#governance
governance shows up across 5 section(s) and 31 page(s) in this workspace. Use this page as a topic map, not just an archive.
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- Systems 16 page(s)
- Sentences 5 page(s)
- Self 1 page(s)
- Shelf 6 page(s)
- Sticky Notes 3 page(s)
AI Agents vs AI Workflows
A practical explanation of the difference between autonomous-seeming agents and controlled workflows, and why the distinction matters in production systems.
Agent Instructions and Handoff as an Operating System
A practical architecture for running AI agents reliably using instruction contracts, handoff memory, and measurable quality gates.
Decision-Making Under Uncertainty in AI Runtimes
A practical framework for making accountable decisions in AI systems when evidence is partial, time is limited, and outcomes are high-impact.
AI Architecture Explained: How Modern LLM Applications Work
A practical map of the layers that make modern LLM applications reliable: model access, retrieval, orchestration, interfaces, and governance.
Designing Reusable AI Skills
How to design AI skills with clear boundaries, input and output contracts, tool limits, side-effect controls, and escalation paths.
Engineering Agentic Systems for Reliability
A practical reliability model for agentic systems built around governed steps, verification, escalation, and observability.
Evaluation as a Runtime Discipline
Why evaluation should live inside the operating loop of an AI system instead of being treated as an occasional review ritual.
From Ad-Hoc Prompts to Repeatable Agent Workflows
A practical case study showing how structured instructions, handoff memory, and quality gates improved consistency and coverage in this repository.
Human-in-the-Loop Is a System Design Choice
Why oversight is a design decision, not a safety blanket.
What LLM-Ops Actually Means
LLM-Ops is governance over time. Understanding the lifecycle of probabilistic systems.
Skill Evaluation and Versioning
How to define expected behavior, detect regressions, version skill changes safely, and decide when rollback is the right move.
Skills vs Prompts vs Agents
A systems-level comparison of prompts, skills, workflows, and agents so teams can stop mixing up instruction surfaces with execution architecture.
Tech Stack for NLPg-Driven AI-Assisted SDLC
A language-first SDLC design: from intent and compiled instruction to governed execution, validation, and delivery.
Why Most AI Projects Fail After the Demo Stage
Why AI projects often stall after promising demos: weak integration, missing governance, low observability, and unclear adoption design.
Engineering Bounded Autonomy into AI Systems
How to design autonomous AI systems with safety constraints, operational boundaries, and governance hooks that keep autonomy useful without letting it become uncontrolled.
Enterprise AI at Scale: From Blueprint to Operating Reality
How enterprise AI differs from prototype: the governance contracts, architecture layers, and adoption design that turn blueprints into live systems.
A decision rule is a kindness to your future self.
Autonomy needs a brake.
Ethics is a design constraint.
Policy before power.
Governance before architecture.
What I Learned Running AI Governance at Scale
What I discovered when governance contracts met real organizational behavior, and why the blueprint rarely survives first contact with the workflow.
Engineering Agentic Systems deck
An engineering-focused deck on building agentic systems with explicit control points, checks, and observability.
ContentOps deck
An operating model for content systems where quality, cadence, and governance stay aligned.
Everything as Code deck
A control-plane framing where policy, infrastructure, workflows, and quality checks become versioned artifacts.
AI skill design templates
Three compact templates for defining, reviewing, and composing AI skills as reusable execution units.
The I-7 Reliability Standard deck
A companion deck to the I-7 loop with reliability-focused stage-by-stage framing.
The Enterprise AI Blueprint deck
A comprehensive blueprint for building enterprise AI systems with governance, architecture, and adoption frameworks.