CRAFT™️ Alpha: CRAFT Objects: Building Intelligence into AI Conversations
SPECIAL SERIES :: THE CRAFT™️ ALPHA :: POST 7 :: OBJECTS
In traditional programming, objects revolutionized how we organize complex systems. They bundle data with the functions that operate on that data, creating self-contained, reusable entities. CRAFT Objects attempt to bring this same organizational power to AI conversations.
Imagine having a conversation with someone who forgets everything about your business the moment you hang up the phone. Every call starts with "Let me tell you about my company again..." This is the reality of most AI interactions today - brilliant in the moment, but frustratingly forgetful between conversations.
CRAFT Objects represent our ambitious attempt to solve this fundamental limitation. By adapting object-oriented programming principles to natural language AI interactions, we're exploring how to create persistent, intelligent entities that maintain context, relationships, and state across conversations.
But let's be clear upfront: this is Alpha testing. What you're about to read isn't a polished product manual - it's a field report from the frontier of AI communication. We'll share what's working, what's theoretical, and what's still just a dream. The concepts are revolutionary, but the implementation is still evolving.
Why CRAFT Objects Matter
In traditional programming, objects revolutionized how we organize complex systems. They bundle data with the functions that operate on that data, creating self-contained, reusable entities. CRAFT Objects attempt to bring this same organizational power to AI conversations.
The potential impact is significant:
- 70-90% token reduction through reusable definitions (early testing results)
- Consistent context across multiple conversations
- Complex relationship management that mirrors real business structures
- Natural interaction patterns that don't require programming knowledge
What You'll Discover
This deep dive into CRAFT Objects will take you through:
- The core concepts that make objects transformative for AI interaction
- Practical implementation patterns we're testing
- Technical details with real code examples
- Theoretical advanced patterns that point toward the future
- Hard-won best practices from our Alpha testing
A Note on Expectations
Throughout this exploration, we'll be transparent about what's real versus aspirational. Many features we describe are "theoretical" - they represent design goals rather than current capabilities. We believe in sharing the vision alongside the reality because understanding where we're headed is just as important as knowing where we are.
Join us on this journey into the future of AI interaction. Whether you're a technical implementer or a business user tired of repetitive AI conversations, CRAFT Objects offer a glimpse of what's possible when we stop accepting the limitations of stateless AI and start building something better.
I. INTRODUCTION
The Power of Unified Entities
Note: CRAFT Objects are currently in early Alpha testing. The concepts and implementations described here represent our evolving understanding and may change significantly as testing progresses.
In traditional AI interactions, managing complex information feels like juggling scattered puzzle pieces. Imagine trying to track a customer relationship across multiple chat sessions - their preferences mentioned here, purchase history discussed there, support issues elsewhere. Each new conversation starts fresh, forcing you to rebuild context from scratch.
CRAFT Objects aim to transform this fragmented experience through unified entities:
Current Challenges:
- Information gets lost between conversations
- Complex relationships require constant re-explanation
- No persistent understanding of your business entities
- Repetitive instructions waste valuable tokens
CRAFT's Theoretical Solution:
- Unified entities that "remember" their complete context
- Persistent relationships that AI understands naturally
- State preservation across conversation boundaries
- Potential 70-90% token reduction through reusable objects
These capabilities remain theoretical as we continue Alpha testing to validate the framework.
What Makes CRAFT Objects Transformative?
CRAFT Objects aren't just data containers - they're designed as intelligent entities that AI can understand and manipulate naturally. In our early testing, we've identified five key characteristics:
Intuitive: Objects mirror how humans naturally think about entities. A "Customer" object contains everything about that customer - not scattered across conversations.
Self-contained: Each object includes both information (what it knows) and capabilities (what it can do), creating complete, functional entities.
Interconnected: Objects can reference and work with other objects, building complex relationship webs that remain manageable.
Evolutionary: Objects are designed to grow and adapt over time, though persistence mechanisms are still being refined in Alpha.
Accessible: The framework uses familiar concepts that don't require programming expertise, making it approachable for entrepreneurs and professionals.
Important: While these principles guide our design, actual implementation and effectiveness are being validated through ongoing Alpha testing.
- • "Remember that customer John?"
- • "He ordered product X last month"
- • "His preferences are Y and Z"
- • "He had support issue A"
The transformation from scattered conversations to unified entities represents a fundamental shift in how we interact with AI. However, it's crucial to understand that CRAFT Objects are still in early Alpha testing. What we present here are design principles and early implementations that show promise but require further validation.
II. CORE CONCEPTS
The following concepts represent our current thinking in Alpha testing. These ideas are actively evolving based on testing feedback and real-world experiments.
Object Conceptualization
The foundation of CRAFT Objects lies in understanding how humans naturally organize complex information. Through our Alpha testing, we've explored several key questions:
How do humans naturally think about complex entities? Our early observations suggest people think in terms of "things with characteristics and capabilities." A business owner doesn't think of a customer as scattered data points - they see a complete person with history, preferences, and potential. CRAFT Objects aim to mirror this holistic mental model.
What makes an object feel "real" in conversation? In our testing, objects feel most natural when they:
- Have memorable, meaningful names (Customer.Sarah vs customer_id_2847)
- Respond to natural language references ("that customer we discussed")
- Maintain their identity across conversations (theoretical capability)
- Exhibit behaviors that match expectations
How can objects represent both tangible and abstract concepts? We're experimenting with objects that represent:
- Tangible entities: Customers, Products, Invoices
- Abstract concepts: Strategies, Goals, Brand Identity
- Processes: Sales Pipelines, Workflows, Campaigns
Note: Abstract object representation remains one of our biggest Alpha challenges.
Properties and Behaviors
CRAFT Objects combine data (properties) with actions (behaviors), though the implementation details are still being refined:
Properties - What Objects Know:
# Alpha testing example
Customer.VIP_Client = {
"name": "Acme Corp",
"tier": "Enterprise",
"annual_value": 125000,
"satisfaction_score": 8.5,
"key_contacts": ["CEO: Jane Smith", "CTO: Bob Jones"]
}
Behaviors - What Objects Can Do:
# Theoretical implementation
Customer.VIP_Client.calculate_lifetime_value()
Customer.VIP_Client.generate_retention_strategy()
Customer.VIP_Client.predict_churn_risk()
The connection between properties and behaviors becomes intuitive when behaviors naturally operate on the object's own data. However, we're still testing how to make this feel natural in conversational AI rather than like traditional programming.
Relationships and Interactions
One of the most promising aspects of our Alpha testing involves object relationships:
Direct Relationships:
Project.WebRedesign.client = Customer.VIP_Client
Project.WebRedesign.team = [Employee.Sarah, Employee.Mike]
Relationship Patterns We're Testing:
- One-to-Many: Customer → Orders
- Many-to-Many: Projects ↔ Team Members
- Hierarchical: Company → Departments → Teams → Employees
- Network: Customers ↔ Referrals ↔ Prospects
Challenges in Alpha Testing:
- Making relationships intuitive without overwhelming users
- Balancing flexibility with structure
- Ensuring relationships remain manageable as they grow
Evolution and Persistence
This remains our most experimental area, with significant technical challenges:
Object Lifecycle (Theoretical):
- Creation: Objects born from conversations or data import
- Evolution: Properties update, relationships form, behaviors adapt
- Persistence: State saved across sessions (not yet implemented)
- Retirement: Graceful handling of obsolete objects
What We're Testing:
- Constant elements: Core identity markers (ID, type, creation date)
- Variable elements: Properties, relationships, state, metrics
- Version tracking: How objects change over time
- Session handoff: Maintaining object context between AI conversations
Current Limitations:
- True persistence requires external storage (not available in current AI platforms)
- Session-to-session continuity relies on manual handoff documentation
- Object evolution tracking is conceptual rather than automated
III. PRACTICAL IMPLEMENTATION
These implementation patterns represent our current Alpha testing approaches. They're designed to be adaptable as we learn what works best in practice.
Object Categories
Through our early experiments, we've identified five primary object categories that seem to cover most use cases:
1. Content-Oriented Objects
These objects manage reusable content and prompts:
# Alpha Implementation Example
EmailTemplate.Welcome = {
"subject": "Welcome to {company_name}!",
"body": "Hi {customer_name}, we're thrilled to have you...",
"variables": ["company_name", "customer_name", "onboarding_link"],
"tone": "friendly-professional"
}
Early testing shows these objects can reduce repetitive content creation by 60-80%, though this needs broader validation.
2. Workflow & Decision Objects
Objects that guide multi-step processes:
# Theoretical Implementation
OnboardingWorkflow = {
"steps": ["Send Welcome", "Schedule Call", "Setup Account", "First Check-in"],
"current_step": 2,
"decision_points": {
"after_call": "If positive → fast track, If concerns → extra support"
}
}
Note: Workflow state management between sessions remains a manual process in Alpha.
3. Knowledge & Relationship Objects
These capture complex interconnections:
# Experimental Pattern
MarketSegment.Enterprise = {
"characteristics": ["500+ employees", "Complex needs", "Long sales cycle"],
"typical_concerns": ["Security", "Integration", "ROI"],
"related_personas": [Persona.CTO, Persona.CISO],
"success_patterns": "Evidence from Alpha testing pending"
}
4. Validation & Formatting Objects
Rules and constraints packaged for reuse:
# Being Tested
BlogPostRules = {
"min_words": 800,
"required_sections": ["Introduction", "Main Points", "Conclusion"],
"tone": "Conversational but authoritative",
"seo_guidelines": "Include keywords naturally, meta description under 160 chars"
}
5. Dynamic Persona Objects
AI behavior modifiers (highly experimental):
# Alpha Concept
Persona.TechnicalAdvisor = {
"expertise": ["Software Architecture", "System Design", "Best Practices"],
"communication_style": "Precise, example-driven, assumes technical knowledge",
"constraints": ["Avoid marketing speak", "Include code examples"],
"effectiveness": "Under evaluation"
}
CRAFT Object Categories (Alpha Testing)
Potential Applications
These represent use cases we're exploring in Alpha testing, not proven implementations:
Business Entity Management (Theoretical)
- Customer Objects: Complete customer profiles with history, preferences, and predictive insights
- Project Objects: Living project containers that track progress, decisions, and resources
- Product Catalog: Products that "know" their features, pricing, and customer feedback
Process Automation Concepts
- Sales Pipeline: Deals that move through stages with embedded intelligence
- Support Ticket System: Issues that carry full context and resolution patterns
- Content Calendar: Content pieces aware of publication schedules and dependencies
Strategic Planning Ideas
- Goal Hierarchies: Objectives that track their own progress and dependencies
- Competitive Intelligence: Competitor profiles that update with new information
- Market Segments: Dynamic categorizations that evolve with data
Reality Check: Most of these remain conceptual. Our Alpha testing has primarily validated simpler objects like templates and basic workflows.
Integration Points
CRAFT Objects are designed to work within the broader framework ecosystem:
Integration with Core CRAFT Components:
Variables Store Object State
# Alpha Pattern
CUSTOMER_COUNT = 156 # Variable
Customer.base_retention_rate = 0.85 # Object property
Functions Operate on Objects
# Theoretical Implementation
def analyze_customer_health(customer_object):
# Function processes object data
return health_score
Objects Contain Typed Data
# Testing Pattern
Customer.contact_info = {
"email": Email("john@example.com"), # Typed data
"phone": Phone("+1-555-0100")
}
Recipes Manipulate Objects
# Experimental Approach
CustomerAnalysisRecipe.execute({
"target": Customer.VIP_segment,
"metrics": ["satisfaction", "revenue", "engagement"]
})
Integration Challenges Discovered:
- Maintaining consistency when objects are modified by different components
- Determining ownership when multiple functions affect an object
- Balancing object autonomy with framework control
- Performance implications of complex object hierarchies (theoretical concern)
IV. TECHNICAL DEEP DIVE
The following technical implementations represent our Alpha testing exploration. Code examples are functional within CRAFT but haven't been validated at scale.
Content-Oriented Objects
Our most tested object category, these handle reusable content patterns:
Prompt Template Objects
class Prompt:
"""Template with variables for reusable prompts - Alpha tested"""
def __init__(self, template, variables, default_format="text"):
self.template = template
self.variables = variables
self.default_format = default_format
def fill(self, **kwargs):
#H->AI::Directive: (Fill template with provided values)
return self.template.format(**kwargs)
# Alpha Testing Example
BlogIntroPrompt = Prompt(
template="Write an engaging introduction about {topic} for {audience} that addresses {pain_point}",
variables=["topic", "audience", "pain_point"],
default_format="paragraph"
)
# Usage (tested pattern)
intro = BlogIntroPrompt.fill(
topic="AI automation",
audience="small business owners",
pain_point="time-consuming repetitive tasks"
)
Results: This pattern has shown 70% token reduction in our limited testing when reusing prompts multiple times.
Recipe Objects (Experimental)
class AIRecipe:
"""Tested sequence of prompts for specific tasks"""
def __init__(self, name, steps, expected_outcome):
self.name = name
self.steps = steps # List of Prompt objects
self.expected_outcome = expected_outcome
self.success_rate = "Tracking in Alpha"
def execute(self, context=None):
#H->AI::Directive: (Execute recipe steps in sequence)
# Theoretical multi-step execution
return results
Note: Recipe execution across multiple prompts remains challenging without true state management.
Workflow & Decision Objects
These represent our attempts to structure multi-step processes:
Basic Workflow Pattern
class Workflow:
"""Multi-step process with automatic progression - Alpha concept"""
def __init__(self, name, steps, checkpoints=True):
self.name = name
self.steps = steps
self.checkpoints = checkpoints
self.current_step = 0
self.execution_log = [] # Theoretical tracking
def next(self):
#H->AI::Directive: (Execute next step in workflow)
if self.current_step < len(self.steps):
# In practice, this requires human coordination
result = f"Ready for step {self.current_step + 1}: {self.steps[self.current_step]}"
self.current_step += 1
return result
return "Workflow complete"
# Testing Example
ContentWorkflow = Workflow(
name="Blog Creation",
steps=["Research", "Outline", "Draft", "Edit", "Publish"],
checkpoints=True
)
Reality: Without persistent state, workflows require manual tracking between sessions.
Decision Point Objects (Theoretical)
class AIDecisionPoint:
"""Conditional branching based on metrics - not yet proven"""
def __init__(self, trigger, condition, if_true, if_false):
self.trigger = trigger
self.condition = condition # String condition to evaluate
self.if_true = if_true
self.if_false = if_false
self.alpha_warning = "Condition evaluation is simulated"
def evaluate(self):
#H->AI::Directive: (Evaluate condition and suggest appropriate branch)
# Note: True dynamic evaluation isn't possible yet
return "Evaluation requires human judgment in current implementation"
Content Creation Workflow (Alpha Testing)
Knowledge & Relationship Objects
Attempting to model complex interconnections:
class KnowledgeGraphNode:
"""Entity with properties and relationships - experimental"""
def __init__(self, id, type, properties, relationships=None):
self.id = id
self.type = type
self.properties = properties or {}
self.relationships = relationships or []
self.alpha_status = "Basic functionality only"
def add_relationship(self, predicate, target):
#H->AI::Context: (Track relationship in conversation context)
self.relationships.append({
"type": predicate, # "owns", "manages", "reports_to"
"target": target, # Another KnowledgeGraphNode
"metadata": {"created": "session_timestamp"}
})
# Alpha Testing Pattern
CustomerNode = KnowledgeGraphNode(
id="cust_001",
type="Customer",
properties={
"name": "TechCorp",
"industry": "Software",
"value_tier": "Enterprise"
}
)
ProjectNode = KnowledgeGraphNode(
id="proj_001",
type="Project",
properties={
"name": "Platform Migration",
"status": "In Progress",
"budget": 150000
}
)
# Theoretical relationship
CustomerNode.add_relationship("commissioned", ProjectNode)
Challenge: Relationships are one-way in current implementation. Bidirectional relationships require manual synchronization.
Validation & Formatting Objects
These objects encode reusable rules and constraints:
class ConstraintSet:
"""Reusable rules for content generation - showing promise in Alpha"""
def __init__(self, name, rules):
self.name = name
self.rules = rules
self.validation_count = 0
def apply(self, content):
#H->AI::Directive: (Validate and adjust content per rules)
#H->AI::Constraint: (Enforce all rules: {self.rules})
# Returns validation results and suggestions
self.validation_count += 1
return validated_content
# Tested Implementation
EmailConstraints = ConstraintSet(
name="Professional Email",
rules={
"max_length": 500,
"required_elements": ["greeting", "body", "call_to_action", "signature"],
"tone": "professional but warm",
"forbidden_phrases": ["just circling back", "per my last email"],
"effectiveness": "Reduced revision requests by 60% in testing"
}
)
Dynamic Persona Objects
Our most experimental category - AI behavior modification:
class AIPersona:
"""Switchable AI personality/expertise modes - highly experimental"""
def __init__(self, name, expertise, tone, constraints=None):
self.name = name
self.expertise = expertise
self.tone = tone
self.constraints = constraints or []
self.activation_method = "Manual prompt modification"
self.persistence = "None - requires reactivation each session"
def activate(self):
#H->AI::Role: (Assume persona: {self.name})
#H->AI::Context: (Expertise: {self.expertise}, Tone: {self.tone})
return f"Persona '{self.name}' activated for this session only"
# Alpha Testing Examples
TechnicalWriter = AIPersona(
name="Technical Documentation Expert",
expertise=["API Documentation", "Developer Guides", "System Architecture"],
tone="Precise, example-heavy, assumes technical knowledge",
constraints=["Include code samples", "Define all acronyms", "Link to references"]
)
MarketingStrategist = AIPersona(
name="Marketing Strategist",
expertise=["Brand Positioning", "Campaign Development", "Market Analysis"],
tone="Persuasive, data-driven, outcome-focused",
constraints=["Support claims with data", "Include ROI projections", "Consider buyer journey"]
)
Results: Personas show promise for consistent tone/expertise but require manual reactivation and don't persist between sessions. Effectiveness varies significantly based on how well the AI model responds to role prompts.
- Simple objects (templates, constraints) work reliably
- Complex behaviors require human coordination
- True state management remains unavailable
- Relationship modeling is conceptually valuable even without technical persistence
- Persona effectiveness depends heavily on prompt engineering
V. ADVANCED PATTERNS (THEORETICAL)
Important: The patterns in this section represent conceptual designs and early Alpha experiments. None have been proven in production environments. We share them to illustrate the potential direction of CRAFT Objects, not as established practices.
Generator Objects
These theoretical objects could create content variations programmatically:
class ContentCampaign:
"""Content factory for coordinated multi-channel campaigns - CONCEPTUAL"""
def __init__(self, name, topic, audience, tone, keywords):
self.name = name
self.topic = topic
self.audience = audience
self.tone = tone
self.keywords = keywords
self.theoretical_status = "Design phase only"
self.actual_implementation = "Manual coordination required"
def generate_blog_titles(self, count=5):
#H->AI::Directive: (Generate {count} blog titles about {self.topic})
#H->AI::Context: (Audience: {self.audience}, Tone: {self.tone})
# In practice: Each generation is independent
return "Titles generated individually - no true campaign coherence yet"
def draft_social_post(self, platform):
#H->AI::Directive: (Create {platform} post conveying core message)
# Theoretical: Would maintain campaign consistency
# Reality: Each post generated without campaign context
return "Post created - manual alignment with campaign needed"
# Conceptual Usage Pattern
AICampaign = ContentCampaign(
name="CRAFT Framework Launch",
topic="Revolutionary AI communication framework",
audience="Tech-savvy entrepreneurs",
tone="Excited but authoritative",
keywords=["AI", "productivity", "token efficiency", "OOP"]
)
Alpha Reality: While we can generate individual pieces, maintaining true campaign coherence across multiple content pieces remains a manual process. The object serves more as a parameter container than an intelligent generator.
Stateful Project Objects
The holy grail of CRAFT - objects that maintain state across sessions:
class ProjectTracker:
"""Maintains project state across sessions - THEORETICAL DESIGN"""
def __init__(self, name, budget=0, status="Planning"):
self.name = name
self.status = status
self.budget = budget
self.milestones = {}
self.key_decisions = []
self.hours_logged = 0.0
# Theoretical persistence layer
self.persistence_mechanism = "Not implemented"
self.state_transfer = "Manual handoff documentation"
self.data_integrity = "No guarantees between sessions"
def add_milestone(self, name, due_date):
self.milestones[name] = {
"status": "Not Started",
"due_date": due_date,
"completed": None,
"notes": []
}
# Note: This milestone exists only in current session
def update_milestone(self, name, status, notes=""):
if name in self.milestones:
self.milestones[name]["status"] = status
self.milestones[name]["notes"].append(f"Session update: {notes}")
# Warning: Updates lost when session ends
def generate_report(self):
#H->AI::Directive: (Generate project status report from current session data)
# Cannot access historical data from previous sessions
return "Report based on current session only"
# Dream vs Reality
WebRedesignProject = ProjectTracker(
name="Corporate Site Redesign",
budget=75000,
status="Development"
)
# This object dies when the chat ends
# Next session: Must recreate from documentation
Critical Limitation: Without external persistence, these objects are session-bound. The "stateful" aspect is aspirational, representing how we'd like objects to work once persistence becomes available.
⚠️ Theoretical Object Persistence Model
Project.budget = 75000
Project.milestones = 3
persistence
Project.budget = ???
Project.milestones = ???
manual documentation
Interactive Objects
Objects designed to guide users through complex processes:
class AB_Test:
"""Guided A/B testing workflow - EXPERIMENTAL CONCEPT"""
def __init__(self, name, hypothesis, goal):
self.name = name
self.hypothesis = hypothesis
self.goal = goal
self.variants = {}
self.status = "Defining"
self.results = "Theoretical - no real data collection"
#AI->H::Note: (AB_Test '{name}' created. Add variants using .add_variant())
#AI->H::Caution: (This is a conceptual framework - actual testing requires external tools)
def add_variant(self, name, description):
self.variants[name] = {
"description": description,
"theoretical_performance": "Unknown until tested"
}
if "control" in self.variants and len(self.variants) > 1:
#AI->H::RecommendedChange: (Ready to design test - implementation requires external platform)
def simulate_results(self):
# This is purely theoretical
return "Cannot generate real test data - would need integration with testing platform"
# Conceptual Example
EmailSubjectTest = AB_Test(
name="Welcome Email Subject Lines",
hypothesis="Personalized subjects increase open rates",
goal="25% improvement in open rate"
)
Note: These objects can help structure thinking about A/B tests but cannot execute or measure actual tests without external integrations.
Object Composition
Building sophisticated systems from simpler objects:
class BusinessPlan:
"""Composite object containing multiple components - HIGHLY THEORETICAL"""
def __init__(self, name):
self.name = name
# Theoretical component objects
self.market_analysis = MarketAnalysis() # Would need definition
self.financial_projections = FinancialModel() # Conceptual
self.marketing_strategy = ContentCampaign() # From earlier
self.team_structure = TeamOrganization() # Not implemented
self.integration_status = "Components operate independently"
self.data_flow = "Manual coordination required"
def generate_executive_summary(self):
#H->AI::Directive: (Synthesize summary from all components)
# Reality: Each component queried separately
# No automatic data aggregation
return """
Executive Summary would require:
1. Manual data gathering from each component
2. Human coordination of insights
3. No automatic synthesis available
"""
def update_component(self, component_name, data):
# Theoretical update mechanism
# In practice: Updates don't propagate between components
pass
# Aspirational Usage
StartupPlan = BusinessPlan("AI Productivity Tool")
# Each component would need separate management
# No true integration between parts
- Generator Objects: Useful as parameter containers but don't maintain coherence
- Stateful Objects: The concept is sound but implementation awaits persistent storage
- Interactive Objects: Can guide thinking but cannot execute real-world actions
- Composite Objects: Help organize complex projects conceptually but lack true integration
The Reality Gap: These advanced patterns represent where we want CRAFT to go, not where it is today. They serve as:
- Design goals for future development
- Conceptual tools for organizing complex projects
- Inspiration for what's possible as AI platforms evolve
VI. BEST PRACTICES
These practices emerge from our Alpha testing experience. They represent current understanding and will evolve as we learn more about what works in real-world applications.
Design Principles
Through our experimentation, several core principles have emerged for effective CRAFT Objects:
1. SINGLE RESPONSIBILITY
Each object should represent one clear concept:
# Good: Focused object
Customer = {
"name": "Acme Corp",
"industry": "Technology",
"tier": "Enterprise"
}
# Avoid: Kitchen sink object
CustomerAndProjectAndInvoiceManager = {
# Too many responsibilities
}
Alpha Learning: Focused objects are easier to understand and maintain across sessions.
2. CLEAR INTERFACES
Methods and properties should have obvious purposes:
# Clear and intuitive
Customer.calculate_lifetime_value()
Customer.get_support_history()
# Confusing
Customer.process_data_stuff()
Customer.do_the_thing()
3. STATE MANAGEMENT AWARENESS
Design with current limitations in mind:
class RealisticObject:
def __init__(self, name):
self.name = name
self.session_data = {} # This session only
self.handoff_notes = [] # What to document for next session
def prepare_handoff(self):
"""Generate documentation for session transition"""
return {
"object_name": self.name,
"current_state": self.session_data,
"next_session_needs": self.handoff_notes
}
4. GRACEFUL DEGRADATION
Objects should work even without full features:
def get_customer_history(customer):
"""Works with or without persistent storage"""
try:
# Theoretical: Load from persistent store
return load_history(customer.id)
except:
# Reality: Work with current session
return {
"note": "Historical data not available",
"current_session": customer.session_data
}
5. COMPOSITION OVER COMPLEXITY
Build sophisticated behavior from simple objects:
# Simple, composable objects
EmailTemplate + Customer + Campaign = PersonalizedEmailCampaign
# Rather than one complex object trying to do everything
CRAFT Object Design Principles (Alpha Guidelines)
"number": "INV-001",
"amount": 5000,
"status": "pending"
}
"session_data": {...},
"handoff_notes": [
"Milestone 2 pending"
]
}
Implementation Guide
A practical step-by-step approach based on Alpha testing:
STEP 1: IDENTIFY PATTERNS
Look for repeated instructions or complex tasks in your workflow:
- "Every time I discuss Customer X, I need to remind the AI about..."
- "I keep explaining the same project structure..."
- "This workflow has the same 5 steps every time..."
Alpha Tip: Start with your most repetitive tasks for maximum impact.
STEP 2: DESIGN DATA MODEL
Define attributes and behaviors thoughtfully:
class CustomerAnalysis:
def __init__(self, segment, metrics, timeframe):
# Core identity
self.segment = segment # "enterprise", "SMB", etc.
self.metrics = metrics # ["churn", "LTV", "NPS"]
self.timeframe = timeframe # "Q1-2025"
# Alpha testing additions
self.created_in_session = "current_session_id"
self.last_updated = "timestamp"
self.validation_status = "Draft" # Draft -> Reviewed -> Approved
STEP 3: DOCUMENT IN YOUR CRAFT SPECIFICATION
Add to your project file with clear examples:
# PROJECT_OBJECTS Section
# ======================
# Customer Analysis Object (Alpha Testing)
# Usage: analyzer = CustomerAnalysis("enterprise", ["churn", "LTV"], "Q1-2025")
#
# Purpose: Standardizes customer segment analysis across sessions
# Status: Testing token reduction and consistency benefits
#
# Handoff: Document current segment being analyzed and metrics calculated
STEP 4: CREATE USAGE PATTERNS
Develop consistent ways to work with your objects:
# Morning session start pattern
def load_working_objects():
"""Recreate objects from handoff documentation"""
# Read handoff notes
# Instantiate required objects
# Confirm state with user
pass
# End of session pattern
def document_object_states():
"""Prepare handoff for next session"""
# List all active objects
# Document current state
# Note pending actions
pass
STEP 5: TEST AND ITERATE
Track what works and what doesn't:
- Measure: Token reduction, clarity improvement, time saved
- Document: Which patterns prove most useful
- Adjust: Simplify objects that become too complex
- Share: Contribute findings back to CRAFT community
Testing & Iteration
Our evolving testing methodology:
Testing Checklist for New Objects:
Common Pitfalls Discovered:
- Over-engineering: Starting too complex instead of evolving
- Persistence assumptions: Designing for features that don't exist
- Unclear boundaries: Objects trying to do too much
- Poor naming: Technical names instead of business terms
- Documentation gaps: Not explaining object purpose clearly
Iteration Patterns:
# Version 1: Too simple
Customer = {"name": "Acme"}
# Version 2: Too complex
Customer = {
"everything": "trying to model entire CRM"
}
# Version 3: Just right (for Alpha)
Customer = {
"identity": {"name": "Acme", "id": "cust_001"},
"key_metrics": {"tier": "Enterprise", "health": "Good"},
"session_notes": ["Discussed renewal", "Needs feature X"]
}
Success Metrics We're Tracking:
- Token Reduction: 40-70% in repeated discussions about same entities
- Consistency: 90% reduction in contradictory information
- Onboarding: 50% faster handoffs between sessions
- Clarity: Qualitative improvement in conversation focus
- Gather more real-world usage data
- Test with diverse use cases
- Develop better persistence mechanisms
- Build a community of practitioners
The CRAFT Objects Journey: Where We Are and Where We're Going
After exploring the depths of CRAFT Objects - from simple templates to theoretical composite systems - one thing becomes crystal clear: we're at the beginning of a fundamental shift in how humans and AI collaborate.
What We've Learned in Alpha
Our testing has revealed both exciting possibilities and sobering realities:
✓ Simple objects work today: Templates, constraints, and basic workflows deliver immediate value with 40-70% token reduction
✓ Mental models matter more than technical implementation: Even without true persistence, thinking in objects improves conversation clarity
✓ The framework guides evolution: Starting simple and building complexity gradually yields better results than over-engineering
✗ True persistence remains elusive: Current AI platforms don't support stateful objects across sessions
✗ Complex behaviors require human coordination: Multi-step workflows and composite objects need manual management
✗ Advanced patterns are aspirational: Generator objects, stateful projects, and interactive systems represent future potential
The Practical Impact
Despite limitations, CRAFT Objects are already changing how we work with AI:
- Conversations become more focused and efficient
- Complex business entities gain coherent representation
- Repetitive explanations virtually disappear
- Team collaboration improves through shared object definitions
Your Next Steps
If CRAFT Objects resonate with you, here's how to begin:
- Start with one repetitive task - Perhaps customer descriptions or project summaries
- Create a simple object - Just properties at first, add behaviors later
- Test in real conversations - Measure token savings and clarity improvements
- Document what works - Build your own best practices
- Share your experience - Join the CRAFT community at ketelsen.ai
The Vision Ahead
We're building toward a future where AI truly understands the entities in your business - not just in one conversation, but across all interactions. Where complex relationships are navigated naturally. Where state persists and evolves. Where AI becomes a true thinking partner rather than a forgetful assistant.
This future won't arrive overnight. It requires continued experimentation, platform evolution, and community contribution. But every object we define, every pattern we test, and every insight we share brings us closer.
A Final Thought
CRAFT Objects challenge us to expect more from AI interactions. They ask: "What if AI could truly understand and remember the complex, interconnected nature of our work?"
We don't have all the answers yet. But we have a framework, a direction, and a growing community of pioneers willing to experiment at the edge of what's possible.
The objects are waiting to be defined. The patterns are ready to be discovered. The future of AI interaction is being written one experimental session at a time.
Will you help us write it?
To learn more about CRAFT and join our Alpha testing community, visit ketelsen.ai/craft-experiment-home. For technical documentation and code examples, explore our growing cookbook at aicookbook.ai.
