When Natural Language Orchestrates Algorithms Through Constructive Thinking
Abstract
The evolution from Software 1.0 (explicit code) through Software 2.0 (neural networks) brings us to Software 3.0—a synthesis that transcends the false dichotomy of "code vs no-code." Drawing from SMD methodology and the Shchedrovitsky school, we present a three-layer framework: Constructive Thinking (world knowledge), Communication (planning and goal-setting), and Execution (collaborative action). We reveal the fundamental formula: Software 3.0 = Tools (Software 1.0) + World Knowledge for Software 2.0. Through the Agent Trinity—Operator, Agent, and Architect—we explore how this framework enables any domain where human intent must translate into action. When we use Software 3.0 to create Software 1.0, we call this "vibecoding," but the implications extend far beyond code to reshape how humans and AI collaborate across all fields.
1. The Evolution of Software Paradigms
Andrej Karpathy's classification provides our foundation. He identified Software 1.0 as the era when we wrote explicit instructions in code, Software 2.0 as when we trained neural networks on data, and Software 3.0 as when we converse with AI in natural language. But this progression needs refinement.
What Karpathy calls Software 3.0 is actually just the interface. The complete picture reveals a fundamental formula:
Software 3.0 = Tools (Software 1.0) + World Knowledge for Software 2.0
This formula captures the essential synthesis. We're not replacing code with conversation—we're creating systems where world knowledge expressed in natural language guides AI systems to orchestrate algorithmic tools.
Beyond the False Dichotomy
The "code OR no-code" debate fundamentally misunderstands what's happening. We're witnessing the emergence of "code AND no-code"—a synthesis where natural language provides world knowledge and orchestration, AI systems understand and plan using both this knowledge and their inherent capabilities, algorithmic tools execute precise actions, and the combination achieves what neither could alone.
This isn't about making programming easier. It's about making capability accessible across every domain where intent needs to become action.
2. The Three Layers: An SMD Framework
Drawing from SMD methodology and the Shchedrovitsky school of thought, Software 3.0 operates through three distinct but interconnected layers that work together to transform intent into action.
Top Layer: Constructive Thinking
The top layer contains world knowledge—the principles and instructions that define how the world works and how tools operate within it. This layer sets the overarching context of what's possible for the agent and encodes domain expertise as principles. It provides instructions for tool usage, establishes boundaries and constraints, and defines patterns and relationships. World knowledge isn't just documentation—it's the accumulated understanding that makes intelligent action possible.
Middle Layer: Communication
The communication layer is where planning happens. Here, the Operator communicates goals, intentions, and situational context while providing expected outputs and success criteria. The Agent uses world knowledge from the top layer combined with its LLM capabilities to create plans. Questions are asked, clarifications made, and plans confirmed through multimodal interfaces that enable rich interaction including text, voice, image, and interactive elements. This layer is fundamentally collaborative—a negotiation toward shared understanding.
Ground Layer: Execution
The execution layer is where plans become reality through collaboration. The Agent executes the plan using available tools while the Operator acts as an additional tool, providing real-time feedback, screenshots and logs, explanations of what they see when the Agent has limited visibility, and course corrections. The Operator monitors execution and can request return to the communication layer, with adjustments to plan, context, or expected output triggering layer transitions.
The Dynamic Flow
The layers aren't static—they're dynamically connected. Constructive Thinking provides world knowledge down to Communication, which has bidirectional flow with Execution for planning and replanning by Operator request. The Operator's ability to move between layers is crucial. When execution diverges from intent, the Operator doesn't just provide feedback—they can halt execution and return to communication for replanning.
3. The Agent Trinity
Three distinct roles emerge in Software 3.0 systems, each essential to the whole.
The Operator (O)
The Operator brings human judgment, domain expertise, and intent to the system. They set goals and provide context in the communication layer, define expected outcomes and success criteria, and act as a sensory tool during execution, providing feedback the Agent cannot obtain. They monitor execution for alignment with intent and decide when to return from execution to communication. The Operator works at the level of problems and outcomes, not implementations. Their expertise is in knowing what needs to be done, not how to do it technically.
The Agent (A)
The Agent bridges human intent and system capabilities. Crucially, the Agent is not just an LLM—it's the combination of LLM capabilities for understanding and reasoning, access to world knowledge from the constructive thinking layer, connection to tools for execution, and ability to maintain context across interactions. The Agent uses both world knowledge and its inherent LLM capabilities to create plans. This combination is what enables it to translate abstract goals into concrete actions.
The Architect (AA)
The Architect designs the environment where Operators and Agents collaborate. They create the world knowledge in the top layer, design and build necessary tools, reflect on whether world knowledge is adequate for agent planning, evaluate if available tools match task requirements, and evolve the Software 3.0 system based on usage patterns. The Architect must think systematically about the entire stack, understanding both human needs and AI capabilities while encoding this understanding into world knowledge.
4. Software 3.0: The Synthesis
Software 3.0 represents a true synthesis, not a progression or replacement. From Software 1.0, we inherit precise algorithmic tools, deterministic operations, performance-optimized functions, and existing APIs and services. From Software 2.0, we gain natural language understanding, pattern recognition, contextual reasoning, and adaptive behavior. The synthesis creates natural language as an orchestration layer, world knowledge as connective tissue, multimodal interfaces for rich interaction, and seamless integration of AI reasoning and algorithmic precision.
Why This Synthesis Matters
Each paradigm alone has limitations that the synthesis resolves. Software 1.0's rigid interfaces give way to natural language flexibility. Its steep learning curves are replaced by intuitive goal expression. Limited accessibility transforms into universal access through language. Software 2.0's tendency to hallucinate on precise tasks is addressed through algorithmic tool execution. Token limitations are overcome by efficient tool processing, and unpredictability is structured through world knowledge.
The Emergent Capabilities
Software 3.0 enables capabilities impossible in either paradigm alone: complex orchestrations through simple language, domain expertise encoded as reusable world knowledge, adaptive interfaces that respond to context, and seamless handling of both fuzzy and precise tasks.
5. World Knowledge: Principles and Instructions
World knowledge—the content of the constructive thinking layer—deserves deeper examination as it forms the foundation of intelligent action in Software 3.0.
Principles: The Why and When
Principles encode judgment about when to use different approaches. For instance, when handling structured data with large datasets, the principle might indicate that processing more than 100 records suggests tool usage because LLM token limits make direct processing inefficient, while for fewer than 10 records, direct LLM processing remains acceptable. Principles aren't rules—they're encoded wisdom that guides intelligent decision-making.
Instructions: The How
Instructions translate tool capabilities into actionable knowledge. They specify the purpose of tools, their inputs and outputs, usage patterns, and constraints. For example, a data analyzer's aggregate function would include details about performing statistical operations on datasets, the types of operations available, and when to use it in the workflow. Instructions bridge the gap between raw capability and practical application.
The Knowledge Synthesis
World knowledge combines principles and instructions into coherent understanding. Principles provide strategic guidance while instructions enable tactical execution. Together they form the "mental model" agents use for planning. This knowledge must be domain-specific, reflecting real-world constraints; tool-aware, understanding available capabilities; pattern-based, encoding successful approaches; and evolutive, improving through reflection.
6. The Communication Layer in Practice
The middle layer—communication—is where human intent transforms into actionable plans through rich, multimodal interaction.
Goal Articulation
Operators express goals in natural, domain-specific language. They might say "Analyze customer churn patterns from last quarter" or "Generate a compliance report for the new regulations" or "Optimize the production schedule for cost efficiency." No technical translation is required—the domain language is the interface.
Context Provision
Operators provide rich context that shapes planning. This includes situational constraints like maintenance windows, success criteria emphasizing accuracy over speed, available resources such as specific data centers, and domain knowledge about customer behavior patterns. Each piece of context helps the Agent create more effective plans.
Plan Creation
The Agent combines multiple sources to create plans. World knowledge from the top layer provides patterns and constraints. LLM capabilities enable understanding and reasoning. Tool awareness shapes what's possible. Operator context defines what's needed. This isn't template matching—it's intelligent synthesis that adapts to each unique situation.
Multimodal Negotiation
Communication happens through multiple channels. Text provides detailed explanations and requirements. Voice enables natural conversation for complex discussions. Images share screenshots, diagrams, and visual examples. Interactive elements allow direct manipulation of plans. Each modality serves different aspects of communication, creating richer understanding between Operator and Agent.
7. Execution as Collaboration
The ground layer transforms plans into reality through human-AI collaboration that goes beyond traditional automation.
The Agent's Execution Role
During execution, the Agent orchestrates tool usage according to the plan, maintains state across operations, handles errors and edge cases, provides progress updates, and requests Operator input when needed. It acts as an intelligent coordinator rather than a simple executor.
The Operator as Tool
Uniquely in Software 3.0, the Operator becomes a tool during execution. They provide sensory functions by offering visual feedback about what looks wrong, sharing system states like error dialogs, and explaining external constraints such as customer calls. They offer judgment functions through validating intermediate results, making subjective assessments, and providing domain interpretation. They maintain control functions by stopping execution when needed, requesting returns to planning, and approving critical operations.
The Feedback Loop
Execution isn't linear—it's a continuous feedback loop. Agent actions produce results that Operators observe and provide feedback on, leading to Agent adjustments. When significant deviations occur, the Operator can request a return to the Communication Layer. This loop enables real-time course correction while maintaining Operator control.
Layer Transitions
The Operator's ability to request layer transitions is crucial. When execution reveals planning gaps, they return to communication. When context changes mid-execution, they replan with new information. When unexpected results emerge, they adjust goals and expectations. These transitions aren't failures—they're the system working as designed, adapting to reality rather than forcing predetermined outcomes.
8. Vibecoding and Beyond
When we use Software 3.0 to create Software 1.0, I call this "vibecoding"—but it's just one application of the broader pattern that extends across all domains of human endeavor.
Vibecoding Defined
Vibecoding captures the intuitive, conversational nature of using Software 3.0 for software creation. The Operator expresses intent for software functionality. The Agent plans implementation using world knowledge and LLM capabilities. Execution generates actual code through tool orchestration. The Operator provides feedback on results. Iteration continues until working software emerges. The "vibe" captures the collaborative, intuitive flow—less commanding, more conversing.
Beyond Software: Universal Applications
The same pattern applies across domains. In medical diagnosis, a doctor describes unusual symptoms after patient travel, the Agent orchestrates diagnostic tools and literature searches, execution runs tests and analyzes results, and the doctor provides clinical observations and test interpretations. In financial analysis, an analyst requests evaluation of merger synergies, the Agent coordinates data retrieval and modeling, execution builds models and runs scenarios, and the analyst provides market insights and risk assessments. In creative production, a designer requests a campaign for sustainable fashion, the Agent orchestrates design tools and trend analysis, execution produces concepts and variations, and the designer provides brand alignment and aesthetic judgment.
The Universal Pattern
Across all applications, world knowledge enables domain-specific planning, communication establishes shared understanding, execution combines tools with human judgment, and layer transitions adapt to reality. This pattern works wherever human intent needs to become action.
9. The Architect's Evolution
The Architect role becomes increasingly crucial as Software 3.0 matures, representing the meta-level thinking that makes these systems possible.
Designing World Knowledge
Creating effective world knowledge requires domain expertise to deeply understand the field, pattern recognition to identify successful approaches, abstraction skills to encode specifics into principles, and tool awareness to know what's possible and practical. The Architect must balance generality with specificity, creating knowledge that guides without constraining.
Building Adequate Tools
The Architect must constantly evaluate whether current tools are sufficient for emerging needs, where LLM capabilities end and tools begin, how tools can be composed for complex tasks, and what new tools would unlock new capabilities. This requires understanding both the problem space and the solution space deeply.
The Reflection Practice
Critical to the Architect role is reflection. They observe actual usage patterns, identify where plans fail or struggle, recognize missing world knowledge, and spot tool gaps. This reflection drives evolution of the Software 3.0 system, creating a continuous improvement cycle.
Toward Self-Architecting Systems
An emerging possibility presents itself: can Agents become their own Architects? Early examples suggest they're beginning to recognize when new tools are needed, generate world knowledge from patterns, create instructions from successful executions, and propose system improvements. This meta-capability points toward systems that evolve and improve themselves.
10. Future Implications
Software 3.0's three-layer architecture points toward profound changes in how we create, share, and use capability.
Democratization Through Layers
Each layer democratizes different aspects of capability. Constructive Thinking makes domain expertise shareable. Communication replaces technical interfaces with natural language. Execution combines human judgment with AI capability. Together, they make sophisticated capability accessible to anyone who can articulate goals.
New Economic Models
The layered architecture enables new value creation through world knowledge markets for domain-specific principles and instructions, tool ecosystems providing specialized capabilities for different fields, architecture services offering expert design of Software 3.0 systems, and execution platforms providing infrastructure for human-AI collaboration.
The Meta-Question
As systems mature, fundamental questions emerge. Can Agents develop their own world knowledge? How do we validate automatically generated principles? What happens when execution patterns suggest new layer structures? Where does human judgment remain irreplaceable? These questions will shape the evolution of Software 3.0.
Ethical Considerations
The power of Software 3.0 raises important issues. Accountability asks who is responsible when world knowledge guides decisions. Transparency questions how we audit three-layer processes. Access concerns how we ensure equitable access to capabilities. Control examines how we maintain human oversight across layers. These aren't just technical questions—they're fundamental to how Software 3.0 integrates into society.
Conclusion
Software 3.0 represents neither evolution nor revolution—it's a synthesis that resolves the false dichotomy of code versus no-code. Through the three-layer architecture drawn from SMD methodology, we see how world knowledge guides AI systems to orchestrate tools, creating capabilities neither could achieve alone.
The layers—Constructive Thinking, Communication, and Execution—provide structure while enabling flexibility. The Agent Trinity—Operator, Agent, and Architect—defines roles that make this synthesis practical. Together, they create systems where natural language doesn't replace code but orchestrates it.
When we use Software 3.0 to create Software 1.0, we call it vibecoding. But that's just one facet of a broader transformation affecting every domain where human intent must become action. From medicine to finance, from engineering to art, the pattern remains consistent: world knowledge enables planning, communication establishes understanding, and execution combines AI capability with human judgment.
The Operator's ability to move between layers—particularly to request returns from execution to communication—maintains human agency while leveraging AI capability. The Architect's role in designing world knowledge and reflecting on system adequacy ensures continuous evolution.
As we enter the age of Software 3.0, the question isn't "can you code?" but "can you articulate what you want to achieve?" The barrier shifts from technical skill to clear communication and domain understanding. In this synthesis of code AND no-code, we find not compromise but multiplication of possibility.
Welcome to Software 3.0, where principles guide algorithms, where conversation orchestrates computation, and where the power to transform intent into action belongs to everyone who understands their domain.
"If you don't know what tool can do, you don't know what you can do."
In Software 3.0, this wisdom extends: If you don't have the world knowledge to guide tools, you cannot plan. If you cannot communicate intent, you cannot collaborate. If you cannot move between layers, you cannot adapt. The synthesis of code AND no-code isn't just about technology—it's about creating systems that think with us, plan with us, and act with us.