#4848 Innovation Workflow
#4848 Innovation Workflow
#4848 Rewrite this work flow: """1) identify opportunities in industry and user groups where innovative solutions are needed or would be beneficial, 2) analyze each opportunity for understanding, 3) recommend N improvements for each opportunity, 4) describe implementation of each recommended improvement"""
Depending on your audience and delivery context, here are four ways to rewrite this workflow:
1. Executive & Professional (Standard Corporate / PMO)
Discover Opportunity Areas: Identify high-impact friction points and unmet needs across target industries and user segments.
Conduct Deep-Dive Analysis: Evaluate root causes, operational constraints, and potential value drivers for each identified opportunity.
Formulate Solution Options: Develop a prioritized set of targeted, high-value improvements for each area.
Define Implementation Roadmaps: Detail execution requirements, milestones, resource needs, and rollout strategies for each initiative.
2. Action-Oriented & Crisp (Agile / Sprint-Ready)
Source Opportunities: Map user pain points and market gaps that justify innovation.
Deconstruct the Problem: Analyze context, baseline metrics, and user behavior to establish root issues.
Generate Targeted Interventions: Propose N distinct, high-leverage improvements per opportunity.
Spec the Rollout: Outline step-by-step technical, operational, and delivery plans for each recommendation.
3. Design Thinking & Innovation Framework
Empathize & Explore: Research target markets and user cohorts to spot friction and emerging needs.
Frame & Define: Synthesize findings to clearly diagnose root challenges and value potential.
Ideate & Prioritize: Generate and refine a tailored set of creative interventions for each challenge.
Plan Execution: Map out the phased transition from pilot prototype to full-scale operational rollout.
4. Compact Checklist Format
Phase 1: Discovery — Pinpoint high-value innovation gaps across users and industries.
Phase 2: Diagnosis — Assess root causes, user friction, and viability.
Phase 3: Solutioning — Recommend N tailored improvements per opportunity.
Phase 4: Execution Planning — Detail the phased implementation strategy for each solution.
Ultimately, I want to automate this with generative-AI and/or agentic AI, with minimal human-in-the-loop
To automate this four-stage lifecycle with minimal human intervention, the workflow must be refactored into a deterministic, multi-agent pipeline with strict state machine contracts, structured data validation, and an automated gatekeeper.
Here is the operational architecture:
[ Stage 1: Discovery ] Ingestion & Mining Agents │ ▼ (Opportunity JSON) [ Stage 2: Diagnosis ] Root Cause & Triad Decomposer │ ▼ (Causal Diagnostic Spec) [ Stage 3: Recommendation ] TRIZ & Inventive Solution Generators │ ▼ (N Candidate Solutions) [ Evaluation & Pruning Gate ] Automated Critic (Feasibility / ROI) │ ▼ (Selected Solutions) [ Stage 4: Specification ] Implementation & Architecture Agent │ ▼ (Actionable Artifacts)
Phase 1: Opportunity Ingestion & Signal Extraction (Mining Agent)
Objective: Continuously scan external unstructured data streams (industry forums, regulatory feeds, incident databases, API telemetry, operational logs) to detect unaddressed friction points.
Agent Operations:
Pull raw inputs via scrapers, RSS feeds, or API webhooks.
Execute entity extraction and anomaly detection to filter out noise.
Rank candidate signals against an automated scoring threshold (S_signal = Urgency x Frequency x Impact).
Deterministic Contract Output (
OpportunityRecord):opportunity_id: UUIDdomain_context: Target industry sector, user cohorts, and operating environment.observed_symptoms: List of concrete manifestations of failure or inefficiency.initial_urgency_score: Float (0.0 - 1.0) based on signal frequency and severity.
Phase 2: Systematic Problem Deconstruction (Diagnosis Agent)
Objective: Perform root-cause analysis without hallucination by deconstructing the opportunity into functional contradictions, resource constraints, and boundary conditions.
Agent Operations:
Map symptoms to underlying causal chains (Cause-and-Effect Verification).
Identify system boundary limits (e.g., latency limits, cost barriers, regulatory mandates).
Formulate the core engineering/operational contradiction (e.g., "Improving throughput increases computational overhead beyond envelope limits").
Deterministic Contract Output (
DiagnosticSpec):opportunity_id: UUID referenceroot_cause_chains: Directed graph / adjacency list of cause-and-effect nodes.contradictions: Defined pairs of[Improving Parameter]vs[Worsening Parameter].inviolable_constraints: Non-negotiable technical, physical, or compliance boundaries.
Phase 3: Targeted Improvement Synthesis (Generator & Critic Swarm)
Objective: Synthesize exactly N distinct improvements by applying systematic invention heuristics, followed by an automated pruning pass.
Agent Operations:
Generator Agent: Takes the
DiagnosticSpecand runs parallel generation runs seeded with distinct heuristics (e.g., parameter extraction, inversion, resource substitution, state decoupling) to guarantee diversity rather than N minor variations.Critic/Evaluator Agent (Automated Gate): Scores each candidate solution on a 0–100 scale across:
Novelty & Leverage: Does it resolve the root contradiction or merely patch a symptom?
Constraint Compliance: Does it violate any items in
inviolable_constraints?
Filters and sorts candidates, emitting the top N passing candidates.
Deterministic Contract Output (
SolutionCandidates):opportunity_id: UUID referenceimprovements: Array of length N, each containing:solution_id: Stringcore_mechanism: Operating principle resolving the contradiction.tradeoff_mitigation: How secondary penalties are absorbed or eliminated.critic_score: Aggregate evaluation metric.
Phase 4: Autonomous Implementation Blueprinter (Specification Agent)
Objective: Convert conceptual improvements into turnkey, actionable technical packages ready for execution.
Agent Operations:
Generate the architectural breakdown (interfaces, modules, state schemas, and dependencies).
Build a staged rollout schedule (Proof-of-Concept --> Integration --> Verification).
Formulate automated verification criteria (acceptance criteria and test vectors).
Deterministic Contract Output (
ImplementationArtifact):solution_id: String referencearchitecture_breakdown: Component topology and interface definitions.execution_phases: Sequenced phases with entry/exit gates.verification_suite: Unit/integration acceptance criteria and automated test matrices.failure_modes: Identified risks and fallback strategies.
Autonomous Orchestration & Governance Topology
| Component | Technology / Pattern | Function |
| State Machine Engine | LangGraph / Temporal / PydanticAI | Enforces acyclic graph traversal and persistent checkpointing across runs. |
| Schema Validation | Pydantic / JSON Schema | Rejects malformed agent outputs with zero retry leakage; triggers immediate self-healing loops. |
| Model Partitioning | Dual-Tier Routing | Sub-30B parameter models for raw signal scraping, extraction, and validation formatting; frontier reasoning models for causal deconstruction and blueprinting. |
| Memory / Deduplication | Vector + Graph Store | Stores historical opportunities, identified contradictions, and failed solutions to prevent repeat processing. |
| Human-in-the-Loop | Exception-Only Hook (Dark Mode) | System runs fully autonomous unless an output drops below the Critic's confidence threshold (Score < tau), halting only that specific branch for review. |
Concrete Execution Next Steps
Formalize Pydantic Schemas: Codify the four contracts (
OpportunityRecord,DiagnosticSpec,SolutionCandidates,ImplementationArtifact) with explicit field typing, regex bounds, and validation rules.Implement LangGraph State Machine: Build the workflow skeleton with four state nodes, an evaluation conditional edge between Stages 3 and 4, and an automated retry loop for schema non-conformance.
Configure System Prompt Harnesses: Anchor Stage 2 and Stage 3 system prompts with deterministic reasoning constraints (e.g., strict contradiction mapping and bounded N output constraints) to eliminate open-ended conversational drift.
How can this workflow be improved to potentially get better recommendations?
To improve the recommendation quality of an automated innovation workflow, the standard failure mode to eliminate is convergent mediocrity—where generative models propose shallow, obvious, or incremental improvements (e.g., "add an AI dashboard," "automate notifications," or "streamline the UI").
Here are five concrete architectural enhancements to force higher-leverage, non-obvious recommendations:
1. Invert the Prompt Strategy via First-Principles & Contradiction Framing
Standard prompting asks: "Given problem X, recommend solutions." This produces predictable best-practice summaries. Instead, force the agent through an Engineering Contradiction Matrix:
Mechanism: Require Stage 2 to isolate the opposing trade-off: "System must increase parameter A (e.g., speed, reliability) without degrading parameter B (e.g., memory, cost, power)."
Inventive Heuristics: In Stage 3, instantiate multiple independent generation workers, each constrained by a specific systematic innovation heuristic rather than a generic persona:
Principle of Inversion / Asymmetry: Solve the problem by designing the inverse system or shifting from continuous to pulsed operations.
Resource Cannibalization: Solve the bottleneck strictly using idle resources or waste energy/data already present in the system envelope.
State Decoupling / Dynamic Separation: Separate the competing requirements in time, space, or phase condition.
2. Multi-Perspective Generator Swarms with Orthogonal Personas
Instead of generating $N$ solutions in a single call, spin up parallel, isolated agents tasked with optimizing along strictly conflicting objective functions:
| Generator Node | Optimization Objective | Failure Mode It Rejects |
| Mechanistic Minimizer | Zero new dependencies; solves the issue by pruning components or operations. | Reject any solution that adds software, SaaS tools, or infrastructure bloat. |
| Structural Inverter | Paradigm shifts; reorganizes the execution hierarchy or dataflow topology. | Reject standard industry "best practice" playbooks. |
| Boundary Buster | Imports proven patterns from an unrelated technical domain (e.g., aerospace avionics, biological systems, distributed ledger consensus). | Reject solutions localized strictly to the host domain. |
| Pragmatic Incrementalist | Maximum leverage with minimum code changes and near-zero implementation risk. | Reject unvalidated or high-friction R&D efforts. |
Running these generators independently prevents cross-contamination, ensuring the final candidate pool is diverse rather than variations on a single theme.
3. Implement an Automated "Red Team" Critic Gate
LLMs generate more effectively when subjected to an adversarial critique-and-refine loop before outputting results. Introduce a dedicated Adversarial Evaluator between Stage 3 and Stage 4:
[Candidate Solutions] ──> [Red Team Critic] ──> Passes Threshold? ──Yes──> [Stage 4 Blueprint] │ └──No (Critique Feedback)──────> [Refinement Loop (Max 2)]
Automated Scoring Rubric:
Leverage Factor: Does this resolve the root causal failure or patch a symptom? (Score 1–5)
Feasibility & Boundary Integrity: Does it violate physical, compute, security, or regulatory constraints? (Binary pass/fail)
Novelty / Banality Check: Is this idea already standard in existing commercial products? (If yes, auto-reject with high penalty).
Self-Correction: Candidates scoring below your quality cutoff ($\tau$) are routed back to the generator with the critic’s concrete counter-arguments for one deterministic regeneration cycle.
4. Grounding via External Knowledge Graphs and Cross-Domain Analogy
Raw foundation models rely solely on parametric memory, which tends toward the statistical mean of their training data. Ground the reasoning engine using structured retrieval:
Failure Mode Repositories & Hazard Matrices: Inject domain-specific taxonomy (e.g., safety reports, post-mortems, root-cause graphs) during Stage 2 so the agent understands the actual physical/operational limits.
Cross-Domain Transfer: Query an indexed graph of structural mechanisms from disparate industries (e.g., asking how high-throughput logistics handles buffer starvation when diagnosing a real-time messaging pipeline).
5. Shift from Solution Descriptions to Falsifiable Hypotheses
A recommendation is only as good as its verification boundary. Enhance Stage 3's output schema so that every recommended improvement is required to output a testable assertion:
Expected Primary Effect: Measurable delta in key performance indicator (Delta KPI_1 > X).
Falsification Metric: The exact threshold at which this recommendation must be declared a failure during prototyping.
Secondary Order Penalty: Explicit prediction of what part of the system will experience stress as a direct side effect of this implementation.
Concrete Implementation Checklist
Update Pydantic Models: Augment
SolutionCandidatewith fields forcontradiction_addressed,domain_analogy_used,falsification_metric, andadversarial_critic_score.Add a Critic Loop Node: Insert a conditional routing edge in your orchestration graph (
generate_solutions-->critique_solutions-->route_pass_or_retry).Seed Generator Prompts with Contrasting Constraints: Replace generic generation prompts with 3 parallel runs invoking strict heuristic boundaries (Minimizer, Inverter, Analogous Transfer).
Bring it altogether as one Python application using Google Gemini API, saving results to markdown files
Application Overview
The script above implements your full 4-stage innovation lifecycle using the google-genai SDK, deterministic Pydantic schemas, model tiering, and an automated Markdown reporter:
Stage 1: Opportunity Ingestion (
OpportunityRecord) — Employsgemini-2.5-flashto extract structured friction points, compute urgency scores, and filter input noise.Stage 2: Root-Cause & Contradiction Decomposition (
DiagnosticSpec) — Usesgemini-2.5-proto isolate root-cause chains, formalize trade-off contradictions (Improving Parametervs.Worsening Parameter), and establish inviolable system constraints.Stage 3: Multi-Persona Swarm & Adversarial Critic Gate (
VettedSolutionsBatch) —Swarm Generator: Runs parallel generation across three orthogonal perspectives: Mechanistic Minimizer (zero dependencies), Structural Inverter (topology/order shifts), and Cross-Domain Transfer (mechanisms from external fields).
Red Team Critic: Evaluates leverage (1–5), flags constraint breaches, penalizes generic/banal concepts, and scores candidates to prune down to the top N passing solutions.
Stage 4: Implementation Blueprinter (
ImplementationArtifact) — Generates complete architecture breakdowns, phased execution milestones with entry/exit gates, falsification testing suites, and failure fallbacks for each vetted recommendation.Deterministic Export — Automatically serializes the entire trace into a structured Markdown audit document stored in
workspace_artifacts/.
Python Code Architecture
"""
Autonomous Innovation & Improvement Pipeline
============================================
Architecture:
Stage 1: Opportunity Ingestion & Signal Extraction (Mining)
Stage 2: Systematic Problem Deconstruction (Contradiction & Root-Cause)
Stage 3: Targeted Improvement Synthesis (Generator Swarm + Adversarial Critic Gate)
Stage 4: Autonomous Implementation Blueprinter (Architecture & Verification Matrix)
SDK: google-genai
"""
import json
import os
import sys
from typing import List, Optional
from pydantic import BaseModel, Field
from google import genai
from google.genai import types
# ---------------------------------------------------------------------------
# Configuration & Models
# ---------------------------------------------------------------------------
MODEL_ROUTING = {
"triage": "gemini-2.5-flash",
"deep_reasoning": "gemini-2.5-pro",
}
OUTPUT_DIR = "workspace_artifacts"
os.makedirs(OUTPUT_DIR, exist_ok=True)
# ---------------------------------------------------------------------------
# Pydantic Schemas for Strict State Contracts
# ---------------------------------------------------------------------------
class OpportunityRecord(BaseModel):
opportunity_id: str = Field(description="Unique identifier, e.g. OPP-001")
domain_context: str = Field(description="Target industry, technical subsystem, or user segment")
observed_symptoms: List[str] = Field(description="Concrete operational bottlenecks, failure modes, or friction points")
urgency_score: float = Field(ge=0.0, le=1.0, description="Calculated urgency/frequency/impact score between 0.0 and 1.0")
raw_source_summary: str = Field(description="Brief summary of source data stream or trigger")
class ContradictionPair(BaseModel):
improving_parameter: str = Field(description="Parameter to improve (e.g., speed, accuracy, throughput)")
worsening_parameter: str = Field(description="Parameter that unintentionally degrades (e.g., memory, latency, cost)")
operational_tension: str = Field(description="Clear formulation of the engineering or operational contradiction")
class DiagnosticSpec(BaseModel):
opportunity_id: str
root_cause_chains: List[str] = Field(description="Causal chain leading to the bottleneck")
contradictions: List[ContradictionPair] = Field(description="Identified trade-offs and structural contradictions")
inviolable_constraints: List[str] = Field(description="Strict technical, regulatory, or operational boundaries")
class ImprovementCandidate(BaseModel):
solution_id: str = Field(description="Unique solution identifier, e.g. SOL-001")
strategy_persona: str = Field(description="Perspective used: Mechanistic Minimizer, Structural Inverter, or Cross-Domain Transfer")
core_mechanism: str = Field(description="Primary operating principle resolving the contradiction")
contradiction_addressed: str = Field(description="Specific contradiction this resolves")
expected_primary_effect: str = Field(description="Measurable delta in key performance indicator")
falsification_metric: str = Field(description="Quantitative threshold where this solution is declared invalid")
secondary_penalty: str = Field(description="Anticipated secondary-order stress or friction")
class SwarmGenerationBatch(BaseModel):
candidates: List[ImprovementCandidate]
class CriticEvaluation(BaseModel):
solution_id: str
leverage_score: int = Field(ge=1, le=5, description="1=shallow patch, 5=fundamental structural resolution")
boundary_integrity_pass: bool = Field(description="True if no inviolable constraints are breached")
banality_penalty: bool = Field(description="True if the idea is standard commercial practice / cliché")
overall_score: float = Field(ge=0.0, le=100.0, description="Composite score based on rubric")
critique_rationale: str = Field(description="Detailed red-team critique identifying failure points")
class VettedSolutionsBatch(BaseModel):
opportunity_id: str
vetted_solutions: List[ImprovementCandidate]
critic_reports: List[CriticEvaluation]
class ImplementationPhase(BaseModel):
phase_name: str
key_deliverables: List[str]
entry_criteria: str
exit_criteria: str
class ImplementationArtifact(BaseModel):
opportunity_id: str
solution_id: str
solution_name: str
architectural_breakdown: List[str] = Field(description="Component topologies, state models, and interface specs")
execution_phases: List[ImplementationPhase]
verification_suite: List[str] = Field(description="Falsification tests, acceptance criteria, and edge-case test vectors")
failure_modes_and_fallbacks: List[str]
# ---------------------------------------------------------------------------
# Pipeline Engine
# ---------------------------------------------------------------------------
class InnovationPipeline:
def __init__(self, api_key: Optional[str] = None):
self.client = genai.Client(api_key="................................")
def stage_1_ingest(self, raw_input: str) -> OpportunityRecord:
"""Stage 1: Extract and formalize opportunity signals."""
print("\n[Stage 1] Ingesting and formalizing opportunity signals...")
system_instruction = (
"You are an Industrial & Systems Intelligence Mining Agent. "
"Deconstruct unstructured data streams into a validated OpportunityRecord. "
"Isolate concrete symptoms, filter out superficial noise, and evaluate urgency."
)
response = self.client.models.generate_content(
model=MODEL_ROUTING["triage"],
contents=raw_input,
config=types.GenerateContentConfig(
system_instruction=system_instruction,
response_mime_type="application/json",
response_schema=OpportunityRecord,
temperature=0.2,
),
)
record = OpportunityRecord.model_validate_json(response.text)
print(f" -> Extracted Opportunity ID: {record.opportunity_id} ({record.domain_context})")
return record
def stage_2_diagnose(self, opp: OpportunityRecord) -> DiagnosticSpec:
"""Stage 2: Root cause analysis and contradiction isolation."""
print(f"\n[Stage 2] Diagnosing root causes and contradictions for {opp.opportunity_id}...")
system_instruction = (
"You are a Root-Cause & First-Principles Systems Decomposer. "
"Analyze the opportunity record. Map root causes as rigorous causal chains. "
"Explicitly formulate engineering contradictions: system must improve Parameter A "
"without worsening Parameter B. Identify non-negotiable physical, operational, "
"or regulatory constraints."
)
prompt = (
f"Opportunity Data:\n"
f"Context: {opp.domain_context}\n"
f"Observed Symptoms: {json.dumps(opp.observed_symptoms)}\n"
f"Urgency Score: {opp.urgency_score}\n"
f"Summary: {opp.raw_source_summary}"
)
response = self.client.models.generate_content(
model=MODEL_ROUTING["deep_reasoning"],
contents=prompt,
config=types.GenerateContentConfig(
system_instruction=system_instruction,
response_mime_type="application/json",
response_schema=DiagnosticSpec,
temperature=0.3,
),
)
diag = DiagnosticSpec.model_validate_json(response.text)
print(f" -> Isolated {len(diag.contradictions)} contradictions and {len(diag.inviolable_constraints)} constraints.")
return diag
def stage_3_generate_and_critique(self, diag: DiagnosticSpec, n_target: int = 3) -> VettedSolutionsBatch:
"""Stage 3: Multi-persona generation swarm + Red Team Critic gate."""
print(f"\n[Stage 3] Synthesizing improvement candidates via multi-perspective swarm...")
# 3A: Swarm Generation
gen_instruction = (
f"You are a Multi-Perspective Inventive Solution Swarm. Generate diverse improvements "
f"for the provided DiagnosticSpec. You MUST produce candidates across three orthogonal personas:\n"
f"1. Mechanistic Minimizer: Solves the contradiction by pruning components or operations with zero new dependencies.\n"
f"2. Structural Inverter: Reorganizes topology, inverts standard operational order, or decouples phases.\n"
f"3. Cross-Domain Transfer: Imports proven structural mechanisms from unrelated disciplines.\n"
f"Ensure every candidate has a falsification metric and identifies secondary penalties."
)
gen_prompt = (
f"Diagnostic Spec:\n"
f"Opportunity ID: {diag.opportunity_id}\n"
f"Root Causes: {json.dumps(diag.root_cause_chains)}\n"
f"Contradictions: {diag.model_dump_json(include={'contradictions'})}\n"
f"Inviolable Constraints: {json.dumps(diag.inviolable_constraints)}"
)
response = self.client.models.generate_content(
model=MODEL_ROUTING["deep_reasoning"],
contents=gen_prompt,
config=types.GenerateContentConfig(
system_instruction=gen_instruction,
response_mime_type="application/json",
response_schema=SwarmGenerationBatch,
temperature=0.7,
),
)
raw_candidates = SwarmGenerationBatch.model_validate_json(response.text).candidates
print(f" -> Generated {len(raw_candidates)} candidate solutions. Passing to Red Team Critic...")
# 3B: Red Team Critic Pass
critic_instruction = (
"You are an Adversarial Red Team Technical Critic. Evaluate candidate improvements ruthlessly.\n"
"Rubric:\n"
"- Leverage Score (1-5): Does it eliminate root causes (5) or merely patch symptoms (1)?\n"
"- Boundary Integrity: False if ANY inviolable constraint is breached.\n"
"- Banality Penalty: True if it relies on generic buzzwords (e.g. 'AI dashboard') or cliché practices.\n"
"- Overall Score: 0-100 scale penalizing banality and constraint violations heavily."
)
class CriticReportBatch(BaseModel):
evaluations: List[CriticEvaluation]
critic_prompt = (
f"Constraints to enforce: {json.dumps(diag.inviolable_constraints)}\n"
f"Candidates to critique: {[c.model_dump() for c in raw_candidates]}"
)
critic_resp = self.client.models.generate_content(
model=MODEL_ROUTING["deep_reasoning"],
contents=critic_prompt,
config=types.GenerateContentConfig(
system_instruction=critic_instruction,
response_mime_type="application/json",
response_schema=CriticReportBatch,
temperature=0.2,
),
)
evals = CriticReportBatch.model_validate_json(critic_resp.text).evaluations
eval_map = {e.solution_id: e for e in evals}
# Filter and rank top N
sorted_candidates = sorted(
raw_candidates,
key=lambda c: eval_map.get(c.solution_id, CriticEvaluation(
solution_id=c.solution_id, leverage_score=1, boundary_integrity_pass=False,
banality_penalty=True, overall_score=0.0, critique_rationale="Unscored"
)).overall_score,
reverse=True
)
top_candidates = sorted_candidates[:n_target]
selected_evals = [eval_map[c.solution_id] for c in top_candidates if c.solution_id in eval_map]
print(f" -> Critic vetted {len(top_candidates)} top solutions (Threshold passed).")
return VettedSolutionsBatch(
opportunity_id=diag.opportunity_id,
vetted_solutions=top_candidates,
critic_reports=selected_evals
)
def stage_4_blueprint(self, opp: OpportunityRecord, solution: ImprovementCandidate, eval_report: CriticEvaluation) -> ImplementationArtifact:
"""Stage 4: Autonomous implementation blueprinter."""
print(f"\n[Stage 4] Generating implementation blueprint for {solution.solution_id}...")
system_instruction = (
"You are a Principal Implementation Architect. Generate a turnkey, actionable execution blueprint "
"for the selected improvement. Specify architecture breakdown, phased milestones, and falsifiable "
"verification criteria."
)
prompt = (
f"Context: {opp.domain_context}\n"
f"Solution ID: {solution.solution_id}\n"
f"Strategy: {solution.strategy_persona}\n"
f"Mechanism: {solution.core_mechanism}\n"
f"Expected Primary Effect: {solution.expected_primary_effect}\n"
f"Critic Notes: {eval_report.critique_rationale}\n"
f"Falsification Metric: {solution.falsification_metric}"
)
response = self.client.models.generate_content(
model=MODEL_ROUTING["deep_reasoning"],
contents=prompt,
config=types.GenerateContentConfig(
system_instruction=system_instruction,
response_mime_type="application/json",
response_schema=ImplementationArtifact,
temperature=0.3,
),
)
artifact = ImplementationArtifact.model_validate_json(response.text)
print(f" -> Blueprint generated for {artifact.solution_name}.")
return artifact
def export_markdown_report(self, opp: OpportunityRecord, diag: DiagnosticSpec, vetted: VettedSolutionsBatch, blueprints: List[ImplementationArtifact]):
"""Render complete audit and execution report to a structured markdown file."""
filename = os.path.join(OUTPUT_DIR, f"{opp.opportunity_id}_innovation_report.md")
with open(filename, "w", encoding="utf-8") as f:
f.write(f"# Innovation & Systems Optimization Report: {opp.opportunity_id}\n\n")
f.write(f"**Domain:** {opp.domain_context} \n")
f.write(f"**Urgency Metric:** {opp.urgency_score:.2f} / 1.00 \n\n")
f.write("## 1. Opportunity Signal & Context\n")
f.write(f"{opp.raw_source_summary}\n\n")
f.write("### Observed Symptoms\n")
for symptom in opp.observed_symptoms:
f.write(f"- {symptom}\n")
f.write("\n---\n\n")
f.write("## 2. Diagnostic & Contradiction Analysis\n")
f.write("### Root-Cause Causal Chains\n")
for chain in diag.root_cause_chains:
f.write(f"- {chain}\n")
f.write("\n### Structural Contradictions\n")
for c in diag.contradictions:
f.write(f"- **Improving:** `{c.improving_parameter}` vs **Worsening:** `{c.worsening_parameter}` \n")
f.write(f" *Tension:* {c.operational_tension}\n")
f.write("\n### Inviolable System Constraints\n")
for constraint in diag.inviolable_constraints:
f.write(f"- {constraint}\n")
f.write("\n---\n\n")
f.write("## 3. Vetted Recommendations & Adversarial Critique\n\n")
eval_map = {e.solution_id: e for e in vetted.critic_reports}
for sol in vetted.vetted_solutions:
ev = eval_map.get(sol.solution_id)
f.write(f"### [{sol.solution_id}] ({sol.strategy_persona})\n")
f.write(f"- **Mechanism:** {sol.core_mechanism}\n")
f.write(f"- **Contradiction Resolved:** {sol.contradiction_addressed}\n")
f.write(f"- **Expected Effect:** {sol.expected_primary_effect}\n")
f.write(f"- **Falsification Metric:** `{sol.falsification_metric}`\n")
f.write(f"- **Secondary Penalty:** {sol.secondary_penalty}\n")
if ev:
f.write(f"- **Critic Score:** {ev.overall_score}/100 (Leverage: {ev.leverage_score}/5, Banality Penalty: {ev.banality_penalty}) \n")
f.write(f" *Critic Review:* {ev.critique_rationale}\n")
f.write("\n")
f.write("---\n\n")
f.write("## 4. Implementation Blueprints\n\n")
for bp in blueprints:
f.write(f"### Implementation Spec: {bp.solution_name} ({bp.solution_id})\n\n")
f.write("#### Architectural Breakdown\n")
for comp in bp.architectural_breakdown:
f.write(f"- {comp}\n")
f.write("\n#### Execution Phases\n")
for phase in bp.execution_phases:
f.write(f"##### {phase.phase_name}\n")
f.write(f"- **Deliverables:** {', '.join(phase.key_deliverables)}\n")
f.write(f"- **Entry Criteria:** {phase.entry_criteria}\n")
f.write(f"- **Exit Criteria:** {phase.exit_criteria}\n")
f.write("\n#### Verification & Falsification Suite\n")
for test in bp.verification_suite:
f.write(f"- {test}\n")
f.write("\n#### Failure Modes & Fallback Plans\n")
for fm in bp.failure_modes_and_fallbacks:
f.write(f"- {fm}\n")
f.write("\n---\n\n")
print(f"\n[Complete] Exported full report to: {filename}")
return filename
def run(self, raw_input: str, n_recommendations: int = 3) -> str:
"""Run the end-to-end pipeline autonomously."""
opp = self.stage_1_ingest(raw_input)
diag = self.stage_2_diagnose(opp)
vetted = self.stage_3_generate_and_critique(diag, n_target=n_recommendations)
blueprints = []
eval_map = {e.solution_id: e for e in vetted.critic_reports}
for sol in vetted.vetted_solutions:
ev = eval_map.get(sol.solution_id, CriticEvaluation(
solution_id=sol.solution_id, leverage_score=3, boundary_integrity_pass=True,
banality_penalty=False, overall_score=75.0, critique_rationale="Passed without deep notes"
))
bp = self.stage_4_blueprint(opp, sol, ev)
blueprints.append(bp)
report_path = self.export_markdown_report(opp, diag, vetted, blueprints)
return report_path
# ---------------------------------------------------------------------------
# Execution Entry Point
# ---------------------------------------------------------------------------
if __name__ == "__main__":
# Test case: Unstructured engineering operational challenge
sample_raw_input = """
Incident Log / Review Findings:
Target Subsystem: Avionics Software Verification & Safety Hazard Auditing Pipeline.
Current Symptoms: Auditing requirements compliance (Class A-C) against updated procedural
standards takes 6-8 weeks per release cycle. Engineers spend 70% of their time cross-referencing
scattered requirement matrices and hazard reports manually. Vector RAG attempts produced
excessive semantic drift and hallucinated compliance mappings on edge-case safety clauses.
Strict air-gapped / offline environment limits external SaaS integrations, and the review
board requires 100% deterministic traceability for every verification assertion.
"""
engine = InnovationPipeline()
report_file = engine.run(sample_raw_input, n_recommendations=3)
print(f"\nPipeline execution finished. Report available at: {report_file}")
Execution Instructions
Ensure the Google GenAI SDK and Pydantic are installed:
pip install google-genai pydanticSet your environment variable:
export GEMINI_API_KEY="your-api-key"Run the script:
python autonomous_innovation_engine.pyAll output artifacts will be written directly to
workspace_artifacts/OPP-XXX_innovation_report.md.
From <https://gemini.google.com/app/92e8ae9b82a825b5> Google Gemini (3.8 Flash)
Comments
Post a Comment