Chapter 2: Why This Transformation is Needed

1 The Fundamental Contradiction of Software Engineering

Looking back at the more than half-century development of software engineering, this field has always been troubled by a fundamental contradiction: the structural conflict between rapidly changing business requirements and the high cost of modifying software systems.

To understand the essence of this contradiction, we must first analyze the dual sources of software complexity. In the past, the essence of software complexity lay in the superposition of business complexity and technical complexity:

Business complexity stems from the complexity of the real world itself—intricate business processes, intertwined stakeholders, constantly changing business rules, and elusive user needs. This complexity is endogenous and cannot be eliminated because it directly maps the essential characteristics of the business domain.
Technical complexity stems from the process of transforming business requirements into executable code—architectural design trade-offs, technology stack choices, concurrency and performance handling, edge case coverage, and legacy system compatibility. This complexity is exogenous, brought about by implementation methods, making already complex business logic even more difficult to manage at the code level.

The business environment changes rapidly, and market competition requires enterprises to respond to changes on a weekly or even daily basis; however, once a software system is built, it takes on a “solidified” characteristic—every functional module, every line of code, and every interface is like being cast in concrete, and modifying them requires high costs and risks. This contradiction has become increasingly acute as digitalization deepens.

Facing this dilemma, traditional coping strategies usually involve continuously increasing human resources, optimizing project management processes, and introducing more advanced development tools. But these efforts often yield little results, or may even backfire. As Fred Brooks famously stated in his classic work “The Mythical Man-Month”: “Adding manpower to a late software project makes it later.” The reason is that communication costs grow exponentially with team size, and the productivity gains from new personnel are often offset by coordination overhead.

However, the breakthrough in large model agent technology is fundamentally changing this landscape—the two layers of complexity that have been superimposed on software engineering in the past will now be taken over by large model agents. AI can not only understand and process complex business logic (business complexity), but can also generate, refactor, and maintain code with extremely high efficiency (technical complexity). When AI can carry these two layers of complexity with nearly unlimited capability, the core contradiction of software engineering also shifts: from “how to manage dual complexity to respond to changes” to “how to accurately express intent to drive AI efficient implementation.”

Obviously, we need a fundamental paradigm innovation, rather than patching on the original path.

2 The Deep Dilemma of Current Models

Before delving into the direction of transformation, let us objectively examine several structural dilemmas faced by traditional software development models.

The Loss Chain of Requirement Transmission

In traditional waterfall or agile development processes, a business requirement from birth to final implementation needs to go through a long and tortuous transmission chain:

Business's original idea → Product manager's understanding and abstraction → PRD document's standardized expression → Technical personnel's interpretation → Detailed design plan → Specific code implementation → Test validation

Every link in this chain is a “translation” process—converting one form of expression to another. And every translation inevitably involves information loss, semantic drift, and understanding deviation. The business party wants to say A, the product manager understands it as B, the technical personnel implement it as C, and what is ultimately delivered to the user is D. This layer-by-layer attenuation transmission mechanism is the root cause of high rework rates and low customer satisfaction in software projects.

The Curse of Code Complexity

As software system scale expands, code complexity shows exponential growth, which is another “curse” that is difficult to escape in the software engineering field. Specific manifestations include:

New feature development becomes slower and slower: because every new feature added needs to consider compatibility with existing systems, needs to understand the design logic of large amounts of historical code, and needs to assess the impact scope on existing functions.
The probability of introducing new bugs when fixing bugs becomes higher and higher: when system complexity exceeds what the human brain can clearly grasp, any change may produce unexpected side effects, forming a vicious cycle of “pressing the gourd and lifting the ladle.”
The cost of onboarding new people becomes higher and higher: a developer who has just joined the team may need several months to understand the overall architecture and core logic of the system before they can carry out effective independent development.
Technical debt accumulates like a snowball: temporary solutions taken to meet deadlines, hack code left for compromises, continuously accumulate into a heavy historical burden, slowing down the team’s pace.

The Bottleneck of Human Resources

Excellent software engineers are always scarce resources in the market, which is determined by factors such as long talent growth cycles, high training costs, and large individual differences. When business enters a rapid expansion period, teams often face a dilemma:

If high hiring standards are maintained, it may not be possible to recruit enough people, leading to business development being limited by insufficient technical capacity;
If the threshold is lowered for rapid expansion, although the number of people increases, code quality and system stability may decline, technical debt will accelerate accumulation, and ultimately drag down long-term development.

This rigid constraint of human resources is a bottleneck that traditional software production models find difficult to break through.

3 Three Drivers of Transformation

Since traditional models have such profound structural dilemmas, where does the power for transformation come from? We believe that three interwoven forces are driving software engineering toward the new intent-oriented paradigm.

Generational Leap in Productivity

The maturity of large language models and AI agent technology has introduced a new type of “digital employee” to software production. Compared with human developers, AI agents demonstrate disruptive capability characteristics in multiple dimensions:

Capability Dimension	Human Developers	AI Agent
Execution Power	Limited by physiology, effective working time about 8 hours per day	Theoretically unlimited, capable of 24×7 uninterrupted work
Parallelism	Primarily single-threaded, one person can only focus on one complex task at a time	High concurrency capability, can simultaneously schedule multiple agents to handle different subtasks
Consistency	Affected by fatigue, emotions, and state fluctuations, output quality has variance	Stable and consistent, unaffected by physiological and psychological state
Learning Speed	Learning a new language or framework takes months or even years	Knowledge updates are nearly instant, only requiring model replacement or fine-tuning
Marginal Cost	Higher, including salary, benefits, office facilities, management overhead, etc.	Lower, mainly API call fees and computing costs

This generational difference means that the cost structure and efficiency curve of software production are undergoing fundamental changes. AI agents are not replacements for humans, but amplifiers of human capabilities—they can undertake large amounts of repetitive, rule-based implementation work, freeing humans to handle more creative and judgmental tasks.

Fundamental Migration of Complexity

In the intent-oriented software engineering paradigm, the distribution of system complexity has undergone a fundamental migration:

Complexity shifts from the “code level” to the “intent level”

Specifically:

AI agents, with their powerful pattern recognition and code generation capabilities, can easily handle enormous code complexity—whether it’s large-scale refactoring, legacy system migration, or complex algorithm implementation;
But what they cannot automatically handle is conflict detection, negotiation, and balancing between multiple intents.

Let us understand this through a concrete example. Suppose we are developing an e-commerce platform and may face the interweaving of the following intents:

Business Intent A: “Improve conversion rate”—this may lead the product team to want to add more promotional pop-ups and guide buttons to the interface;
Business Intent B: “Enhance user experience”—this requires the interface to remain clean and fresh, avoiding excessive disturbance to users;
Technical Intent C: “Ensure system security”—this may require adding complex identity verification processes before key operations, which will reduce conversion efficiency.

The tension among these three is not a purely technical problem that can be solved. It requires humans to make value judgments and trade-offs based on understanding of business essence. The core challenge in the intent-oriented era is no longer “how to write code,” but “how to define, coordinate, and balance competing intents.”

Significant Acceleration of Feedback Loops

Feedback cycles in traditional development models are often long and painful:

Write code → Compile and build → Run tests → Fix issues → Demo to product manager → Modify based on feedback → Test again

This complete cycle usually takes hours or even days. At this pace, the cost of rapid trial and error and frequent iteration is high, and teams tend to pursue “perfect” requirements analysis and design plans early in the project to reduce later rework—which is precisely the root of waterfall thinking patterns.

The intent-oriented paradigm completely changes this picture:

Adjust intent description → Agent generates code in real-time → Instant automatic validation → Real-time demo of working prototype

The entire feedback cycle is compressed to minutes, or even seconds. When intent adjustments can be validated so quickly, teams can adopt an “intent exploration” way of working—quickly trying different solutions, learning by observing actual effects, and then rapidly adjusting direction. This capability for rapid trial and error and rapid iteration will greatly improve the efficiency and success rate of business innovation.

4 The Deep Significance of Transformation

Intent-oriented software engineering brings not only improvements at the tool and efficiency levels, but also a profound reflection on “the role of humans in software production.” Understanding the deep significance of this transformation helps us better grasp the direction of transition.

Human Value Returns to Its Origin

First, it needs to be clarified that the goal of intent-oriented software engineering is absolutely not to make human developers unemployed. On the contrary, it aims to let “human value return to its most original and irreplaceable parts.” This role transformation is specifically reflected in three dimensions:

From “Translator” to “Intent Definer”

In the past, developers spent a lot of time “translating” business requirements into machine-executable code—this was skill-intensive but creativity-limited work;
Under the new paradigm, developers will focus on understanding business essence, identifying core problems, and defining problem boundaries and solution frameworks—this is the link that truly requires human wisdom and experience.

From “Executor” to “Value Decision Maker”

When AI can precisely execute every technical detail, humans no longer need to personally implement every functional point;
The human role shifts to higher-level value judgment—making trade-offs under competing constraints, handling edge cases and abnormal scenarios that AI struggles with, and being responsible for overall system quality.

From “Code Typist” to “System Architect”

Skills such as syntax details and API calling methods that once required extensive memory and practice will gradually be mastered by AI;
Humans need to elevate their attention to the system architecture level—focusing on coupling relationships between modules, data flow direction, system evolvability, and technical debt control.

Democratization of Knowledge Work

The intent-oriented paradigm also has profound social significance, namely promoting the “democratization of knowledge work” in the software development field.

When expressing clear, structured intent becomes more important than writing correct code, the entry barrier for software development will undergo a fundamental change:

Product experts and business analysts who deeply understand business logic and have keen user insights will be able to directly drive the construction of software systems without relying on technical personnel as “translation intermediaries”;
Creativity and business insight will become core competitiveness, while programming skills—though still important—will no longer be the only passport to participate in software innovation.

This democratization trend will unleash the innovative potential of society, allowing more domain experts to transform their professional knowledge into digital products.

Systematic Improvement of Software Quality

Finally, intent-oriented software engineering provides a feasible path for the systematic improvement of software quality.

By building constraint networks and automated validation mechanisms, we can transform quality assurance measures that previously depended on individual consciousness and experience accumulation into systematic, enforceable technical mechanisms:

Enforcement of architectural principles: no longer relying on developers’ personal habits or memory, but ensuring every line of code conforms to preset architectural specifications through constraint networks;
Built-in security baselines: security is no longer a pre-launch checklist item, but embedded into every layer of the system from the intent definition stage;
Automated test coverage: AI automatically generates comprehensive test cases based on intent, eliminating test blind spots caused by limited manpower or time pressure.

5 Why Now?

Faced with such a profound paradigm transformation, a natural question is: why is the transformation happening at this moment? We believe that the convergence of three factors—technology maturity, industry consensus, and economic pressure—constitutes the “perfect storm” for transformation.

Technology Maturity Crosses the Critical Point

First, the core technologies supporting intent-oriented software engineering have reached usable maturity:

Capability leap of large language models: Models represented by GPT-4 and Claude 3 have the ability to understand complex business requirements and generate high-quality production code, with levels approaching or even surpassing ordinary developers;
Breakthrough of reasoning models: Models with deep reasoning capabilities such as o1 and DeepSeek-R1 can perform multi-step logical deduction, handling software engineering tasks that require complex planning and decision-making;
Maturity of agent frameworks: Open-source frameworks such as AutoGen, MetaGPT, and LangGraph provide multi-agent collaboration infrastructure, making the decomposition and parallel processing of complex tasks possible;
Significant expansion of context windows: from early 4K tokens to today’s 128K or even theoretically unlimited context, large models can now “remember” and understand the overall structure of large codebases.

These technological advances are not linear improvements, but qualitative leaps. Together, they lay the technical foundation for the implementation of the IOSE paradigm.

Accelerating Formation of Industry Consensus

Second, the software industry’s perception of AI-driven development is rapidly converging, forming a near-consensus judgment:

GitHub launched the spec-kit toolset, explicitly advocating a development model centered on specifications (spec) rather than code;
Google explored the future form of Agentic IDE in internal project Antigravity, demonstrating the possibility of intent-driven development;
From Microsoft to Amazon, from startups to traditional enterprises, almost every technology company is launching its own AI programming assistant products, and the market education process has been completed.

This industry-level consensus means that IOSE is no longer the radical experiment of individual pioneers, but is becoming the mainstream direction of technical evolution.

Continuous Accumulation of Economic Pressure

Finally, the economic driving force cannot be ignored:

Software development labor costs have continued to rise over the past decade, with excellent technical talent becoming one of the most expensive assets for enterprises;
Global talent competition is becoming increasingly fierce, with enterprises investing more and more in recruiting and retaining technical talent;
Against the backdrop of increased macroeconomic uncertainty, enterprises’ desire for efficiency improvement and cost optimization has never been stronger.

This economic pressure constitutes the “pull” of transformation—it gives enterprises and teams strong motivation to explore new paradigms that can significantly improve efficiency.

6 Risks and Response Strategies of Transformation

Any profound transformation is accompanied by risks and uncertainties. For the transformation of intent-oriented software engineering, we need to face potential concerns squarely and think about how to effectively respond.

Common Concerns and Responses

Concern One: “Will AI eventually replace programmers?”

Our response is: not completely, but it will profoundly change the skill structure of programmers. Developers who “can only write code but don’t understand business” do face the risk of being replaced by AI. Future programmers must move up the value chain, becoming intent definers, system guardians, and AI collaborators.

Concern Two: “Is AI-generated code quality reliable?”

This is indeed a question that needs to be taken seriously. Just as we cannot assume that human-written code is necessarily bug-free, we cannot blindly trust AI-generated code. The solution lies in establishing完善的 constraint networks and validation mechanisms—just as human code needs Code Review, AI code also needs to undergo intent alignment review and quality checks.

Concern Three: “Can existing teams adapt to this change?”

Organizational transformation is never accomplished overnight. Forcibly advancing may lead to team resistance, efficiency decline, or even talent loss. The key lies in adopting a gradual transformation strategy, helping teams adapt to the new paradigm through training and cultural construction, rather than simply replacing old processes with new tools.

Specific Risk Control Strategies

Based on the above analysis, we recommend the following risk control strategies:

Small-step piloting, gradual promotion: Start validating IOSE methods from non-core business or newly launched projects, accumulating experience and confidence before expanding to core business;
Dual-track parallel, safe transition: During the transition period, let old and new models run in parallel for a period of time, ensuring business continuity while providing a buffer for team learning and adaptation;
Metrics-driven, data speaks: Establish clear effectiveness evaluation indicator systems, use objective data to verify transformation effects, and adjust strategies in a timely manner;
Invest in talent, empower transformation: Invest resources in upgrading the skills of existing teams, helping them transform from “code writers” to “intent architects.”

7 Chapter Summary

Through the analysis in this chapter, we can draw a clear conclusion: intent-oriented software engineering is not a fleeting technological trend, but an inevitable choice to address the deep contradictions in the software engineering field.

Core Problem	Traditional Model Dilemma	Intent-Oriented Model Solution
Requirement transmission loss	Multi-level translation leads to information attenuation	Structured intent reaches execution layer directly, eliminating intermediate links
Code complexity curse	Exponential growth with scale	AI handles code complexity, humans focus on intent-level complexity
Human resource bottleneck	Excellent programmers are scarce and expensive	AI provides nearly unlimited execution power
Long feedback cycles	Measured in days or weeks	Compressed to minutes, supporting rapid trial and error
Difficult quality assurance	Relies on manual review, limited coverage	Constraint networks automatically ensure, systematically improve quality

The deep essence of this transformation lies in: shifting complexity in the software production process from “implementation detail complexity” and allowing humans to focus on those areas that truly require human wisdom—understanding business domain complexity and handling value judgment complexity.

In the next chapter, we will deeply explore the layered architecture of intent—this is the theoretical cornerstone for understanding intent-oriented software engineering and also an operational guide at the practical level.

Discussion Questions

Reviewing your team’s project experiences over the past year, what is the biggest pain point currently faced? Is it insufficient human capacity, overly frequent requirement changes, difficult-to-maintain legacy code, or other issues? Can these problems be alleviated under the IOSE paradigm?
Assuming AI can undertake 80% of your team’s coding workload, what more strategically valuable work do you think the saved time and energy should be invested in? Deeper understanding of user needs, more systematic thinking about technical architecture, or something else?
If your organization decides to launch the transformation to intent-oriented software engineering, what do you think might be the biggest resistance? Immaturity at the technical level, resistance at the personnel level, or inertia at the organizational culture level? How would you respond to these resistances?