AISDLC Insights · Edition 2026.10
Original perspectives on the AI software development lifecycle, production deployment, and the engineering pods that turn capability into outcomes.
I20 · Evidence and control · 6 min
A historical Java demonstration records a cross-tenant test failure followed by an implementation repair under unchanged checks. Reading both attempts reveals more than the final green result.
I19 · Independent assurance · 7 min
A practical evaluation method for Factory, Claude Code, SprintLoop, and other enterprise AI delivery platforms: trace authority, challenge the controls, and measure accepted changes.
I18 · Operating thesis · 8 min
AI can accelerate a feature roadmap without improving the business it serves. Sam M. Sweilem argues for testing the operating model with one team that owns a problem.
I15 · Operating thesis · 7 min
Sam M. Sweilem reviews Anthropic’s AI-native SDLC playbook and proposes an acceptance record connecting each release to its scope, evidence, and human authority.
I16 · Evidence and control · 6 min
A passing check and a release approval can refer to different things. A practical acceptance record connects the candidate, policy, environment, evidence, and decision.
I17 · Operating thesis · 6 min
Faster generation can move the queue without shortening delivery. A measurement brief for AI SDLC teams connects acceptance, review capacity, repair, and operating results.
I11 · Independent assurance · 7 min
A second model can find a defect. It cannot make a failed build safe. The engineering question is whether the system can refuse a change, and whether the author can change the terms of that refusal.
I12 · Delivery teams · 5 min
An AI delivery pod needs more than a backlog. It needs a visible account of the decision being made, the assumption being tested, and the evidence that would change the plan.
I13 · Delivery teams · 5 min
Source code and runbooks can cross an organizational boundary while the ability to operate the system stays behind. A useful handover lets the receiving team demonstrate the work while help is still available.
I14 · Evidence and control · 5 min
A busier team, a faster build, and a more fluent answer are observations. A scale decision needs a defensible connection between the changed work, the result that matters, and the cost of operating it.
I09 · Delivery teams · 6 min
AI makes implementation more abundant. The scarce resource is a team that can turn domain knowledge into a change, put it into production, and stay accountable for what happens next.
I10 · Evidence and control · 6 min
A working demonstration establishes possibility. A production release needs a specific case for the version being shipped, the authority it receives, and the conditions under which it must stop.
I01 · Operating thesis · 7 min
Enterprise advantage will not come from renting the same frontier model as everyone else. It will come from engineering the system that turns capability into bounded, repeatable, provable work.
I02 · Context systems · 7 min
Prompt craft still matters. Production reliability now depends on the full state made visible, trusted, current, and actionable at each decision.
I03 · Platform architecture · 8 min
The model supplies capability. The harness determines whether that capability can plan, act, recover, prove, and stop inside a real engineering environment.
I04 · Independent assurance · 7 min
A builder can test its own work. It cannot independently accept the risk created by that work.
I05 · Identity and authority · 7 min
If an agent can take consequential action, it needs its own attributable identity, a delegator, a purpose, an authority envelope, and an expiry.
I06 · Evidence and control · 8 min
Autonomy should expand only when the organization can prove what the system did, why it was allowed, how it was challenged, and how it can be stopped.
I07 · Operating thesis · 8 min
The goal is not to slow generation. It is to redesign the system that turns generated work into trusted change.
I08 · Operating thesis · 7 min
Models and interfaces will keep changing. The durable capability is knowing what to delegate, how to bound it, what evidence to demand, and when a human must say no.
AISDLC Insights publishes source-informed editorial synthesis and implementation positions. It is reference material, not a standard, certification, legal opinion, or authorization to deploy an agent.