What will happen to software development when autonomous AI agents can create, modify, and completely rewrite code?
The book “Renewable Code: Specification-Driven Software Development” offers a new perspective on building software systems. Instead of endlessly maintaining codebases accumulated over years of development, it treats code as a derived result—the implementation of requirements, business rules, architectural decisions, and technical constraints.
At the heart of this model are specifications as the source of truth, code provenance tracking, architectures designed for selective renewal, and AI agents capable of participating in software development as full-fledged contributors.
The book explains how to move from a model of “constantly fixing old code” to one in which changes begin with specifications, the system identifies the affected components, and code can be selectively updated or generated again from the underlying specifications.
This is a book about a possible future of software development in which the most valuable asset is no longer the number of lines of code written, but a precise description of what a system must do, why it must do it, and which rules it must never violate.
Table of Contents
Renewable Code
Specification-Driven Software Development
Preface
Why software must be maintained for years
Why code gradually turns into technical debt
How artificial intelligence is changing the cost of creating and modifying code
From maintaining codebases to managing specifications
PART I. THE PROBLEM: CODE AS ACCUMULATED HISTORY
Chapter 1. A World Built Around Source Code
Source code as the primary asset of software development
Why code is considered the source of truth
The incremental evolution of software systems
How large codebases are formed
Why old code continues to live on
Chapter 2. Code as an Accumulation of Decisions
Every line of code as a trace of a past decision
Why the history of development becomes more complex than the system itself
Software archaeology
When developers no longer understand why code exists
Hidden decisions inside the codebase
Chapter 3. Technical Debt and the Problem of Perpetual Maintenance
Why software ages
Temporary solutions that become permanent
Dependencies and outdated technologies
Why changing legacy systems is dangerous
“This module is better left untouched”
The cost of preserving the past
Chapter 4. The Limitations of Traditional Software Development
Incremental changes
Pull requests
Code review
Version control
Testing changes
Why these processes assume human participation
PART II. ARTIFICIAL INTELLIGENCE CHANGES SOFTWARE DEVELOPMENT
Chapter 5. When AI Becomes the Primary Author of Code
From programming assistant to autonomous developer
Generating functions
Generating components
Generating applications
Large-scale codebase modifications
Why the volume of change is no longer human-scale
Chapter 6. The Problem of Massive Changes
What happens when AI modifies thousands of files
Why it is impossible to read all generated code
The limits of traditional code review
Why the question “What changed?” is no longer enough
From reviewing lines of code to reviewing intent
Chapter 7. The New Unit of Software Development
If code can be created quickly, what becomes valuable?
Intent
Requirements
Business rules
Architectural decisions
Constraints
Tests
Why these become the primary intellectual assets
PART III. SPECIFICATIONS AS THE SOURCE OF TRUTH
Chapter 8. From Code to Specifications
The traditional development model
The specification-driven development model
Code as an outcome
What is a system specification?
Different levels of specifications
Chapter 9. What a Specification Contains
Business requirements
Functional requirements
Non-functional requirements
Business rules
Data models
API contracts
Architectural constraints
Security rules
Chapter 10. Specifications as the New Source Code
What does “source of truth” mean?
Why Git cannot always answer the question “Why?”
What matters more: the history of changes or the reason something exists?
Specifications as the foundation of the system
Code as a derived artifact
Chapter 11. From Intent to a Working System
User intent
Formalizing requirements
Architectural models
Technical constraints
Generating components
Generating code
Testing the result
PART IV. CODE PROVENANCE
Chapter 12. Why Code Must Be Able to Explain Its Existence
The question “Who changed this?”
The question “When was it changed?”
The question “Why does this component exist?”
The question “Which requirement produced this code?”
Chapter 13. Provenance and Lineage
What is provenance?
What is software lineage?
The relationship between requirements and code
The relationship between architectural decisions and components
The relationship between components and deployed systems
Chapter 14. The Software Provenance Graph
Requirements as graph nodes
Architectural decisions
Components
Modules
Code
Tests
Deployment
Tracing the path from a business requirement to production
Chapter 15. What Happens When a Specification Changes
Identifying dependencies
Impact analysis
Which components need to change
Which components must remain untouched
Building an impact map
PART V. RENEWABLE CODE
Chapter 16. Code as a Derived Artifact
What does “derived artifact” mean?
The analogy with compilation
Specification → generation → code
Why code can be recreated
Which parts of a system are suitable for regeneration
Chapter 17. Full Regeneration
Rebuilding a system from scratch
The advantages of a clean implementation
Eliminating accumulated technical debt
The risks of full regeneration
When full regeneration is justified
Chapter 18. Selective Regeneration
Why not everything needs to be recreated
Defining the scope of change
Dependency graphs
Selecting components for renewal
Localized generation
Compatibility validation
Chapter 19. Stability Versus Renewability
Why constant change can be dangerous
When a system should remain stable
The cost of regeneration
The risks of unpredictable behavior
Finding the balance between renewal and stability
PART VI. ARCHITECTURE FOR RENEWAL
Chapter 20. Architecture Designed for Regeneration
Modularity
Clear component boundaries
Contracts
Dependency isolation
Minimizing coupling
Maximizing replaceability
Chapter 21. Components Instead of a Monolithic Codebase
Why monoliths are difficult to renew
Boundaries of responsibility
Independent components
Contracts between components
Replacing an implementation without changing the system
Chapter 22. Contracts as System Protection
API contracts
Data schemas
Compatibility
Contract versioning
Why implementations can change when contracts remain stable
Chapter 23. Invariants: Rules That Must Never Be Broken
What is an invariant?
Business invariants
Data invariants
Security invariants
Architectural invariants
How to validate a system after regeneration
PART VII. AI AGENTS AS DEVELOPERS
Chapter 24. From AI Assistant to AI Agent
The assistant
The task executor
The autonomous agent
Multi-agent systems
How the role of humans changes
Chapter 25. The Analyst Agent
Understanding requirements
Identifying contradictions
Creating specifications
Clarifying business rules
Checking requirement completeness
Chapter 26. The Architect Agent
Creating architecture
Selecting components
Defining boundaries
Creating contracts
Architectural constraints
Chapter 27. The Developer Agent
Generating code
Modifying existing components
Refactoring
Regeneration
Working with specifications
Chapter 28. The Testing Agent
Generating tests
Validating invariants
Regression testing
Property-based testing
Verifying system behavior
Chapter 29. The Agent System
Agent collaboration
Task delegation
Conflicts between agents
Decision validation
Orchestration
Humans in the control loop
PART VIII. TESTING IN THE WORLD OF GENERATED CODE
Chapter 30. Why Testing Changes Is Not Enough
The traditional approach
Diff-based testing
The problem of large-scale generation
Changing implementation without changing behavior
Chapter 31. Testing Behavior
What the system must do
Behavioral contracts
Scenarios
Acceptance tests
Validating outcomes instead of implementation
Chapter 32. Testing Invariants
Rules that must remain true
Business constraints
Data constraints
Security constraints
Automated validation
Chapter 33. Trusting Generated Code
Can AI be trusted?
Levels of trust
Automated validation
Human approval
Fully autonomous changes
PART IX. THE RENEWABLE SOFTWARE SYSTEM
Chapter 34. The Complete Lifecycle
Creating a requirement
Creating a specification
Designing the architecture
Generating code
Testing
Deployment
Monitoring
Updating
Regeneration
Chapter 35. The Production System
What happens in production?
Observing the system
Collecting feedback
Detecting problems
Updating specifications
Regenerating the system
Chapter 36. Continuous System Renewal
Continuous Integration
Continuous Delivery
Continuous Regeneration
Automatically detecting changes
Automated regeneration
Safe deployment
PART X. THE PRACTICAL TRANSITION
Chapter 37. Why You Cannot Simply Rewrite Everything
The dangers of large-scale migration
The cost of replacement
Critical systems
Hidden dependencies
Incremental transition
Chapter 38. Your First Renewable Component
How to choose a component
Creating a specification
Defining contracts
Creating tests
Generation
Validation
Chapter 39. Adding Provenance to an Existing System
Linking requirements and components
Documenting architectural decisions
Creating a dependency graph
Tracking changes
Chapter 40. Incremental Codebase Migration
From legacy code to specifications
The Strangler Pattern
Replacing individual components
Maintaining compatibility
Gradually expanding the model
PART XI. A PRACTICAL CASE STUDY
Chapter 41. A Logistics Company: The Existing System
Orders
Shipments
Routes
Warehouses
Transportation
Delivery tracking
Customers
Chapter 42. Creating the Specifications
Describing business processes
The Shipment model
Shipment statuses
Business rules
Security
API contracts
Chapter 43. Creating the Architecture
Services
Events
Data storage
APIs
Integrations
Component boundaries
Chapter 44. Generating the System
How AI agents work
Creating components
Creating the database
Creating APIs
Creating tests
Chapter 45. A Change in the Business
A new requirement
Updating the specification
Dependency analysis
Identifying the impact area
Selective regeneration
Testing
Deployment
PART XII. THE FUTURE OF SOFTWARE DEVELOPMENT
Chapter 46. Is This the End of Traditional Programming?
What remains the responsibility of humans?
What moves to AI?
New developer roles
New skills
Chapter 47. The Programmer of the Future
Moving beyond writing lines of code
Designing systems
Creating specifications
Managing agents
Expressing intent
Chapter 48. Beyond Source Code
What will become a company's most valuable asset?
Knowledge
Specifications
Business rules
Architecture
Provenance data
Conclusion
We No Longer Have to Maintain the Past Forever
Code is not the memory of the system
Code is an implementation of intent
Specifications matter more than implementation
Rules matter more than individual lines of code
Architecture matters more than individual files
Renewable systems instead of endlessly repaired software
Top comments (2)
Code absolutely still has value. It's just no longer necessarily the primary intellectual asset.
AI may not make software development less intellectual. It may simply move the intellectual work further upstream—from writing implementations to defining systems worth implementing.
Very interesting thought. A great idea for a new book!