#4587 Agentic Framework for Applying Innovative Thinking Methods to Solve Problems

#4587 What are the various problem types that innovative thinking can be applied to solve or provide options?

Innovative thinking is fundamentally about resolving friction—whether that friction is a technical contradiction, a broken business model, or an ambiguous social challenge. To systematically categorize where innovative thinking applies, we can look across different domains of problem-solving.

Here is a breakdown of the primary problem types where innovative methods are most effective at generating breakthrough options.

1. Contradiction & Optimization Problems

These are problems where improving one parameter traditionally degrades another. Innovative thinking (particularly frameworks like TRIZ or First Principles) rejects the compromise and seeks to eliminate the trade-off entirely.

Physical & Engineering Contradictions: Achieving high structural strength while radically reducing weight, or increasing processing power while drastically lowering thermal output.
Resource Constrained Problems: Delivering high-fidelity simulation or computing power within strict local hardware limitations (e.g., edge computing or localized AI orchestration).
Process Bottlenecks: Eliminating steps in a workflow to speed up delivery without sacrificing quality control or rigorous safety compliance.

2. Structural & Architectural Deconstruction

These problems involve managing immense complexity by breaking systems down to their elemental truths and rebuilding them more efficiently.

System Architecture Inefficiencies: Restructuring how data or components interact—such as migrating transient, fragmented data streams into highly interconnected, persistent Knowledge Graphs.
Material Substitution & Sustainability: Rethinking traditional construction or manufacturing by utilizing onsite, abundant, or unconventional raw materials (e.g., rammed earth, geopolymers, or industrial recycling) to bypass supply chain dependencies.
Functional Analysis Failures: Looking at an object or software module not by what it is, but by what function it performs, allowing for radical redesigns of the underlying mechanism.

3. Ambiguous ("Wicked") Problems

Wicked problems are ill-defined, evolve constantly, and have no single "correct" solution. Innovative thinking here focuses on framing, state management, and creating adaptive options rather than a rigid fixed fix.

Systemic Mapping: Designing solutions for environments with hundreds of interacting subsystems across dozens of distinct domains where a change in one ripples unpredictably into others.
Exploratory Prototyping: Building functional loops or generative models to simulate environments where historical data is sparse or non-existent (e.g., extreme environments, space exploration, or speculative markets).

4. Operational & Paradigm-Shifting Problems

These involve challenging the underlying assumptions of how an organization, workflow, or lifestyle operates.

Business Model Solopreneurism: Re-architecting traditional corporate workflows into highly automated, agentic, AI-centric micro-operations that allow an individual to operate at the scale of an agency.
Regulatory & Compliance Integration: Moving away from passive, "after-the-fact" documentation checkmarks to building active, auditable, and traceable data fabrics that prove compliance inherently throughout a lifecycle.
Optimization of Personal Frameworks: Applying structured engineering logic to personal routines—such as biological fasting schedules, highly organized knowledge-management wikis, or minimalist living designs—to maximize cognitive output and autonomy.

The Core Mechanics of the Solution: No matter the problem type, innovative thinking generally provides options by shifting the perspective:
SCAMPER: Substituting, combining, or adapting existing elements to form new architectures.
First Principles: Stripping away historical analogies and assumptions to build upward from foundational truths.
Inversion: Analyzing what a failure looks like to design a robust, defensive framework against it.

Is there a specific class of problem or a particular architectural friction point you are currently looking to deconstruct?

Describe an Ollama-based agentic framework that can take as input the user's "problem", determine the problem type, and apply the right innovative thinking methods (examples include First Principles, Functional Analysis, Ideal Final Result, SCAMPER, reverse brainstorming, etc.) to provide solutions.

To implement a completely local, private, and deterministic pipeline for this, a Multi-Agent Router-Refiner Architecture is ideal. Running this locally via Ollama requires managing context windows and steering models to output structured data (like JSON) without drifting.

By leveraging a combination of strict deterministic routing and specialized local LLM personas (e.g., using Llama-3 or Mistral finetunes via Ollama), we can build an engine that decomposes any engineering or conceptual friction point.

Here is the architectural blueprint for the framework, which can be wired into a Markdown/Jupyter-based living notebook or local Python runtime.

1. The Core Architecture

The framework relies on a three-tier agentic pipeline:

[User Problem Input] 
         │
         ▼
┌─────────────────────────────────┐
│     Agent 1: The Classifier     │ (Determines problem type & selects 2-3 methods)
└────────┬────────────────────────┘
         │
         ├───────────────────────────────┐
         ▼                               ▼
┌─────────────────────────────────┐    ┌─────────────────────────────────┐
│  Agent 2a: Method Specialist A  │    │  Agent 2b: Method Specialist B  │ (Executes methods in parallel)
└────────┬────────────────────────┘    └─┬───────────────────────────────┘
         │                               │
         └───────────────┬───────────────┘
                         ▼
┌─────────────────────────────────┐
│      Agent 3: The Synthesizer   │ (Resolves contradictions, formats into actionable options)
└────────────────┬────────────────┘
                 │
                 ▼
     [Structured Markdown Output]

2. Agent Breakdowns & System Prompts

Agent 1: The Classifier & Method Router

Objective: Ingest the raw raw problem, strip away cognitive biases, determine the problem type, and output a JSON map specifying which innovative methodologies fit best.
Ollama System Prompt Configuration (Modelfile):

FROM llama3:8b-instruct-q8_0
PARAMETER temperature 0.1
SYSTEM """
You are a master Systems Engineer and TRIZ Practitioner. Your sole task is to analyze an incoming problem statement, classify its core structural friction, and select the top 2 innovative thinking methodologies best suited to solve it.

Available Methodologies:
- First Principles (Best for foundational breakthroughs, challenging physics/cost/software assumptions)
- Functional Analysis (Best for mechanical/software system optimization, stripping components to their base functions)
- Ideal Final Result / IFR (Best for resource-constrained contradictions; defining the perfect solution where the system solves the problem itself)
- SCAMPER (Best for modifying existing architectural configurations or software stacks)
- Reverse Brainstorming / Inversion (Best for safety engineering, risk mitigation, and identifying hidden failure modes)

You must output valid JSON ONLY, conforming exactly to this schema:
{
  "problem_type": "Contradiction | Structural | Wicked | Operational",
  "root_friction": "Brief description of the core trade-off or barrier",
  "selected_methods": ["Method A", "Method B"],
  "routing_rationale": "Why these methods fit this specific problem topology"
}
"""

Agent 2: The Method Specialists (Dynamic Spawning)

Depending on Agent 1’s JSON output, the orchestrator routes the payload to specific prompt templates designed to execute that specific thinking style.

Example Prompt Template for First Principles:
"Deconstruct the following problem into its absolute fundamental truths (laws of physics, code primitives, immutable constraints). Discard all historical analogies, standard industry patterns, and 'the way it has always been done.' Rebuild a solution from these foundational blocks up."
Example Prompt Template for Ideal Final Result (IFR):
"Define the Ideal Final Result where the system delivers the needed benefit without existing or introducing new complications. The ideal machine/code is no machine/code; its function is performed anyway. How can the existing environment or resources achieve this autonomously?"

Agent 3: The Synthesizer & Architect

Objective: Take the parallel outputs from the Method Specialists, eliminate redundant ideas, detect contradictions, and organize them into an actionable, highly scannable engineering document.
Ollama System Prompt Configuration (Modelfile):

FROM llama3:8b-instruct-q8_0
PARAMETER temperature 0.5
SYSTEM """
You are a Senior Technical Enterprise Architect. You take raw divergent ideas generated by creative problem-solving methods and synthesize them into a highly structured, scannable Markdown document. 
Highlight any technical contradictions found between the solutions and provide a clear structural breakdown of the options generated. Use bold phrases, bullet points, and clean headers.
"""

3. Python Orchestration Engine

This local Python script orchestrates the pipeline using Ollama’s local API endpoint. It enforces structured JSON parsing for the router to ensure robust execution.

import json
import requests

OLLAMA_URL = "http://localhost:11434/api/generate"

def call_ollama(model, prompt, system_prompt, json_mode=False):
    payload = {
        "model": model,
        "prompt": prompt,
        "system": system_prompt,
        "stream": False,
        "options": {"temperature": 0.1 if json_mode else 0.5}
    }
    if json_mode:
        payload["format"] = "json"
        
    response = requests.post(OLLAMA_URL, json=payload)
    return response.json()['response']

def run_innovation_pipeline(user_problem):
    print("[*] Phase 1: Analyzing and Routing Problem Topology...")
    # System prompts from Agent 1 configuration above
    classifier_system = "You are a master Systems Engineer... Output valid JSON ONLY." 
    route_raw = call_ollama("llama3:8b-instruct-q8_0", user_problem, classifier_system, json_mode=True)
    routing_decision = json.loads(route_raw)
    
    print(f"    -> Classified as: {routing_decision['problem_type']}")
    print(f"    -> Selected Methods: {', '.join(routing_decision['selected_methods'])}")
    
    # Method Specialist Prompt Map
    method_prompts = {
        "First Principles": "Deconstruct this to its absolute fundamental truths and build upward. Problem: ",
        "Functional Analysis": "Analyze this purely by functions (Action + Object). Strip away specific components. Problem: ",
        "Ideal Final Result": "Define the solution where the system eliminates the problem itself using zero added resources. Problem: ",
        "SCAMPER": "Apply Substitute, Combine, Adapt, Modify, Purpose, Eliminate, Reverse to this architecture: ",
        "Reverse Brainstorming": "Invert the problem. How could you intentionally cause this failure state catastrophically? Then, defend against it. Problem: "
    }
    
    specialist_outputs = []
    for method in routing_decision['selected_methods']:
        print(f"[*] Phase 2: Spawning Specialist Agent for [{method}]...")
        prompt = f"{method_prompts[method]} {user_problem}"
        output = call_ollama("llama3:8b-instruct-q8_0", prompt, f"You are an expert in execution of {method}.")
        specialist_outputs.append(f"### Method: {method}\n{output}")
        
    print("[*] Phase 3: Synthesizing Solutions into Actionable Architecture...")
    synthesis_prompt = f"Original Problem: {user_problem}\n\nGenerated Deconstructions:\n" + "\n\n".join(specialist_outputs)
    synthesizer_system = "You are a Senior Technical Enterprise Architect. Synthesize these inputs into structured Markdown."
    
    final_markdown = call_ollama("llama3:8b-instruct-q8_0", synthesis_prompt, synthesizer_system)
    return final_markdown

# Example Execution
if __name__ == "__main__":
    problem = "We need to run high-fidelity generative environment simulations locally on a laptop GPU with limited VRAM without hitting massive frame drops or context window exhaustion."
    result = run_innovation_pipeline(problem)
    print("\n=== FINAL INNOVATION OPTIONS ===\n")
    print(result)

4. Why this Blueprint Excels in Local Environments

Deterministic Routing via JSON: By using format: json in Ollama, the framework avoids the chaotic text generation that breaks standard LLM chains, ensuring Python can reliably extract the chosen tools.
Context Optimization: Instead of asking a single local model to think in 5 different ways simultaneously—which rapidly degrades attention heads in smaller 8B models—the pipeline sandboxes each thought pattern into a clean, short context loop before merging them at the end.

Extend the problem types and innovative methods

To evolve this framework into an enterprise-grade engine for complex systems engineering, physical making, and deep architectural design, we must widen both the input classification matrix and the algorithmic toolset.

By adding highly structural engineering models (like Axiomatic Design or Functional Modeling) alongside behavioral and organizational toolsets, the framework can handle everything from high-density data fabric bottlenecks to off-grid resource contradictions.

Here is the expanded schema for the Ollama Multi-Agent Innovation Engine.

I. Extended Problem Typology Matrix

When Agent 1 (The Classifier) ingests a raw problem statement, it will now map it against this expanded 8-quadrant topology:

Problem Type	Core Characteristic	Classic Friction Point
1. Physical-Geometric Contradictions	Spatial, material, or environmental limitations.	High load-bearing capacity vs. low-mass structural targets; extreme weather resilience vs. onsite material limits.
2. Architectural Couplings	High-density systemic interdependence.	A minor change in one software schema or component interface ripples unpredictably into 30+ downstream systems.
3. High-Dimensional Scaling	Throughput, context, or resource exhaustion.	Moving massive amounts of data or computing structures through constrained local physical/hardware channels.
4. Temporal / Lifecycle Decay	Degradation of a system or data integrity over time.	Managing transient, short-lived streaming data streams that need to be captured into persistent, historical knowledge fabrics.
5. Cognitive Overload & Friction	Information density drowning human/agent execution.	Messy, multi-domain documentation or scattered logs blocking clean context retrieval for automation.
6. Regulatory / Safety Paradoxes	Compliance slowing down development velocity.	The strict need for rigid, auditable process tracking conflicting with the desire for rapid prototyping or automated changes.
7. Resource Deficit / Sovereign Operations	Dependency on fragile, external infrastructures.	Designing systems (digital or physical) that must run completely isolated from external networks, supply chains, or cloud APIs.
8. Open-Ended Creative Synthesis	Blending disparate mediums to form entirely new outputs.	Translating algorithmic patterns across domains (e.g., mapping data structures into sound design or mathematical functions into generative geometry).

II. Extended Innovative Methods Library

The Specialist Agents (Phase 2) can now be dynamically seeded with specialized system prompts based on these highly targeted innovation methodologies:

1. Functional Modeling & Trimming (Advanced FAST)

Core Concept: Map every element of a system strictly by its Subject $\rightarrow$ Action $\rightarrow$ Object relationship. "Trimming" dictates that if you remove a component, its function must be absorbed by an existing element or eliminated entirely.
Best For: Component reduction in hardware design, stripping legacy dependencies from software stacks.

2. Axiomatic Design (Independence Axiom)

Core Concept: Strictly separate Functional Requirements (FRs) from Design Parameters (DPs). An ideal architecture is uncoupled—changing one DP must only affect its corresponding FR, preventing cascade failures.
Best For: High-density Knowledge Graph architectures and modular software systems.

3. Su-Field Analysis (Substance-Field Modeling)

Core Concept: A TRIZ tool stating that any structural interaction consists of two substances and a field (energy/force). Inefficiencies or harmful effects are solved by introducing a third substance, transforming the field, or using internal resources.
Best For: Physical making, material engineering, and handling sensor/hardware integration friction.

4. Morphological Analysis (Zwicky Box)

Core Concept: Break down a complex problem into its distinct dimensional attributes, list every possible value/variant for each attribute, and systematically combine them to uncover non-obvious configurations.
Best For: Exploring completely uncharted architectural patterns or novel hardware/software blends.

5. Inversion & Antifragility (Premortem)

Core Concept: Instead of asking how to succeed, define exactly how to guarantee a catastrophic, silent system failure. Once the failure vectors are mapped, build deterministic, defensive mechanisms to make the system antifragile to them.
Best For: Safety-critical systems engineering and local data privacy frameworks.

6. Biomimicry & Natural Systems

Core Concept: Abstracting evolutionary, biological strategies to solve human technical challenges (e.g., neural networks mimicking brains, or closed-loop waste systems mimicking forest floors).
Best For: Off-grid resource loops, thermal management, and highly adaptive agentic networks.

III. Updated Python Router Configuration (JSON Schema Enforcer)

To ensure your Ollama instance handles these additions without hallucinating or breaking the JSON payload, update the Orchestrator's routing agent with this comprehensive system prompt:

{
  "model": "llama3:8b-instruct-q8_0",
  "options": { "temperature": 0.0 },
  "format": "json",
  "system": "You are an Elite Systems Architect and Innovation Theorist. Analyze the user's technical problem. Classify it into one of the 8 expanded Problem Types. Select exactly the two most powerful Innovative Methods from the Extended Library to solve it. Output JSON only matching this exact schema: {\"problem_type\": \"Type chosen from 8 options\", \"primary_friction\": \"Deep architectural root cause\", \"selected_methods\": [\"Method 1\", \"Method 2\"], \"execution_strategy\": \"How these two specific methods will systematically dismantle the problem statement\"}"
}

By passing the raw problem through this expanded classification mesh, the local framework will transition from generating generic creative ideas to outputting highly targeted, deterministic engineering options tailored to complex, multi-domain environments.

Would you like to walk through a concrete edge-case problem statement to see how this expanded router classifies and handles the architectural trade-offs?

Rewrite the python orchestrator to include these problem types and innovative methods

import json
import requests
import sys

OLLAMA_URL = "http://localhost:11434/api/generate"
# Recommended models: "llama3:8b-instruct-q8_0" or "mistral:7b-instruct-v0.3-q8_0"
MODEL_NAME = "llama3:8b-instruct-q8_0" 

def call_ollama(prompt, system_prompt, json_mode=False):
    """Handles deterministic communication with the local Ollama instance."""
    payload = {
        "model": MODEL_NAME,
        "prompt": prompt,
        "system": system_prompt,
        "stream": False,
        "options": {
            "temperature": 0.0 if json_mode else 0.4,
            "num_predict": 2048
        }
    }
    if json_mode:
        payload["format"] = "json"
        
    try:
        response = requests.post(OLLAMA_URL, json=payload)
        response.raise_for_status()
        return response.json()['response']
    except Exception as e:
        print(f"\n[!] Error connecting to Ollama: {e}")
        sys.exit(1)

def get_classifier_system_prompt():
    """Defines the 8-quadrant problem matrix and methodology mapping rules."""
    return """You are an Elite Systems Architect and Innovation Theorist. Your job is to analyze an incoming technical friction point, classify it accurately, and select the absolute best toolset for deconstruction.

PROBLEM TYPES AVAILABLE:
1. Physical-Geometric Contradictions (Spatial, material, mass, or environmental limits)
2. Architectural Couplings (High-density interdependence, fragile schemas, cascade risks)
3. High-Dimensional Scaling (Throughput, memory, hardware channels, or resource exhaustion)
4. Temporal / Lifecycle Decay (Transient data capture, synchronization, long-term persistence)
5. Cognitive Overload & Friction (Information density, documentation chaos, context retrieval barriers)
6. Regulatory / Safety Paradoxes (Rigid auditability/compliance vs. execution velocity)
7. Resource Deficit / Sovereign Operations (Air-gapped execution, localized infrastructure, island environments)
8. Open-Ended Creative Synthesis (Cross-domain mapping, multi-medium translation, algorithmic design)

INNOVATIVE METHODS LIBRARY:
- Functional Modeling & Trimming (Map Subject-Action-Object; eliminate components and force existing elements to absorb their functions)
- Axiomatic Design (Decouple Functional Requirements from Design Parameters to eliminate systemic ripple effects)
- Su-Field Analysis (Model interactions as Substance-Substance-Field; introduce new fields or internal substances to resolve harmful effects)
- Morphological Analysis (Deconstruct into multi-dimensional matrices and systematically combine variant parameters)
- Inversion & Antifragility (Map out catastrophic failure modes explicitly to build defensive, self-healing frameworks)
- Biomimicry & Natural Systems (Abstract closed-loop, adaptive, or evolutionary strategies from nature)

You must analyze the input and output valid JSON ONLY matching this exact schema:
{
  "problem_type": "Exactly one of the 8 types listed above",
  "primary_friction": "Deep architectural root cause of why this is a bottleneck",
  "selected_methods": ["Method Name 1", "Method Name 2"],
  "routing_rationale": "Clear engineering justification for why these two specific methods will successfully dismantle this problem typography"
}"""

def get_specialist_prompt_map():
    """Contains targeted, rigorous engineering instructions for each methodology."""
    return {
        "Functional Modeling & Trimming": (
            "You are a Systems Optimization Engineer executing Functional Modeling & Trimming. "
            "Deconstruct the problem down into strict Subject -> Action -> Object relational pairs. "
            "Identify the costly, redundant, or problematic components. Provide specific options to completely "
            "TRIM (eliminate) those components, explaining how the remaining system architecture or environmental "
            "resources can autonomously absorb their functions."
        ),
        "Axiomatic Design": (
            "You are an Enterprise Systems Architect practicing Axiomatic Design. "
            "Map out the core Functional Requirements (FRs) of this problem and separate them strictly from the "
            "Design Parameters (DPs). Identify where 'coupling' occurs—where one parameter inadvertently modifies "
            "another function. Provide options to uncouple the system into a completely modular, orthogonal architecture "
            "where changes never cause downstream cascade failures."
        ),
        "Su-Field Analysis": (
            "You are a Material and Physics Engineer specializing in Substance-Field (Su-Field) Modeling. "
            "Define the two interacting components (Substance 1 and Substance 2) and the energetic force/channel "
            "acting between them (the Field). Identify if the interaction is harmful, ineffective, or excessive. "
            "Provide options using standard TRIZ class solutions: introduce a third internal substance, introduce "
            "a protective/intermediary field, or leverage existing ambient fields to optimize the interface."
        ),
        "Morphological Analysis": (
            "You are a Combinatorial Design Specialist utilizing a Zwicky Morphological Box. "
            "Break the problem down into 3 to 4 independent, critical architectural dimensions or attributes. "
            "For each dimension, list 3 radically different possible values or paradigms. Systematically "
            "construct 2 distinct, highly optimized 'pathways' through this matrix, detailing how these "
            "unconventional combinations solve the overall problem."
        ),
        "Inversion & Antifragility": (
            "You are a Lead Safety & Mission Assurance (S&MA) Engineer practicing Inversion. "
            "Do not try to solve the problem yet. Instead, detail exactly how to intentionally, subtly, and "
            "catastrophically cause this failure mode to happen in production or runtime. Map out these silent failure vectors. "
            "Once mapped, reverse them to build an active, deterministic, defensive, or self-healing architecture "
            "that makes the system completely antifragile to these specific vulnerabilities."
        ),
        "Biomimicry & Natural Systems": (
            "You are an Advanced Systems Ecologist applying Biomimicry. "
            "Abstract the core problem away from engineering jargon into a functional biological challenge (e.g., "
            "metabolic regulation, thermal dissipation, distributed intelligence, or closed-loop waste reuse). "
            "Identify how evolutionary systems handle this challenge (e.g., mycelial networks, social insects, homeostatic loops). "
            "Translate these biological frameworks back into concrete hardware/software architectural specifications."
        )
    }

def run_innovation_pipeline(user_problem):
    print("\n[*] Phase 1: Analyzing Problem Typography & Dynamic Routing...")
    classifier_system = get_classifier_system_prompt()
    route_raw = call_ollama(user_problem, classifier_system, json_mode=True)
    
    try:
        routing_decision = json.loads(route_raw)
    except json.JSONDecodeError:
        print("[!] Failed to parse JSON from routing agent. Raw output:")
        print(route_raw)
        return
    
    print(f"    -> Classified Type  : {routing_decision['problem_type']}")
    print(f"    -> Primary Friction : {routing_decision['primary_friction']}")
    print(f"    -> Selections       : {', '.join(routing_decision['selected_methods'])}")
    print(f"    -> Rationale        : {routing_decision['routing_rationale']}\n")
    
    specialist_map = get_specialist_prompt_map()
    specialist_outputs = []
    
    for method in routing_decision['selected_methods']:
        if method not in specialist_map:
            print(f"[!] Warning: Agent routed to unknown method '{method}'. Skipping.")
            continue
            
        print(f"[*] Phase 2: Spawning Specialized Agent for [{method}]...")
        system_instruction = specialist_map[method]
        execution_prompt = f"Deconstruct and provide options for this problem statement:\n\"\"\"\n{user_problem}\n\"\"\""
        
        output = call_ollama(execution_prompt, system_instruction, json_mode=False)
        specialist_outputs.append(f"## Methodology: {method}\n\n{output.strip()}")
        print(f"    -> Execution complete.")
        
    print("\n[*] Phase 3: Synthesizing Solutions into System Architecture Brief...")
    synthesis_input = (
        f"Original Problem Statement:\n{user_problem}\n\n"
        f"Classification Type: {routing_decision['problem_type']}\n"
        f"Identified Friction: {routing_decision['primary_friction']}\n\n"
        "Raw Expert Deconstructions:\n" + "\n\n---\n\n".join(specialist_outputs)
    )
    
    synthesizer_system = (
        "You are a Senior Systems Architect. Your job is to take raw divergent insights from specialized "
        "innovation frameworks and synthesize them into a clean, highly technical, scannable Markdown brief. "
        "Highlight any underlying engineering trade-offs or technical contradictions. Organise the generated "
        "options into clear, actionable structural patterns. Use clean headers, bold terms, and precise lists. "
        "Do not include conversational pleasantries."
    )
    
    final_brief = call_ollama(synthesis_input, synthesizer_system, json_mode=False)
    return final_brief

if __name__ == "__main__":
    # Example complex multi-domain scenario
    sample_problem = (
        "We are capturing messy, highly transient audio and transcript fragments from engineering review meetings. "
        "We need to transform this patchy, time-decaying stream into a persistent, auditable Knowledge Graph "
        "without manual data entry, while ensuring absolute local data privacy on an uncoupled/air-gapped 12GB VRAM workstation."
    )
    
    print("======================================================================")
    print("            OLLAMA MULTI-AGENT INNOVATION ROUTER ENGINE               ")
    print("======================================================================")
    print(f"Target Problem:\n{sample_problem}")
    
    brief = run_innovation_pipeline(sample_problem)
    
    print("\n======================================================================")
    print("                    FINAL SYSTEM ARCHITECTURE BRIEF                   ")
    print("======================================================================")
    print(brief)

From <https://gemini.google.com/app/ddfcfa384f26c231> Google Gemini (3.5 Flash)

Search This Blog

Learning from ChatGPT