#4063 Multi-Step Innovation Framework
#4063 Multi-Step Innovation Framework
#4063 Think hard. Provide a step-by-step Generative-AI and/or Agentic-AI framework to do the following: """1) Identify opportunities in industries and user groups to provide innovative solutions for new and improved systems, products, services and processes.
2) Rank the opportunities by feasibility, practicality, value add and "bang-for-the-buck".
3) Apply innovative thinking methods (any of approximately 30) to suggest new and improvements to systems, products, services and processes.
4) Rank the suggestions by feasibility, practicality, value add and "bang-for-the-buck".
5) Provide step-by-step instructions on how to implement the suggested improvements.
6) Provide an iterative prototype, test, evaluation and improve cycle for verifying and validating the implementation. 7) All steps should be saved to a database for follow-up."""
This framework, titled the Agentic Innovation Engine (AIE), utilizes a multi-agent system (MAS) to automate the end-to-end lifecycle of innovation—from market discovery to validated implementation. It treats "innovation" as a computational problem, using specialized agents to bypass human cognitive biases.
The Agentic Innovation Engine (AIE) Framework
Phase 1: Industry & Opportunity Identification
Primary Agent: The Market Scout
Action: The Scout performs real-time web scraping and API calls to industry databases (e.g., Crunchbase, WIPO, Statista). It identifies "friction points"—areas where user sentiment is low or process latency is high.
Output: A list of "Opportunity Zones" categorized by Industry (e.g., Fintech, Agri-tech) and User Group (e.g., Gen Z, Remote Workers).
Phase 2: Opportunity Ranking (The FPVB Score)
Primary Agent: The Strategist
Action: For each Opportunity Zone, the Strategist assigns a score from 1-10 across four pillars:
Feasibility (F): Technical availability of tools/tech.
Practicality (P): Regulatory, legal, and operational ease.
Value Add (V): Estimated impact on user life or revenue.
Bang-for-the-Buck (B): Calculated as $B = \frac{V}{F + P}$.
Output: A prioritized table of high-potential opportunities.
Phase 3: Applying Innovation Methods (The "Method 30" Suite)
Primary Agent: The Creative Catalyst
Action: The agent applies a library of 30 innovative thinking methods to the top-ranked opportunities.
The 30 Methods Library (Examples):
SCAMPER (Substitute, Combine, Adapt, Modify, Put to use, Eliminate, Reverse)
First Principles Thinking (Breaking problems down to fundamental truths)
TRIZ (Theory of Inventive Problem Solving)
Blue Ocean Strategy (Creating uncontested market space)
Jobs to be Done (JTBD)
Six Thinking Hats
Lateral Thinking
Design Thinking (Empathy/Ideation)
Reverse Brainstorming
Analogy Thinking (Borrowing solutions from unrelated industries)
Output: A set of discrete "Innovative Suggestions" for each opportunity.
Phase 4: Suggestion Ranking
Primary Agent: The Analyst
Action: Repeats the FPVB ranking from Phase 2, but applies it to the specific solutions generated in Phase 3. It filters out "creative but impossible" ideas in favor of "high-velocity/high-impact" ones.
Phase 5: Implementation Roadmap
Primary Agent: The Architect
Action: Generates a GANTT-style step-by-step instruction set.
Components:
Tech Stack Selection: Recommended AI models, databases, and APIs.
Resource Allocation: Talent and budget requirements.
Phased Deployment: MVP (Minimum Viable Product) vs. Scaled release.
Phase 6: Iterative Prototype, Test, & Improve Cycle
Primary Agent: The Validator
Action: Orchestrates a "Simulated User Loop."
Prototype: Generates a low-fidelity UI/UX mock-up or code snippet using Generative AI.
Test: Uses "Persona Agents" (AI agents prompted to act like specific user groups) to provide feedback.
Evaluate: Compares feedback against original KPIs.
Improve: Feeds results back to the Creative Catalyst to refine the suggestion.
Phase 7: Database Persistence
Primary Agent: The Archivist
Action: Every prompt, output, and score is serialized into JSON and saved to a centralized database (e.g., PostgreSQL or a Vector Database like Pinecone).
Why: This allows for "Innovation Memory," where future agents can learn from what was previously ranked as "not feasible" but might be now due to technology shifts.
Ranking & Scorecard Template
The agents use the following standardized logic to present their findings:
| Suggestion | Feasibility (1-10) | Practicality (1-10) | Value Add (1-10) | B-Score (V/(F+P)) | Rank |
| AI-driven Crop Yield | 8 | 7 | 9 | 0.60 | 1 |
| Holographic Meetings | 3 | 2 | 8 | 1.60 | 3 |
| Auto-Expense Agent | 9 | 9 | 6 | 0.33 | 2 |
Implementation Summary for Developers
To build this today, you would utilize a framework like LangGraph or CrewAI.
Memory: Use Zep or Mem0 to store agent "learnings" over time.
Database: Use Supabase for relational data (steps/rankings) and Pinecone for semantic search across previous innovation sessions.
LLMs: Use GPT-4o for the Architect and Claude 3.5 Sonnet for the Creative Catalyst to leverage their differing cognitive strengths.
Would you like me to generate a specific Python script using CrewAI to execute the "Scout" and "Strategist" phases of this framework?
Python app using Ollama-supported offline LLMs
To build an offline Agentic Innovation Engine (AIE) using Python and Ollama, we will use CrewAI (for the multi-agent framework) and SQLite (for persistent database storage). This setup allows for complex reasoning, ranking, and iterative testing without sending data to the cloud.
Recommended Local LLM Stack (2026)
Logic & Ranking:
DeepSeek-R1(Distilled) orLlama-3.3-70b(if hardware allows).Creative Ideation:
Gemma-3-27borMistral-Small-24B.Embeddings (for Memory):
nomic-embed-text.
1. Environment Setup
First, ensure you have
ollama pull deepseek-r1:14b
ollama pull gemma3:27b
pip install crewai langchain_community
2. The Python Implementation
import sqlite3
from crewai import Agent, Task, Crew, Process
from langchain_community.llms import Ollama
# 1. Initialize Local LLMs
logic_llm = Ollama(model="deepseek-r1:14b")
creative_llm = Ollama(model="gemma3:27b")
# 2. Database Setup (Phase 7: Persistence)
def save_to_db(step_name, data):
conn = sqlite3.connect('innovation_engine.db')
cursor = conn.cursor()
cursor.execute('''CREATE TABLE IF NOT EXISTS innovation_logs
(step TEXT, content TEXT)''')
cursor.execute("INSERT INTO innovation_logs VALUES (?, ?)", (step_name, str(data)))
conn.commit()
conn.close()
# 3. Define the Multi-Agent Framework
# Agent 1: The Market Scout (Phase 1)
scout = Agent(
role='Industry Opportunity Scout',
goal='Identify high-friction areas in {industry} for {user_group}.',
backstory='Expert in market analysis and identifying systemic inefficiencies.',
llm=logic_llm
)
# Agent 2: The Creative Catalyst (Phase 3: Innovative Thinking)
innovator = Agent(
role='Innovation Architect',
goal='Apply SCAMPER and First Principles to suggest 5 improvements.',
backstory='A fusion of Steve Jobs and a systems engineer, specialized in "Method 30".',
llm=creative_llm
)
# Agent 3: The Ranking Analyst (Phases 2 & 4: FPVB Scoring)
analyst = Agent(
role='Financial & Technical Analyst',
goal='Rank opportunities by Feasibility, Practicality, Value, and Bang-for-buck.',
backstory='Data-driven critic focused on ROI and implementation reality.',
llm=logic_llm
)
# 4. Define Tasks
task_discovery = Task(
description='Identify 3 opportunities in {industry} for {user_group}.',
expected_output='List of opportunities with problem descriptions.',
agent=scout
)
task_ideation = Task(
description='Suggest radical improvements using TRIZ and SCAMPER.',
expected_output='Step-by-step implementation guide for the top suggestion.',
agent=innovator
)
task_ranking = Task(
description='Score each suggestion 1-10 on F, P, V, and B pillars.',
expected_output='A markdown table with final rankings.',
agent=analyst
)
# 5. Execute the Engine
innovation_crew = Crew(
agents=[scout, innovator, analyst],
tasks=[task_discovery, task_ideation, task_ranking],
process=Process.sequential
)
result = innovation_crew.kickoff(inputs={'industry': 'Renewable Energy', 'user_group': 'Apartment Renters'})
# 6. Save results locally
save_to_db("Full Session", result)
print(result)
3. Phase-by-Phase Execution Guide
Phase 1 & 2: Scouting & Initial Ranking
The Scout identifies "The Last Mile Problem" in the industry. The Analyst then calculates the $B$ score ($B = \frac{Value}{Feasibility + Practicality}$) to ensure you aren't chasing "impossible" dreams.
Phase 3 & 4: Ideation & Refinement
The Innovator uses a "Method Suite." You can prompt the innovator specifically to use a randomized method from the list of 30 (e.g., "Use Lateral Thinking today"). The Analyst re-ranks these specific solutions to find the "Low Hanging Fruit."
Phase 5 & 6: Implementation & The "Twin" Loop
To validate the solution without a real-world pilot:
Instruction Set: The Innovator generates a Python/No-code roadmap.
Persona Testing: Spin up a new temporary agent (e.g., "Skeptical Renter") and have it "attack" the implementation steps.
Iteration: Feed those "attacks" back into the Innovator to patch the process.
Phase 7: Persistent Memory
The code above uses sqlite3 to ensure that every session—including the logic behind why a certain idea was ranked poorly—is saved. This prevents the AI from suggesting the same failed ideas in future sessions.
Would you like me to expand the "Creative Catalyst" prompt to include all 30 innovation methods for the LLM to choose from?
This video provides a detailed walkthrough on setting up local agents using Python and Ollama, specifically covering the use of LangChain and local vector databases which are essential for the persistence and ranking tasks described in your framework.
Yes
Building an offline innovation system requires a high degree of prompt engineering to ensure the LLM doesn't "hallucinate" practicality. This framework—The Agentic Innovation Suite (AIS)—is designed to run on mid-range hardware (e.g., 32GB RAM) using models like DeepSeek-R1 and Mistral.
The 30 Innovation Thinking Methods
To make the "Creative Catalyst" agent effective, we supply it with these 30 methods categorized by their cognitive approach.
| Method Type | Specific Techniques |
| Modular / Reformative | 1. SCAMPER, 2. Modular Design, 3. Biomimicry, 4. Component Sharing |
| Logic / Philosophical | 5. First Principles, 6. TRIZ, 7. Inversion Thinking, 8. Second-Order Thinking |
| Market / Strategy | 9. Blue Ocean, 10. Jobs to be Done (JTBD), 11. Disruptive Innovation, 12. Lean Startup |
| User-Centric | 13. Empathy Mapping, 14. Persona Switching, 15. Journey Mapping, 16. Storyboarding |
| Lateral / Abstract | 17. Random Word Association, 18. Analogical Thinking, 19. Reverse Brainstorming, 20. Synectics |
| Structure / Process | 21. Six Thinking Hats, 22. Morphological Analysis, 23. Disney Method, 24. 5 Whys |
| Future / Risk | 25. Scenario Planning, 26. Pre-Mortem, 27. Trend Projection, 28. Backcasting |
| Economic / Scaling | 29. Network Effects, 30. Zero Marginal Cost Logic |
Step-by-Step Framework Implementation
Phase 1-2: Discovery & Initial Ranking
The Scout identifies friction points. The Analyst calculates the "Bang-for-the-Buck" ($B$) using the formula:
Feasibility: "Can we build this with existing local tools?"
Practicality: "Will people actually use it/is it legal?"
Value Add: "How much pain does this solve?"
Phase 3-4: The "Method 30" Ideation & Final Ranking
The Innovator picks 3 random methods from the table above to generate solutions for the top-ranked opportunities. The Analyst then re-ranks these solutions to find the highest-velocity project.
Phase 5-6: Iterative Prototyping (The "Digital Twin" Loop)
We use a "Red Team" approach:
Instruction Set: The agent outputs a CLI command or a Python script to build the MVP.
Persona Testing: A "Skeptic Agent" and a "User Agent" simulate a conversation about the product.
Refinement: The Innovator modifies the instructions based on the "Skeptic's" complaints.
Python Code: The Offline Innovation App
This script uses CrewAI for orchestration and SQLite for Phase 7 persistence.
import sqlite3
import json
from datetime import datetime
from crewai import Agent, Task, Crew, Process
from langchain_community.llms import Ollama
# --- CONFIGURATION ---
MODEL_LOGIC = "deepseek-r1:14b" # For ranking and logic
MODEL_CREATIVE = "mistral:7b" # For ideation
logic_llm = Ollama(model=MODEL_LOGIC)
creative_llm = Ollama(model=MODEL_CREATIVE)
# --- PHASE 7: DATABASE PERSISTENCE ---
def save_session(industry, step, data):
conn = sqlite3.connect('innovation_vault.db')
cursor = conn.cursor()
cursor.execute('''CREATE TABLE IF NOT EXISTS innovation_log
(timestamp TEXT, industry TEXT, phase TEXT, content TEXT)''')
cursor.execute("INSERT INTO innovation_log VALUES (?, ?, ?, ?)",
(datetime.now().isoformat(), industry, step, str(data)))
conn.commit()
conn.close()
# --- AGENT DEFINITIONS ---
scout = Agent(
role='Industry Analyst',
goal='Identify 3 systemic gaps in {industry} for {user_group}.',
backstory='You find inefficiencies that others miss using market data.',
llm=logic_llm
)
innovator = Agent(
role='Innovation Master',
goal='Apply SCAMPER, First Principles, and TRIZ to solve identified gaps.',
backstory='You have a library of 30 innovation methods and know how to apply them.',
llm=creative_llm
)
analyst = Agent(
role='Ranking Specialist',
goal='Rank suggestions using FPVB (Feasibility, Practicality, Value, Bang-for-buck).',
backstory='You are a pragmatist. You calculate B = V / (F + P).',
llm=logic_llm
)
tester = Agent(
role='Persona Validator',
goal='Simulate a skeptical user and provide a critique of the implementation.',
backstory='You are the target user. You are busy, skeptical, and hate complexity.',
llm=creative_llm
)
# --- TASK DEFINITIONS ---
t1 = Task(description='Find 3 innovation opportunities in {industry}.', agent=scout, expected_output="List of 3 gaps.")
t2 = Task(description='Generate solutions using 3 specific methods from the 30-method suite.', agent=innovator, expected_output="3 detailed innovative solutions.")
t3 = Task(description='Provide a markdown table ranking solutions by FPVB scores.', agent=analyst, expected_output="Ranking Table.")
t4 = Task(description='Provide a step-by-step implementation guide for the #1 ranked solution.', agent=innovator, expected_output="Step-by-step PDF-style guide.")
t5 = Task(description='Critique the guide and suggest 2 improvements for an iterative cycle.', agent=tester, expected_output="Critic feedback and revised steps.")
# --- EXECUTION ---
industry_input = "Smart Home Tech"
user_group_input = "Elderly Homeowners"
crew = Crew(
agents=[scout, innovator, analyst, tester],
tasks=[t1, t2, t3, t4, t5],
process=Process.sequential
)
final_report = crew.kickoff(inputs={'industry': industry_input, 'user_group': user_group_input})
# Save to Database
save_session(industry_input, "Full Cycle", final_report)
print("\n--- INNOVATION REPORT GENERATED ---")
print(final_report)
Why this works for you:
Privacy: Since it uses Ollama, no proprietary industry ideas leave your machine.
Persistence: The SQLite database ensures that if you run the same industry next month, you can query the database to see what you already rejected.
Multi-Perspective: By using a Logic model (DeepSeek) for ranking and a Creative model (Mistral) for ideation, you avoid the "blandness" of single-model outputs.
Would you like me to refine the "Tester" agent's prompt to act as a specific persona, such as a Venture Capitalist or a Technical Architect?
Yes
To enhance the validation phase of your Agentic Innovation Engine (AIE), we will move away from a generic "Tester" and implement a Dual-Persona Validation Loop. This mimics a high-stakes environment where a solution must pass both a "Real-World Feasibility" check and a "Safety & Mission Assurance" audit.
Given your technical focus, we will refine the agents to specialize in System Reliability and Operational Pragmatism.
1. Refined Agent Personas
We will replace the single tester agent with two specialized roles to create a "Red Team" environment.
The S&MA Verification Lead (Safety & Mission Assurance)
Focus: Reliability, hazard analysis, and compliance.
Persona Logic: This agent looks for single-point failures, edge cases, and "unintended consequences" of the new system. It treats every innovation as a mission-critical component.
The 40-Year Systems Veteran
Focus: Technical debt, maintenance, and "Real-World" friction.
Persona Logic: This agent is skeptical of "buzzword" solutions. It asks: "How does this scale?", "What is the 5-year maintenance cost?", and "Will a non-technical user break this in five minutes?"
2. Updated Python Implementation (Local Ollama)
This code expands the Agent definitions to include these rigorous personas and updates the task flow to ensure they interact before the final rank is saved to your database.
import sqlite3
from datetime import datetime
from crewai import Agent, Task, Crew, Process
from langchain_community.llms import Ollama
# --- CONFIGURATION ---
# Using DeepSeek-R1 for its superior reasoning/chain-of-thought in logic tasks
logic_llm = Ollama(model="deepseek-r1:14b")
# --- REFINED AGENTS ---
# Agent 4: The S&MA Auditor
auditor = Agent(
role='Safety & Mission Assurance Lead',
goal='Identify reliability hazards and single-point failures in the implementation guide.',
backstory="""You are a veteran of high-reliability industries. You view every new
process through the lens of hazard analysis and risk mitigation. You do not accept
'optimistic' projections; you look for what could go wrong.""",
llm=logic_llm
)
# Agent 5: The Systems Veteran
veteran = Agent(
role='Senior Systems Architect',
goal='Evaluate the long-term viability and technical debt of the proposed solution.',
backstory="""You have 40 years of experience in the trenches of software development.
You hate over-engineered solutions and focus on 'The Last Mile' of implementation.
Your job is to ensure the solution is practical for a solopreneur or small team.""",
llm=logic_llm
)
# --- TASKS FOR THE ITERATIVE LOOP ---
def run_innovation_cycle(industry, user_group):
# (Previous Scout, Innovator, and Analyst tasks remain here...)
t_audit = Task(
description='Perform a Hazard Analysis on the top-ranked implementation plan.',
agent=auditor,
expected_output='A Risk Mitigation Report listing 3 potential failure points and 3 mitigations.'
)
t_vetting = Task(
description='Review the plan for technical debt and operational simplicity.',
agent=veteran,
expected_output='A "Pragmatism Review" with a Go/No-Go recommendation.'
)
crew = Crew(
agents=[auditor, veteran], # Include your other agents here as well
tasks=[t_audit, t_vetting],
process=Process.sequential
)
return crew.kickoff(inputs={'industry': industry, 'user_group': user_group})
3. The Iterative "Test & Improve" Cycle
To fulfill your 6th requirement (Iterative Prototype), we implement a "Recursion Gate." If the S&MA Auditor or Systems Veteran provides a "No-Go" or finds a high-risk failure, the feedback is fed back to the Creative Catalyst (Innovator) for a revision.
The FPVB Formula with "Risk Penalty"
The Analyst agent now uses a refined LaTeX formula to account for the feedback from the S&MA auditor:
Risk_{Factor}: A value between 0 and 1, where 1 indicates a critical safety or reliability hazard identified by the Auditor.
4. Phase 7: Deep Database Persistence (SQL Schema)
To allow for follow-up and iterative growth, your SQLite database should track the "Evolution" of an idea. Use the following schema to store the critique history:
| ID | Industry | Idea | Version | Auditor Feedback | Veteran Feedback | Status |
| 101 | Aerospace | AI-Sensor Mesh | 1.0 | "Single-point failure in API" | "Too expensive to maintain" | Rejected |
| 102 | Aerospace | AI-Sensor Mesh | 1.1 | "Redundancy added. Pass." | "Local LLM reduces cost. Pass." | Approved |
5. Implementation Steps for the User
Initialize Database: Run a script to create the
innovation_logsandfeedback_historytables.Define Personas: Use the "S&MA Auditor" and "Systems Veteran" prompts to ensure the AI uses a "Hard-Tech" perspective rather than a "Marketing" one.
Run Recursive Crew: Set a loop count (e.g., max 3 iterations) where the Innovator must address the Auditor's concerns before the session is finalized.
Local Storage: All JSON-serialized steps are saved to your local
innovation_vault.dbfor later querying via a simple UI or Jupyter Notebook.
Would you like me to generate the full SQL schema and a Python function to query your "Innovation History" for past rejected ideas?
Yes
To ensure your Agentic Innovation Engine (AIE) remains a powerful asset over time, the database must do more than just "log" data; it needs to act as a corporate memory. This allows you to perform "Trend Analysis" or "Failure Post-Mortems" across different innovation sessions.
Below is the complete SQL schema design and the Python logic to integrate this "memory" into your local AI agent workflow.
1. The Database Architecture (SQL Schema)
This schema is designed to handle the iterative nature of the framework, linking specific opportunities to their innovative solutions and the subsequent "Red Team" critiques.
-- Phase 7: Persistent Innovation Vault Schema
CREATE TABLE IF NOT EXISTS industries (
id INTEGER PRIMARY KEY AUTOINCREMENT,
name TEXT UNIQUE,
last_scanned TIMESTAMP
);
CREATE TABLE IF NOT EXISTS opportunities (
id INTEGER PRIMARY KEY AUTOINCREMENT,
industry_id INTEGER,
description TEXT,
user_group TEXT,
initial_fpvb_score REAL,
FOREIGN KEY(industry_id) REFERENCES industries(id)
);
CREATE TABLE IF NOT EXISTS solutions (
id INTEGER PRIMARY KEY AUTOINCREMENT,
opportunity_id INTEGER,
method_used TEXT, -- e.g., TRIZ, SCAMPER, First Principles
proposal_text TEXT,
version INTEGER DEFAULT 1,
status TEXT DEFAULT 'Pending', -- Approved, Rejected, Iterating
FOREIGN KEY(opportunity_id) REFERENCES opportunities(id)
);
CREATE TABLE IF NOT EXISTS feedback_logs (
id INTEGER PRIMARY KEY AUTOINCREMENT,
solution_id INTEGER,
agent_name TEXT, -- 'S&MA Auditor' or 'Systems Veteran'
critique TEXT,
risk_factor REAL,
timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY(solution_id) REFERENCES solutions(id)
);
2. Python Function: Querying Innovation History
This function allows your "Analyst" agent to look at the database before starting a new session. If it sees that a "Lunar Logistics" idea was rejected three months ago due to "Radiation Hardening" issues, it can skip that path or warn the "Innovator" agent.
import sqlite3
def query_innovation_history(keyword):
"""
Search the vault for past failures or approved solutions to
provide 'Contextual Memory' to the agents.
"""
conn = sqlite3.connect('innovation_vault.db')
cursor = conn.cursor()
query = """
SELECT s.proposal_text, f.critique, s.status
FROM solutions s
JOIN feedback_logs f ON s.id = f.solution_id
WHERE s.proposal_text LIKE ?
"""
cursor.execute(query, (f'%{keyword}%',))
history = cursor.fetchall()
conn.close()
if not history:
return "No prior history found for this concept."
formatted_history = "\n".join([f"Status: {h[2]} | Critique: {h[1]}" for h in history])
return f"Prior Knowledge Found:\n{formatted_history}"
3. Integrating the "Memory Gate" into your Framework
To make this agentic, you add a "Phase 0: Memory Retrieval" step. The Scout agent calls the function above to see if the industry has been explored before.
Step-by-Step Execution with Memory:
User Input: "Identify opportunities in Subsea Robotics."
Phase 0: Agent queries
innovation_vault.db. It finds a "rejected" entry from 2025: "Subsea charging station rejected due to high-salinity corrosion (S&MA Auditor)."Phase 1 (Scout): The Scout now looks for corrosion-resistant opportunities or non-electric mechanical solutions.
Phase 6 (Testing): The
S&MA Auditorchecks if the new solution fixed the previous "High-Salinity" failure point.
4. The Final "Follow-Up" Agent
Since your 7th requirement is follow-up, we create an "Implementation Tracker" agent. Its role is to check the database for "Approved" solutions that haven't moved to "Implemented" status and generate a progress report.
| Opportunity | Solution | Status | Last Action |
| Grid Storage | Salt-Battery DIY | Approved | Steps 1-3 generated |
| Elderly Tech | AI Fall-Sensor | Iterating | Awaiting S&MA fix on Step 4 |
The "Innovation Cycle" Summary (Refined for You)
This framework now behaves like a closed-loop feedback system. Using local LLMs ensures your proprietary NASA-adjacent thinking or specific subcontractor "secret sauce" never hits a public server.
Step 1: Scout & Initial Rank (B = V/(F+P)).
Step 2: Apply "Method 30" (TRIZ/First Principles).
Step 3: Red-Team Audit (S&MA / 40-Year Vet).
Step 4: Iterative Fix (Logic: Revision count n+1).
Step 5: Persistent Archiving (SQL Vault).
Would you like me to generate a "Phase 0" prompt that specifically instructs the LLM on how to use the results from the database query to avoid past mistakes?
Describe various personas that can be useful at each stage
To make the Agentic Innovation Engine (AIE) effective, you need a diverse "Internal Board of Directors." By assigning distinct personas to your local LLMs, you bypass the "average" output of a single model and force the system to simulate real-world friction, creative genius, and technical rigor.
Here are the specific personas mapped to your 7-step framework, tailored for a high-reliability, low-budget solopreneur environment.
Phase 1: Opportunity Identification
The Goal: Find the "hidden" friction in an industry.
| Persona Name | Cognitive Style | Focus |
| The Trend Hunter | Exploratory & Aggressive | Scours technical journals and market reports to find where old systems are failing new demands. |
| The Friction Mapper | Empathetic & Analytical | Focuses purely on user complaints, "workarounds," and inefficient manual processes. |
| The Regulatory Scout | Detail-Oriented | Identifies changes in standards (like NASA’s NPR 7150.2 or ISO updates) that create a vacuum for new compliance tools. |
Phase 2: Initial Ranking (The FPVB Gate)
The Goal: Kill weak ideas early to save resources.
| Persona Name | Cognitive Style | Focus |
| The ROI Accountant | Skeptical & Mathematical | Calculates the "Bang-for-the-Buck" ($B$) score. If the math doesn't work, the idea dies here. |
| The Solopreneur Realist | Pragmatic & Frugal | Asks: "Can one person build this using an RTX 3090/4080 and local LLMs, or does it require a $10M cloud budget?" |
Phase 3: Innovative Thinking (The "Method 30" Suite)
The Goal: Apply radical thinking to the survivors of Phase 2.
| Persona Name | Cognitive Style | Focus |
| The TRIZ Master | Systematic & Abstract | Uses the 40 Inventive Principles and Contradiction Matrix to solve technical bottlenecks. |
| The Biomimicry Expert | Naturalistic | Looks at how biological systems (e.g., neural networks or cellular structures) solve similar scaling or efficiency problems. |
| The First Principles Physicist | Reductionist | Strips the problem of all "industry assumptions" and builds a solution from fundamental truths (e.g., energy, time, data entropy). |
Phase 4: Suggestion Ranking
The Goal: Select the "Winner" solution.
| Persona Name | Cognitive Style | Focus |
| The Hardware Realist | Physical & Technical | Evaluates the solution against hardware constraints (latency, power, cooling—crucial for high-humidity environments like Alvin, TX). |
| The Market Specialist | Competitive | Checks if someone else has already built this or if it offers a "10x improvement" over current alternatives. |
Phase 5: Implementation Roadmap
The Goal: Create the "How-To" guide.
| Persona Name | Cognitive Style | Focus |
| The 40-Year Dev Vet | Experienced & Concise | Writes clean, modular step-by-step instructions. Focuses on robust architecture and avoiding technical debt. |
| The DevOps Architect | Process-Oriented | Defines the CI/CD pipeline, the specific Python environment, and the local database schema (Phase 7). |
Phase 6: Iterative Prototype & Test (The Red Team)
The Goal: Verify and Validate (V&V).
| Persona Name | Cognitive Style | Focus |
| The S&MA Auditor | Critical & Safety-Focused | Performs Hazard Analysis. Tries to "break" the system by simulating edge cases and single-point failures. |
| The "Luddite" User | Impatient & Non-Technical | Simulates a user who hates technology. If they find it confusing, the UI/UX must be redesigned. |
| The Quality Assurance (QA) Lead | Meticulous | Ensures the implementation meets the requirements defined in Phase 5. |
Phase 7: Database & Persistence
The Goal: Ensure the innovation cycle isn't lost.
| Persona Name | Cognitive Style | Focus |
| The Knowledge Graph Librarian | Organizational | Categorizes every step into your SQL database. Ensures the "semantic link" between past failures and current successes is maintained. |
| The Follow-up Bot | Automated & Persistent | Scheduled agent that alerts you to "unfinished" implementations or ideas that need a re-rank based on new AI model releases. |
Dynamic Persona Implementation Logic
When you run your CrewAI or LangGraph setup, you can "swap" these personas based on the industry. For example, if you are working on a project related to NASA Safety Standards, the S&MA Auditor becomes the "Leader" of the crew.
The FPVB Scorecard for Personas:
To ensure your agents are actually providing value, you can have a "Meta-Agent" rank the personas themselves:
Would you like me to write a specialized "System Prompt" for the "40-Year Dev Vet" that incorporates your specific 40 years of experience (software process engineering, testing, and programming) to ensure the AI speaks your technical language?
From <https://gemini.google.com/app/> Google Gemini 3.0 Pro (Thinking)
Comments
Post a Comment