Chapter 1: What is Intent-Oriented Software Engineering
1 A Core Proposition
Software engineering is undergoing a profound paradigm shift, where future software development will no longer be a craft centered around code, but a new engineering form—Intent-Oriented Software Engineering (IOSE).
The essence of “intent-oriented” lies in introducing large model agents (Agents) into the core of software production, allowing them to replace human programmers, testers, and even architects in undertaking repetitive implementation work in traditional development processes—from writing detailed design documents to writing specific code, constructing test cases, building automation scripts, and executing unit tests. Under this paradigm, as long as humans can express intent accurately and completely, large models can efficiently transform it into runnable software systems.
To better understand the revolutionary significance of this paradigm, we can compare it with another major paradigm shift in software engineering history—Object-Oriented Programming (OOP). The essence of OOP is to reduce programming complexity through mechanisms like encapsulation, inheritance, and polymorphism, helping human developers manage increasingly complex code structures and making it easier for “humans to understand code.” In contrast, the essence of Intent-Oriented Software Engineering is the disruptive change brought to software engineering by the efficiency improvement of large model agents—when AI can efficiently transform human intent into executable code, the core contradiction of software development has shifted from “how to help humans better write and understand code” to “how to help humans express intent more accurately and have AI implement it efficiently.”
It is important to emphasize that this is not a replacement manifesto for programmers, but a profound re-examination of the essence of software engineering. When artificial intelligence can efficiently handle the technical details of “how to do it” (How), human core value will inevitably return to a more essential level—defining “what to do” (What) and understanding “why to do it” (Why).
When AI can efficiently execute “how to do it,” human core value will return to “what to do” and “why to do it.”
2 Layering of Intent
In the intent-oriented software engineering paradigm, “intent” is by no means an isolated natural language instruction, but a layered semantic system. We divide it into three levels: business intent, product intent, and technical intent. Among these, product intent and technical intent are the core focus of this transformation.
Business Intent
Business intent focuses on macro-level business goals, expressing the value propositions that an organization hopes to achieve. It answers the question of “why do it”—such as “improve member renewal rates,” “reduce customer churn,” or “enter new markets.” This level of intent usually comes from senior managers or business leaders who set the direction for the entire organization. In the IOSE transformation, business intent remains relatively stable, serving more as background context rather than the core object of transformation.
Product Intent
Product intent is the bridge connecting business and technology, answering the question of “what to do.” When business intent says “improve member renewal rates,” product intent translates it into specific product solutions: “Add benefit expiration reminder functionality for paid users, reaching them through multiple channels including app push notifications and email.”
Product intent is one of the core battlefields of this transformation. Under the IOSE paradigm, product managers are no longer just writing PRDs and drawing prototypes, but need to define structured product intent—clearly identifying target users, usage scenarios, value propositions, and acceptance criteria. This structured product intent can directly drive AI to generate runnable software, giving product personnel the ability to directly create digital products without being limited by “whether they can write code or not.”
Technical Intent
Technical intent answers the question of “how to do it correctly,” defining the technical boundaries that must be followed when implementing product intent—performance indicators, security specifications, architectural constraints, quality standards, etc. For example, “interface response time controlled within 50 milliseconds” or “all APIs must be authenticated through OAuth2.0.”
Technical intent is another core battlefield of this transformation. Developers shift from “personally writing every line of code” to “defining technical intent and reviewing AI implementations.” The quality of technical intent directly determines the reliability of AI-generated code. When developers shift their focus from coding details to constraint design, they are actually engaging in higher-level system architecture work—defining rules rather than executing them.
These three levels form a natural progression: business intent sets the direction, product intent defines functionality, and technical intent sets boundaries. In this IOSE transformation, product intent and technical intent are the primary interfaces for human-AI collaboration, and are the core assets that we need to invest the most effort in refining and accumulating.
3 Comparison Between Intent-Oriented and Code-Oriented Paradigms
To more clearly understand the transformation brought by intent-oriented software engineering, let’s conduct a comprehensive comparison with traditional code-oriented development:
| Dimension | Code-Oriented (Traditional Paradigm) | Intent-Oriented (Emerging Paradigm) |
|---|---|---|
| Core Activity | Developers spend significant time writing, debugging, and optimizing code | Primary effort invested in defining, decomposing, and validating intent |
| Human-Machine Division | Humans responsible for writing code, machines only responsible for compilation and execution | Humans focus on defining intent, AI responsible for generating specific code |
| Thinking Focus | Focus on How—how to implement a feature or solve a technical problem | Focus on What & Why—what functionality is needed, why this feature is being built |
| Output Form | Code files as primary output, documentation often lagging or missing | Structured intent becomes core asset, code as derivative product automatically generated |
| Validation Method | Rely on manual testing and Code Review to ensure quality | Ensure implementation meets expectations through intent alignment validation and automated testing |
The profound nature of this paradigm shift lies in: it is not merely a tool upgrade, but a fundamental redefinition of the essence of software engineering. In the intent-oriented era, code is no longer the center of engineering activities, but an instantaneous expression form of intent—just as a compiler translates high-level language into machine code, intelligent agents “compile” human intent into executable code.
4 A Vivid Analogy
If we were to use a vivid analogy to understand the differences between these two paradigms, music composition might be a good comparison.
Traditional software development is like personally assembling and training a band:
- You need to personally recruit and coordinate different roles such as guitarists, drummers, keyboardists, and lead vocalists
- All members must carefully study the same sheet music (requirements document), trying to understand its nuances
- Everyone follows the unified direction of the conductor (Scrum Master), advancing at a fixed rhythm
- Through repeated rehearsals (iterative development), they build rapport and eliminate differences
This process is full of interpersonal coordination complexity, and friction at every step consumes significant time and energy.
Intent-oriented development, on the other hand, is more like conducting an AI symphony orchestra:
- You don’t need to tell each musician exactly how to play their instrument—these technical details are mastered by the AI musicians themselves
- You only need to describe to the conductor what kind of music you want to present (business intent)—whether it’s a stirring symphony or a soothing nocturne
- The conductor translates it into specific musical arrangements (product intent) and performance requirements (technical intent), ensuring each section understands its role
- Finally, a group of skilled, tireless, and perfectly coordinated AI musicians (Agent cluster) each perform their parts and automatically complete complex ensemble playing
In this analogy, humans shift from being “performers” to being “conductors,” from focusing on playing every note to focusing on the artistic expression of the entire piece. This is not just a change in work content, but a leap in thinking level.
5 Core Characteristics
Intent-oriented software engineering, as an emerging engineering paradigm, has a series of distinctive characteristics that differentiate it from traditional development. Understanding these characteristics helps us grasp the essence of this paradigm transformation.
Intent as Code
Under the IOSE framework, intent is no longer those vague descriptions scattered in meeting minutes, requirement documents, and verbal communications, but is strictly defined as structured, machine-readable specifications. These specifications have precise semantics that can be directly parsed, executed, and validated by AI agents. It is like a high-level programming language, except that the programming target is no longer computer hardware, but large model agents with understanding and reasoning capabilities.
Constraints as Guardrails
When AI gains the ability to generate code, how do we ensure it doesn’t “go wild” and generate code that doesn’t conform to team standards or has quality risks? The answer lies in the constraint system. By pre-defining multi-dimensional constraints such as architectural specifications, security policies, performance indicators, and complexity limits, we set clear guardrails for AI agents. These constraints are not limitations on creativity, but guarantees of quality—they ensure that AI remains on the right track while freely expressing itself.
Validation as Driver
In traditional software development, testing is often placed at the end of the development process, playing the role of “quality gatekeeper.” But in the IOSE paradigm, validation is no longer a post-hoc check, but an inherent component of intent definition. AI automatically generates test cases based on acceptance criteria, and these test cases in turn drive code generation—only code that can pass all tests is considered a valid implementation of intent. This “validation as driver” model embeds quality assurance into every step of development.
Assets as Compound Interest
In the IOSE era, a team’s most valuable assets are no longer specific codebases, but those knowledge components that can be continuously reused and evolved—high-quality intent templates, validated constraint rules, and effective prompt patterns. These knowledge assets follow the law of compound interest growth: with each completed project, the team accumulates a batch of reusable intent components; with each complex problem solved, there is one more experience template for future reference. Over time, the team’s intent asset library becomes richer, and development efficiency improves exponentially.
6 Why It’s Not Just “AI-Assisted Programming”
People first encountering intent-oriented software engineering might ask: Isn’t this what GitHub Copilot, Cursor, and similar AI programming assistants are already doing?
The answer is no. There are essential differences between the two:
| Comparison Dimension | AI-Assisted Programming (Current Stage) | Intent-Oriented Software Engineering (Future Paradigm) |
|---|---|---|
| Role Positioning | AI acts as a “co-pilot,” assisting humans in completing code writing | AI becomes an independent “executor,” capable of independently completing end-to-end tasks |
| Intent Granularity | Primarily handles line-level or function-level code completion | Capable of understanding and executing task-level or even business-level complex intents |
| Human-Machine Relationship | Humans remain the main body of coding activities, AI provides assistance | Humans transform into the main body of intent definition, AI responsible for specific implementation |
| Code Source | Code primarily written by humans, AI responsible for local completion and optimization | Code automatically generated by AI based on intent, humans responsible for validation and acceptance |
We can understand intent-oriented software engineering as the next evolutionary stage of AI-assisted programming—it marks the leap of software engineering from a “human-machine collaborative coding” model to a “human-machine deep division: humans focus on defining intent, machines responsible for execution and implementation” model. This is not just an enhancement of tool capabilities, but a restructuring of production relations.
7 Chapter Summary
Intent-Oriented Software Engineering (IOSE) represents a new software engineering paradigm. Its core ideas can be summarized in the following four points:
- Humans focus on “What” and “Why”—investing primary effort in defining product intent and technical intent, rather than getting bogged down in specific code implementation details;
- AI focuses on “How”—intelligent agents are responsible for generating code, writing tests, executing deployments, and other implementation-level work;
- Intent layering—business intent sets the direction, product intent defines functionality, technical intent sets boundaries, forming a complete intent system;
- Validation-driven development—using intent satisfaction as the core standard for measuring software quality, rather than traditional lines of code or feature coverage.
Among these, product intent and technical intent are the core operational objects of this transformation—product personnel directly drive software development through structured intent, while technical personnel ensure AI generation quality through constraint design. This transformation is not a distant future, but a reality that is happening now. In the following chapters, we will deeply explore why this transformation is necessary and how teams should systematically complete this transition.
Discussion Questions
- In your current project, how is daily work time allocated? What proportion is spent on “writing code,” and what proportion on “understanding requirements, analyzing business, and designing solutions”? Is this allocation reasonable?
- Assuming that through the introduction of the IOSE paradigm, you could compress “writing code” time by 80%, what more valuable work would you invest the saved time in? Deeper understanding of users, or more systematic thinking about architecture?
- Imagine a future work scenario: if you only need to clearly and completely describe requirements in natural language, and AI can generate usable code, what fundamental changes would occur in your daily work? What new capabilities do you need to develop to adapt to this change?