Agentic Development Principles
These principles are under development. They will be refined and expanded as validated.
Agentic development means intentionally designing workflows, feedback loops, and decision boundaries to maximize the value of AI agents as development partners.
This section defines principles for integrating AI agents into product development workflows, building on The Principles of Product Development Flow and focusing on effective human-AI collaboration. The principles are now organized by chapter so each page stays focused.
Principles, Corollaries, and Design Patterns
This documentation is organized in three layers, and each layer makes a different kind of claim. Confusing them weakens all three, so the distinction is worth stating precisely.
A Principle is a fundamental truth or proposition that serves as the foundation for a chain of reasoning. It is not a best practice or a suggestion. It describes the underlying physics and economics of Human-AI interaction. A principle is purely descriptive: it tells you how the world is, whether or not you act on it.
A Corollary is a constraint that follows necessarily from one or more principles. Corollaries are allowed to be prescriptive ("verification must be structurally enforced"), but they are not optional advice: if you accept the principle, you cannot coherently reject its corollary. A corollary remains implementation-agnostic — it constrains every solution without choosing one.
A Design Pattern is a named, optional, concrete solution to a recurring problem within those constraints. Patterns have alternatives and trade-offs: a competent team can accept every principle and corollary and still legitimately solve the same problem with a different pattern. Patterns live in Agentic Design Patterns.
A simple litmus test: a true principle survives being prefixed with "It is true that…"; a practice survives being prefixed with "You should…". For the middle layer, ask: "Can I accept the principle but reject this?" — if no, it is a corollary; if yes, and it names one concrete mechanism among several viable ones, it is a design pattern.
For example: The Principle of Asymmetric Risk (truth: failure cost is convex while verification cost is linear) entails The Corollary of Bounded Edit Radius (constraint: the agent's editable surface must be bounded), which is satisfied by the Layered Autonomy pattern (one solution: clearance levels) — but veto gates or sandboxing could satisfy the same constraint.
Each chapter includes the core principle statements, failure scenarios, and corollaries derived from those principles.
Chapters
- Foundations of Hybrid Allocation — What to delegate to AI versus code; the prerequisite structure for applying every other chapter.
- Physics of AI Integration — How AI systems behave as probabilistic machines: context limits, pattern inertia, the closed-loop requirement, adversarial input conflation, proxy collapse, and substrate drift.
- Economics of Interaction — The cost structure of prompts and model selection; why cheap generation does not mean cheap commitment.
- Governance of Technical Debt — How debt accumulates invisibly in agentic workflows and the constraints that keep it recoverable.
- Architecture of Flow — How context compounds, tools should be designed, and why architecture outlasts any individual artifact.
- Protocol of Communication — Why instructions degrade over distance and how protocol standardization limits signal entropy.
- Governance of Agency — The asymmetric risk of AI autonomy and the structural constraints that bound it.
- Symbiosis of Human-AI Agency — The division of labor between humans and agents; what each side does better and what atrophies under automation.
These principles are evolving. For team-level prerequisites, see Agentic Engineering Foundations. For implementation strategies, see Agentic Design Patterns. For foundational reasoning, see Product Development Principles.