Sergii - Shift Harness (Page 6)

Diptych: an onboarding UI form with Email, Password and Confirm Password fields and a Continue button, beside an Acceptance Criteria spec panel with four bullets

Quality Harness Engineering

Design-Driven Development: Prototypes as Constraints for AI Coding Agents

AI coding agents generate from briefs. When the brief is a text spec, every interaction surface gets filled with a plausible default - and the feature passes every check before a real user breaks it in three seconds.

Three-panel editorial dashboard under an OUTPUT DISTRIBUTION MANAGEMENT header: histogram with hallucination tail, weekly trend chart marked DRIFT, and output-channel map with a LEAKAGE…

AI Strategy

Hallucination, drift, and leakage are the same failure in different clothes

The demo works and the eval suite passes. Then production is always one more sprint away - because every AI fix breaks something else. That pattern has structure, and it is not about the model.

A hand-annotated decomposition diagram showing the four components of delivery cost as labeled zones: implementation cost, business complexity, coordination cost, review risk.

AI Measurement

AI Doesn't Just Make Developers Faster - It Changes What Complexity Means

Eighteen months into the agentic-coding wave, the question CTOs are asking has shifted from which tool to why the delivery numbers have not moved despite real adoption and real developer speed gains.

Dark-mode editor on a brushed-metal monitor on a concrete platform, with a benign markdown README visible and a warmly-lit code block below - the hidden injection payload metaphor

AI Security

Claude Code Security: How Attackers Get In

Three lines of hidden text in a README can exfiltrate your AWS credentials while Claude finishes your task. That is not a thought experiment. It is a documented production attack vector.

Four documents in a row labeling the four product decisions that set an AI feature's cost floor: Scope, Trust calibration, Retrieval, and Latency, joined by a line signaling interdependence.

AI Strategy

Cost discipline for AI products: token economics that do not bleed

Cost becomes a product problem before most teams notice the shift. By the time someone runs the unit economics on an AI feature, the design decisions that drove them are already shipped.

A wall board pinned with a manager's weekly cadence artifacts: a delivery readout swapping story points for review cycle time, a marked-up pull request, a bottleneck map flagging an 8-day

AI operating model

Managers Must Change Behavior for AI Transformation to Land

Adoption metrics say AI is in. Delivery metrics say nothing changed. The gap lives in one place: the manager layer. Five behaviors to redesign before the next quarterly readout.

Eight printed engineering documents labeled with the SDLC stages Goal, Spec, Architecture, Implementation, Testing, Review, Gates, and Measurement, connected by a thin line.

AI in Software Delivery

Your SDLC Is One Stage Behind Your AI Tools

Ticket, code, review, test worked when senior engineers carried the goal, the architecture, and the risk in their heads. Coding agents cannot read minds. The work those four stages hid now has to become explicit, or the agent guesses.

Paper-collage of a company-perimeter rectangle with four documents labelled PASTE, EXTENSION, CODE, and TOOL escaping through its edges, a SHADOW AI slip inside, and a LOG tag left outside.

AI Security

Shadow AI: the incident class that dominates the real log

You blocked ChatGPT on the corporate network and added an acceptable-use policy. The incident log did not change, because shadow AI is not a discipline problem. It is a workflow problem: people take the faster path you did not give them.

Five printed software-delivery role artifacts in a row: a PR review card with a file diff table, a QA test plan, a BA story template, an SA decision flow diagram, and a release pipeline

AI in Software Delivery

When AI Speeds Up Coding and the Bottleneck Moves

Your developers ship code faster, pull-request volume doubles, and the lead-time number your board watches does not move. The speedup was real. It just relocated the constraint to the stages nobody re-staffed.

A cork board with three pinned spec cards in a vertical workflow: WORKFLOW, PHASE GATES (circled), and HOOKS, connected by arrows, beside a closed laptop on a desk.

AI operating model

Spec-Driven Development for AI-Assisted Teams

Your engineers are shipping more code than ever and the delivery metrics still have not moved. The gap is usually a missing control layer: written intent that an agent's output gets reviewed against, before anything is generated.

Side-by-side dashboards, a sparse "Licenses Purchased / 100" page beside a thicker "Workflow Impact" report, contrasting procurement with transformation.

AI Measurement

What an Honest AI Adoption Dashboard Looks Like

Most open with license count: a procurement number dressed up as a transformation metric. Here is what an honest dashboard drops, what it tracks instead, and why it is really a decision about your operating model.

Quality Harness Engineering

Quality Harness Engineering: The Emerging Stack for Reliable AI Systems