internally to accelerate research and widen your lead?
That might be one of the most consequential incentives in the race toward AGI.
#AI