"Umana" (אומנא), derived from the Aramaic root אמן (aman), embodies the concept of masterful craftsmanship and skilled creation. In Aramaic literature, particularly the Talmud, an Umana represents more than just a craftsperson - they are a master of their trade who creates with wisdom and purpose.
The term carries deep philosophical significance, representing:
- The bridge between concept and creation
- The perfect execution of plans
- Divine-like craftsmanship
- Wisdom in implementation
This name perfectly captures our framework's essence: an AI system that acts as a master craftsman, bridging the gap between project conception and implementation with wisdom, skill, and trustworthiness.
This framework provides a systematic approach for AI developers to understand, decompose, and implement software projects. It consists of three core components that work together to enable autonomous software development:
- Project Definition
- Task Decomposition Framework
- AI Coding Agent
The system is designed to be self-improving, allowing the AI developer to enhance and expand the framework's capabilities over time.
The Project Definition serves as a comprehensive contract that specifies all aspects of the software project. It uses a structured schema that draws from established software engineering principles while being optimized for AI consumption.
ProjectDefinition:
domain:
type: Enum[Web, Mobile, Desktop, Robotics, AI/ML, IoT, Embedded]
required: true
architecturalStyle:
type: List[Enum]
options:
- Microservices
- Monolithic
- Event-Driven
- Layered
- Hexagonal/Clean
- CQRS
- Pipeline
- Real-time Processing
required: true
interfaceParadigm:
type: List[Enum]
options:
- GUI
- CLI
- API
- Event Stream
- Physical Interface (Robotics)
- Voice/Natural Language
required: true
coreCapabilities:
type: List[Category]
categories:
computation:
required: true
options:
- Batch Processing
- Stream Processing
- Real-time Analytics
- Machine Learning
storage:
required: true
options:
- Relational
- Document
- Time Series
- Graph
- In-Memory
communication:
required: true
options:
- HTTP/REST
- GraphQL
- WebSocket
- Message Queue
- gRPC
- Physical Protocol
security:
required: true
options:
- Authentication
- Authorization
- Encryption
- Audit Logging
- Rate Limiting
qualityAttributes:
type: Map[Attribute, Priority]
attributes:
- Performance
- Scalability
- Reliability
- Maintainability
- Security
- Usability
priority: Enum[Critical, High, Medium, Low]
required: true
developmentConstraints:
type: List[Category]
categories:
technology:
languages: List[String]
frameworks: List[String]
platforms: List[String]
operational:
deployment: Enum[Cloud, On-Premise, Edge, Hybrid]
monitoring: List[String]
logging: List[String]
compliance:
standards: List[String]
regulations: List[String]
timeAttributes:
developmentLifecycle: Enum[Waterfall, Agile, Spiral]
releaseStrategy: Enum[Continuous, Scheduled, Milestone]
maintenanceRequirements: List[String]
dataCharacteristics:
volume: Enum[Low, Medium, High, Very High]
velocity: Enum[Batch, Near-Real-Time, Real-Time]
variety: Enum[Structured, Semi-Structured, Unstructured]
sensitivity: List[String]
retention: Map[DataType, Duration]
environmentalContext:
geographicDistribution: List[String]
networkConditions: List[String]
operatingEnvironment: StringThe Task Decomposition Framework converts the Project Definition into a structured set of implementable tasks. It combines elements from:
- Hierarchical Task Network (HTN) Planning
- Work Breakdown Structure (WBS)
- Agile Story Splitting Patterns
- Domain-Driven Design (DDD)
- Automated Task Generation
def generate_tasks(project_definition):
base_tasks = get_universal_tasks()
domain_tasks = get_domain_specific_tasks(project_definition.domain)
arch_tasks = get_architectural_tasks(project_definition.architecturalStyle)
capability_tasks = get_capability_tasks(project_definition.coreCapabilities)
return compose_task_hierarchy(base_tasks, domain_tasks, arch_tasks, capability_tasks)- Dependency Management
def validate_project_completeness(tasks, project_definition):
missing_components = []
for required_component in project_definition.mandatoryComponents:
if not has_implementation_task(tasks, required_component):
missing_components.append(required_component)
return missing_components- Implementation Pattern Matching
def suggest_patterns(project_definition):
patterns = []
if project_definition.qualityAttributes['Performance'] == 'Critical':
patterns.extend([
'Caching Strategy',
'Connection Pooling',
'Async Processing'
])
return patternsThe AI Coding Agent executes the tasks defined by the Task Decomposition Framework. It:
- Generates code following the specified patterns
- Maintains project consistency
- Handles version control and documentation
- Validates implementation against Project Definition requirements
This section explores key AI planning methodologies that could inform our Task Decomposition Framework:
HTN planning is an AI planning methodology that creates plans by task decomposition. It was first introduced in the early 1970s and has been successfully used in various domains.
Key Concepts:
- Tasks are decomposed into subtasks until primitive actions are reached
- Planning occurs through recursive decomposition
- Domain knowledge is encoded in methods that specify valid decompositions
Example:
Task: BuildWebApplication
Methods:
1. Decompose into:
- SetupDevelopmentEnvironment
- ImplementFrontend
- ImplementBackend
- DeployApplication
2. Further decompose ImplementFrontend into:
- CreateUIComponents
- ImplementRouting
- IntegrateWithBackend
Potential Application: HTN could be used to create a hierarchical structure of development tasks, with each level becoming more specific until reaching actual implementable units of work.
STRIPS, developed in 1971, is a fundamental automated planning system that introduces a formal language for expressing planning problems.
Key Concepts:
- States are represented as sets of logical predicates
- Actions are defined by preconditions and effects
- Planning involves finding a sequence of actions to reach a goal state
Example:
Initial State:
- (not (has_database))
- (not (has_api))
- (not (has_frontend))
Goal State:
- (has_database)
- (has_api)
- (has_frontend)
Action: CreateDatabase
Preconditions: (not (has_database))
Effects: (has_database)
Action: CreateAPI
Preconditions: (has_database)
Effects: (has_api)
Potential Application: STRIPS could help in determining the correct order of development tasks and identifying dependencies between different system components.
PDDL, introduced in 1998, is a standardized format for representing planning problems, building upon STRIPS concepts.
Key Concepts:
- Separates domain description from problem description
- Supports typed objects and hierarchical typing
- Allows for complex goal conditions and action effects
Example:
(define (domain software-development)
(:requirements :typing)
(:types
component - object
database api frontend - component)
(:predicates
(implemented ?x - component)
(depends-on ?x ?y - component))
(:action implement
:parameters (?c - component)
:precondition (and
(not (implemented ?c))
(forall (?dep - component)
(imply (depends-on ?c ?dep)
(implemented ?dep))))
:effect (implemented ?c)))Potential Application: PDDL could provide a formal way to express both the general patterns of software development (domain) and specific project requirements (problem).
The following libraries and frameworks could be utilized in implementing this system:
PDDL4J(Java) - Full PDDL implementationpyperplan(Python) - PDDL planner implementationSHOP3(Common Lisp) - HTN plannerpyhop(Python) - Simple HTN planner implementationFastDownward(C++) - State-of-the-art classical planning system
Pydantic(Python) - Data validation using Python type annotationsJSON Schema- Language-agnostic schema definitionprotobuf- Google's language-agnostic schema definitionTypeScript- For type-safe schema definitions if using JavaScript/Node.js
networkx(Python) - Comprehensive graph libraryigraph(Python/R/C++) - High-performance graph librarygraphlib(Python) - Part of Python standard library for DAG operations
Apache Airflow- Complex workflow managementPrefect(Python) - Modern workflow managementLuigi(Python) - Pipeline/task dependency managementDagster- Data orchestration platform
libcst(Python) - Parse and modify Python source codeast(Python) - Python's built-in Abstract Syntax Tree modulejinja2(Python) - Template engine for code generationTree-sitter- Parser generator tool and incremental parsing library
PyGithub- GitHub API integrationpython-gitlab- GitLab API integrationJira-Python- Jira integration
Sphinx(Python) - Documentation generatorMkDocs- Project documentationpdoc- Auto-generate API documentation
hypothesis(Python) - Property-based testingpytest- Testing frameworkcoverage- Code coverage
PyYAML- YAML parsing/generationruamel.yaml- YAML parser with preservation of commentsjsonschema- JSON Schema validation
planout(Multiple languages) - A/B testing and experimentation frameworkopenapi-generator- Generate API clients and serversswagger-codegen- Another API development tool
# Core dependencies
pydantic>=2.0.0 # Schema definition/validation
networkx>=3.0 # Graph processing for dependencies
PyYAML>=6.0 # YAML handling
pyperplan>=1.0 # PDDL planning
jinja2>=3.0 # Code generation templates
# Optional but useful
hypothesis>=6.0 # Testing
pytest>=7.0 # Testing
sphinx>=5.0 # DocumentationThe framework builds upon established methodologies while optimizing for AI consumption:
- Architecture Description Languages (ADLs)
- C4 Model
- AWS Well-Architected Framework
- Domain-Driven Design
- COCOMO
- Function Point Analysis
- User Story Mapping
- INVEST Criteria
- HTN Planning
- STRIPS/PDDL
- Implement Project Definition schema
- Create basic Task Decomposition engine
- Develop simple AI Coding Agent interface
- Add Web Application domain
- Implement Microservices architecture
- Support REST API interfaces
- Enable AI to add new domains
- Allow AI to enhance decomposition rules
- Implement pattern learning from successful implementations
The framework is designed to be extended by the AI in several ways:
- New Domains
- Additional software domains
- Specialized industry sectors
- Emerging technology areas
- Architecture Patterns
- New architectural styles
- Domain-specific patterns
- Performance optimization patterns
- Quality Attributes
- Enhanced security patterns
- Scalability templates
- Reliability patterns
The system is designed to accept contributions from both human developers and AI agents. The AI can:
- Propose new domain templates
- Enhance decomposition rules
- Add implementation patterns
- Improve code generation strategies