#4792 Multi-Phase, AI Agent Team Approach to Innovation
#4792 Multi-Phase, AI Agent Team Approach to Innovation
#4792 Describe a multi-phase, AI Agent team approach to analyzing a user-specified system to identify needed and useful improvements
A robust multi-phase AI agent architecture for system analysis must treat the target system generically—whether mechanical, software, organizational, or hybrid—without hardcoding domain assumptions into the workflow. The architecture relies on specialized agents arranged across sequential phases, bound by a shared state and a feedback-driven review gate before output generation.
Architecture Overview
+-----------------------------+ | Ingestion & Decomposition | +--------------+--------------+ | v +-----------------------------+ | Structural & Causal Mapping | +--------------+--------------+ | v +-----------------------------+ | Bottleneck & Gap Diagnosis | +--------------+--------------+ | v +-----------------------------+ | Intervention Synthesis | +--------------+--------------+ | v +-----------------------------+ +----------->| Critic & Evaluation Gate | | +--------------+--------------+ | (Reject) | (Approve) | v +--------+--------+ +-----------------------------+ | Revision Router | | Roadmap & Recommendation | +-----------------+ +-----------------------------+
Phase 1: Ingestion, Boundary Definition, and Decomposition
The entry phase converts user-provided unstructured or semi-structured descriptions into a formalized system representation.
Boundary & Scope Agent: Identifies system boundaries, explicit constraints (e.g., physical limits, operational envelopes, cost limits), and external interfaces/dependencies. It prevents "scope creep" where the model invents external subsystems outside the user's intent.
Hierarchical Decomposition Agent: Recursively decomposes the system into a tree structure across discrete tiers:
Tier 0: Overall System / Mission Level
Tier 1: Primary Subsystems / Modules
Tier 2: Components / Functional Assemblies
Tier 3: Atomic Elements / Low-level interfaces
Artifact Generated: A structured JSON/Schema representation of nodes (components/functions) and bounded properties.
Phase 2: Structural, Functional, and Causal Mapping
This phase transitions the hierarchical decomposition into an interconnected functional topology.
Interface & Interaction Agent: Detects flows between nodes: mass, energy, signals, information, or mechanical load paths. It tags interfaces with directional attributes (e.g.,
Node_A -> transfers_torque -> Node_B).Causal Dependency Agent: Analyzes prerequisite states and operational sequences. If component X degrades or exhibits latency, what downstream components are affected?
Artifact Generated: An explicit Directed Acyclic Graph (DAG) or multi-relational knowledge graph mapping structural linkages, operational dependencies, and parameter bounds.
Phase 3: Bottleneck, Failure Mode, and Gap Diagnosis
Specialized diagnostic agents analyze the model created in Phase 2 against first principles, standard functional archetypes, and system constraints.
Stress & Contradiction Agent: Locates technical and operational contradictions—points where improving one parameter (e.g., speed, power, weight, throughput) adversely degrades another.
Single-Point-of-Failure (SPOF) & Redundancy Agent: Evaluates system resilience. It scans the graph for high-centrality nodes whose failure isolates critical functional pathways.
Interface Mismatch Agent: Checks for impedance mismatches, protocol incompatibilities, duty-cycle discrepancies, or physical wear/thermal choke points at boundaries.
Artifact Generated: A prioritized Deficiency & Opportunity Matrix, classifying issues by severity, likelihood, and systemic impact.
Phase 4: Improvement & Intervention Synthesis
Once deficiencies are mapped, a council of generative agents proposes candidate modifications.
First-Principles Optimization Agent: Strips the problem down to governing physical or logical equations to assess theoretical minimums (e.g., thermodynamic limits, minimal computational complexity) and suggests architectural simplifications.
Inventive Heuristics Agent: Applies lateral engineering heuristics (e.g., parameter changing, dynamic reconfiguration, decoupling tightly bound systems, inversion of control) to bypass identified contradictions.
Interface Elimination/Consolidation Agent: Proposes component integration or modularization to eliminate unreliable physical/data interfaces.
Artifact Generated: A candidate pool of structured improvement concepts, each paired with the specific bottleneck it addresses.
Phase 5: Multi-Criteria Critic and Evaluation Gate
Before solutions are packaged, they pass through a critical verification loop to prevent hallucinated feasibility or unacceptable trade-offs.
Feasibility & Constraint Auditor: Re-checks candidate improvements against the boundary definitions established in Phase 1 (cost, mass envelopes, thermal budgets, backward compatibility).
Side-Effect & Unintended Consequences Agent: Injects proposed interventions back into the causal graph from Phase 2 to predict secondary and tertiary failure modes.
Governance/Routing Rule:
Score < Threshold: Routes back to Phase 4 (or Phase 2 if the root cause was improperly modeled), attaching concrete critique tokens explaining why the concept failed.
Score $\ge$ Threshold: Passes candidates to the final phase.
Phase 6: Prioritization and Actionable Roadmap
The terminal phase structures the verified improvements into an actionable engineering delivery plan.
Trade-Off & Utility Scorer: Ranks improvements along two primary axes: Implementation Friction (cost, complexity, disruption) vs. Systemic Return (performance gain, reliability increase, risk reduction).
Synthesis & Delivery Agent: Formulates the final output into tiered recommendations:
Quick Wins: Low-friction, non-invasive parameter/configuration adjustments.
Architectural Upgrades: Subsystem-level re-engineering requiring design alterations.
Long-Term Paradigm Shifts: Fundamental topology or technology replacements.
Shared State & Orchestration Architecture
To keep the agent collective synchronized and prevent drift:
| Component | Function | Implementation Mechanism |
| Global State Machine | Controls phase transitions, parallel execution branches, and conditional rewind loops. | Directed graph orchestrator (e.g., LangGraph or custom event loop) |
| System Graph Store | Single source of truth containing component hierarchies, properties, and interface maps. | In-memory graph (NetworkX) or persistent property graph (Neo4j) |
| Context Ledger | Ephemeral blackboard recording assumptions, user constraints, and evaluation scores. | Structured Pydantic state model passed across node edges |
| Dynamic Execution Gate | Evaluates criteria thresholds before allowing Phase N \to N+1 progression. | Deterministic scoring functions combined with an LLM-based Critic Agent |
Show me the core Pydantic state model and LangGraph-style state machine transitions for this multi-phase system analysis framework.
Core Pydantic State Schema
The shared blackboard uses Pydantic models with Annotated reducers so parallel or iterative agent nodes can safely append findings without overwriting prior phase artifacts.
from typing import List, Dict, Any, Optional, Literal, Annotated
from pydantic import BaseModel, Field
import operator
# --- Atomic Primitive Schemas ---
class SystemNode(BaseModel):
node_id: str
name: str
tier: int = Field(..., description="0=System, 1=Subsystem, 2=Assembly, 3=Atomic Part")
parent_id: Optional[str] = None
properties: Dict[str, Any] = Field(default_factory=dict)
operational_limits: Dict[str, Any] = Field(default_factory=dict)
class SystemEdge(BaseModel):
source_id: str
target_id: str
interaction_type: str = Field(..., description="e.g., mass_flow, load_bearing, data, thermal")
properties: Dict[str, Any] = Field(default_factory=dict)
is_critical_path: bool = False
class Deficiency(BaseModel):
deficiency_id: str
affected_node_ids: List[str]
category: Literal["contradiction", "spof", "interface_mismatch", "capacity_choke"]
severity: float = Field(..., ge=0.0, le=1.0)
description: str
governing_constraints_violated: List[str] = Field(default_factory=list)
class CandidateIntervention(BaseModel):
intervention_id: str
target_deficiency_id: str
title: str
mechanism: str = Field(..., description="First-principles or structural mechanism of action")
affected_nodes: List[str]
estimated_friction: float = Field(..., ge=0.0, le=1.0, description="0=trivial, 1=full redesign")
estimated_impact: float = Field(..., ge=0.0, le=1.0, description="0=negligible, 1=transformative")
class CriticEvaluation(BaseModel):
intervention_id: str
approved: bool
score: float = Field(..., ge=0.0, le=1.0)
unintended_consequences: List[str]
critique_feedback: Optional[str] = None
class RankedRoadmapItem(BaseModel):
tier: Literal["quick_win", "architectural_upgrade", "paradigm_shift"]
intervention: CandidateIntervention
priority_rank: int
roi_ratio: float
# --- Core Blackboard / Graph State ---
class SystemAnalysisState(BaseModel):
# Raw Inputs & Scope
user_prompt: str
system_boundaries: Dict[str, Any] = Field(default_factory=dict)
hard_constraints: List[str] = Field(default_factory=list)
# Phase 1 & 2: Topology Model
nodes: Annotated[List[SystemNode], operator.add] = Field(default_factory=list)
edges: Annotated[List[SystemEdge], operator.add] = Field(default_factory=list)
# Phase 3: Diagnostics
deficiencies: Annotated[List[Deficiency], operator.add] = Field(default_factory=list)
# Phase 4: Solutions
candidates: Annotated[List[CandidateIntervention], operator.add] = Field(default_factory=list)
# Phase 5: Critic & Governance
critiques: Annotated[List[CriticEvaluation], operator.add] = Field(default_factory=list)
revision_iteration: int = 0
max_revisions: int = 3
rejection_reasons: Annotated[List[str], operator.add] = Field(default_factory=list)
# Phase 6: Final Deliverable
final_roadmap: List[RankedRoadmapItem] = Field(default_factory=list)
LangGraph State Machine Implementation
The state machine implements parallel diagnostic and synthesis fans, guarded by a conditional routing gate that diverts rejected solutions back into iterative refinement.
from typing import Literal
from langgraph.graph import StateGraph, START, END
# --- Node Stubs ---
def ingestion_and_scope_agent(state: SystemAnalysisState) -> Dict[str, Any]:
# Parse boundaries, operational envelopes, and constraints
return {
"system_boundaries": {"envelope": "generic_bounds"},
"hard_constraints": ["mass <= M_max", "duty_cycle >= 99.5%"],
"nodes": []
}
def hierarchical_decomposition_agent(state: SystemAnalysisState) -> Dict[str, Any]:
# Deconstruct system into multi-tier entities
return {"nodes": []}
def topological_mapping_agent(state: SystemAnalysisState) -> Dict[str, Any]:
# Extract interfaces, load transfers, and data/energy flows
return {"edges": []}
def contradiction_analysis_agent(state: SystemAnalysisState) -> Dict[str, Any]:
# Detect physical/operational parameter tradeoffs
return {"deficiencies": []}
def spof_and_resilience_agent(state: SystemAnalysisState) -> Dict[str, Any]:
# Identify high-centrality bottlenecks and single points of failure
return {"deficiencies": []}
def first_principles_synthesis_agent(state: SystemAnalysisState) -> Dict[str, Any]:
# Propose foundational structural/parametric changes
return {"candidates": []}
def inventive_heuristics_agent(state: SystemAnalysisState) -> Dict[str, Any]:
# Propose decoupling, inversion, or dynamic reconfiguration modifications
return {"candidates": []}
def critic_gate_agent(state: SystemAnalysisState) -> Dict[str, Any]:
# Audit candidates against constraints & compute secondary side-effects
# Generates approvals/rejections and updates iteration state
new_iteration = state.revision_iteration + 1
critique_results = []
rejections = []
for cand in state.candidates:
# Mock evaluation criteria logic
passed = cand.estimated_impact > 0.4 and cand.estimated_friction < 0.8
score = cand.estimated_impact / (cand.estimated_friction + 0.1)
critique = CriticEvaluation(
intervention_id=cand.intervention_id,
approved=passed,
score=min(score / 10.0, 1.0),
unintended_consequences=[] if passed else ["Violates operational margin under peak load"],
critique_feedback=None if passed else "Excessive friction without proportional gain."
)
critique_results.append(critique)
if not passed:
rejections.append(f"Candidate {cand.intervention_id} rejected: {critique.critique_feedback}")
return {
"critiques": critique_results,
"revision_iteration": new_iteration,
"rejection_reasons": rejections
}
def roadmap_delivery_agent(state: SystemAnalysisState) -> Dict[str, Any]:
# Rank approved items by utility and organize into tiered horizons
approved_ids = {c.intervention_id for c in state.critiques if c.approved}
approved_candidates = [c for c in state.candidates if c.intervention_id in approved_ids]
roadmap = []
for idx, cand in enumerate(approved_candidates):
ratio = cand.estimated_impact / (cand.estimated_friction + 0.01)
tier: Literal["quick_win", "architectural_upgrade", "paradigm_shift"]
if cand.estimated_friction < 0.3:
tier = "quick_win"
elif cand.estimated_friction < 0.7:
tier = "architectural_upgrade"
else:
tier = "paradigm_shift"
roadmap.append(
RankedRoadmapItem(
tier=tier,
intervention=cand,
priority_rank=idx + 1,
roi_ratio=ratio
)
)
return {"final_roadmap": roadmap}
# --- Conditional Router ---
def route_critic_decision(state: SystemAnalysisState) -> Literal["revision_loop", "deliver_roadmap", "abort_unfeasible"]:
approved_count = sum(1 for c in state.critiques if c.approved)
total_candidates = len(state.candidates)
# All approved or acceptable threshold met
if total_candidates > 0 and (approved_count / total_candidates) >= 0.7:
return "deliver_roadmap"
# Circuit breaker if solutions continuously fail constraints
if state.revision_iteration >= state.max_revisions:
if approved_count > 0:
return "deliver_roadmap" # Ship partial approved set
return "abort_unfeasible"
return "revision_loop"
# --- Workflow Graph Assembly ---
def build_system_analyzer_workflow() -> StateGraph:
workflow = StateGraph(SystemAnalysisState)
# 1. Register Nodes
workflow.add_node("ingestion_scope", ingestion_and_scope_agent)
workflow.add_node("decomposition", hierarchical_decomposition_agent)
workflow.add_node("topology_mapper", topological_mapping_agent)
workflow.add_node("diag_contradictions", contradiction_analysis_agent)
workflow.add_node("diag_spof", spof_and_resilience_agent)
workflow.add_node("synth_first_principles", first_principles_synthesis_agent)
workflow.add_node("synth_heuristics", inventive_heuristics_agent)
workflow.add_node("critic_gate", critic_gate_agent)
workflow.add_node("roadmap_delivery", roadmap_delivery_agent)
# 2. Add Phase Edges
workflow.add_edge(START, "ingestion_scope")
workflow.add_edge("ingestion_scope", "decomposition")
workflow.add_edge("decomposition", "topology_mapper")
# Fan-out: Parallel Diagnostics
workflow.add_edge("topology_mapper", "diag_contradictions")
workflow.add_edge("topology_mapper", "diag_spof")
# Fan-in to Fan-out: Parallel Synthesis
workflow.add_edge("diag_contradictions", "synth_first_principles")
workflow.add_edge("diag_contradictions", "synth_heuristics")
workflow.add_edge("diag_spof", "synth_first_principles")
workflow.add_edge("diag_spof", "synth_heuristics")
# Fan-in to Gate
workflow.add_edge("synth_first_principles", "critic_gate")
workflow.add_edge("synth_heuristics", "critic_gate")
# 3. Dynamic Routing Edge
workflow.add_conditional_edges(
"critic_gate",
route_critic_decision,
{
"revision_loop": "synth_heuristics", # Route feedback back to synthesis engine
"deliver_roadmap": "roadmap_delivery",
"abort_unfeasible": END
}
)
workflow.add_edge("roadmap_delivery", END)
return workflow.compile()
app = build_system_analyzer_workflow()
Key Structural Invariants
Deterministic Feedback Loop: The conditional router
route_critic_decisioninspectsstate.revision_iterationagainststate.max_revisions. This bounds recursive cost while routing negative critique tokens (state.rejection_reasons) directly to the synthesis engines on revision turns.Asynchronous Reducers: Fields like
nodes,edges,deficiencies, andcandidatesuseAnnotated[..., operator.add]. This ensures the parallel fan-out branches (diag_contradictionsanddiag_spof) safely merge discoveries without state collision.Domain Neutrality: The schemas express components purely in terms of topological rank (
tier), interface interactions (interaction_type), and quantitative friction/impact margins rather than hardcoding system-specific terminology.
Provide a Python application using Google Gemini API to automate as much of this workflow as possible
An end-to-end Python implementation using the google-genai SDK and Pydantic enforces structured JSON outputs at each phase. The script orchestrates decomposition, topological mapping, failure/bottleneck diagnosis, intervention generation, an auditing critic gate with an iterative feedback loop, and final roadmap synthesis.
Prerequisites
pip install google-genai pydantic networkx
Ensure your API key is set:
export GEMINI_API_KEY="your-gemini-api-key"
Python Application Implementation
import os
import json
from typing import List, Dict, Any, Optional, Literal
from pydantic import BaseModel, Field
import networkx as nx
from google import genai
from google.genai import types
# ---------------------------------------------------------------------------
# 1. Pydantic Schemas for Strict Structured Outputs
# ---------------------------------------------------------------------------
class SystemNode(BaseModel):
node_id: str = Field(..., description="Unique slug ID, e.g., 'subsys_power', 'part_bearing_01'")
name: str = Field(..., description="Canonical name")
tier: int = Field(..., description="0=System, 1=Subsystem, 2=Assembly, 3=Atomic Part")
parent_id: Optional[str] = Field(None, description="Parent node_id in the decomposition tree")
properties: Dict[str, str] = Field(default_factory=dict, description="Key functional or physical traits")
class SystemEdge(BaseModel):
source_id: str
target_id: str
interaction_type: str = Field(..., description="Type of interaction (e.g., mass_flow, torque, electrical, data)")
description: str
class IngestionTopologyOutput(BaseModel):
boundaries: List[str] = Field(..., description="Assumed and identified system envelopes")
hard_constraints: List[str] = Field(..., description="Operational, safety, and physical hard boundaries")
nodes: List[SystemNode]
edges: List[SystemEdge]
class Deficiency(BaseModel):
deficiency_id: str
affected_node_ids: List[str]
category: Literal["contradiction", "spof", "interface_mismatch", "capacity_choke"]
severity: float = Field(..., ge=0.0, le=1.0, description="0.0=negligible, 1.0=critical failure")
description: str
underlying_mechanism: str
class DiagnosisOutput(BaseModel):
deficiencies: List[Deficiency]
class CandidateIntervention(BaseModel):
intervention_id: str
target_deficiency_id: str
title: str
mechanism_of_action: str = Field(..., description="First-principles or topological change mechanism")
affected_nodes: List[str]
estimated_friction: float = Field(..., ge=0.0, le=1.0, description="0.0=drop-in trivial, 1.0=full structural redesign")
estimated_impact: float = Field(..., ge=0.0, le=1.0, description="0.0=marginal, 1.0=step-function improvement")
class SynthesisOutput(BaseModel):
candidates: List[CandidateIntervention]
class CriticEvaluation(BaseModel):
intervention_id: str
approved: bool
score: float = Field(..., ge=0.0, le=1.0)
unintended_consequences: List[str]
critique_feedback: Optional[str] = None
class CriticOutput(BaseModel):
evaluations: List[CriticEvaluation]
class RankedRoadmapItem(BaseModel):
priority_rank: int
tier: Literal["quick_win", "architectural_upgrade", "paradigm_shift"]
intervention_id: str
title: str
mechanism: str
impact: float
friction: float
roi_score: float
justification: str
class RoadmapOutput(BaseModel):
executive_summary: str
roadmap: List[RankedRoadmapItem]
# ---------------------------------------------------------------------------
# 2. Automated Multi-Phase Engine
# ---------------------------------------------------------------------------
class SystemAnalyzerEngine:
def __init__(self, model_name: str = "gemini-2.5-pro"):
#api_key = os.environ.get("GEMINI_API_KEY")
#if not api_key:
# raise ValueError("GEMINI_API_KEY environment variable is missing.")
self.client = genai.Client(api_key=api_key)
self.model = model_name
self.max_revisions = 2
def _call_agent(self, system_instruction: str, prompt: str, schema: Any) -> Any:
response = self.client.models.generate_content(
model=self.model,
contents=prompt,
config=types.GenerateContentConfig(
system_instruction=system_instruction,
response_mime_type="application/json",
response_schema=schema,
temperature=0.2,
),
)
return schema.model_validate_json(response.text)
def phase_1_and_2_ingestion_and_topology(self, raw_description: str) -> IngestionTopologyOutput:
sys_prompt = (
"You are a Principal Systems Architect. Ingest the user-specified system description. "
"Decompose it into a hierarchical tree (Tiers 0-3) and map directional interactions "
"(mass, load, data, torque, thermal). Express entities generically and rigorously."
)
return self._call_agent(sys_prompt, f"System Description:\n{raw_description}", IngestionTopologyOutput)
def phase_3_diagnosis(self, topology: IngestionTopologyOutput) -> DiagnosisOutput:
# Construct graph metrics using NetworkX to inject topological context
G = nx.DiGraph()
for node in topology.nodes:
G.add_node(node.node_id, name=node.name, tier=node.tier)
for edge in topology.edges:
G.add_edge(edge.source_id, edge.target_id, type=edge.interaction_type)
betweenness = nx.betweenness_centrality(G)
high_centrality_nodes = [
f"{node_id} (centrality: {score:.3f})"
for node_id, score in sorted(betweenness.items(), key=lambda x: x[1], reverse=True)[:5]
]
sys_prompt = (
"You are a Failure Mode and Systems Diagnostic Agent. Analyze the provided topology, boundaries, "
"constraints, and graph centrality data. Identify single points of failure (SPOFs), interface mismatches, "
"capacity bottlenecks, and engineering contradictions (where optimizing parameter A degrades parameter B)."
)
user_input = {
"boundaries": topology.boundaries,
"hard_constraints": topology.hard_constraints,
"nodes": [n.model_dump() for n in topology.nodes],
"edges": [e.model_dump() for e in topology.edges],
"highest_centrality_nodes_structural": high_centrality_nodes,
}
return self._call_agent(sys_prompt, json.dumps(user_input, indent=2), DiagnosisOutput)
def phase_4_synthesis(
self,
topology: IngestionTopologyOutput,
diagnosis: DiagnosisOutput,
prior_critiques: Optional[List[CriticEvaluation]] = None,
) -> SynthesisOutput:
sys_prompt = (
"You are an Advanced Engineering Synthesis Agent. Propose architectural, structural, and "
"first-principles interventions to resolve the identified deficiencies. Aim to decouple tight couplings, "
"eliminate single points of failure, or reconfigure interfaces. Provide concrete mechanisms of action."
)
user_input: Dict[str, Any] = {
"deficiencies": [d.model_dump() for d in diagnosis.deficiencies],
"system_nodes": [n.model_dump() for n in topology.nodes],
"hard_constraints": topology.hard_constraints,
}
if prior_critiques:
user_input["rejected_attempts_feedback"] = [
c.model_dump() for c in prior_critiques if not c.approved
]
return self._call_agent(sys_prompt, json.dumps(user_input, indent=2), SynthesisOutput)
def phase_5_critic_gate(
self,
topology: IngestionTopologyOutput,
diagnosis: DiagnosisOutput,
synthesis: SynthesisOutput,
) -> CriticOutput:
sys_prompt = (
"You are a Rigorous Feasibility and Safety Auditor. Evaluate candidate interventions against system "
"hard constraints, physical realism, and unintended second-order consequences. "
"Reject solutions that introduce severe secondary failure modes or have unreasonable friction without proportionate impact."
)
user_input = {
"constraints": topology.hard_constraints,
"deficiencies": [d.model_dump() for d in diagnosis.deficiencies],
"candidates_to_audit": [c.model_dump() for c in synthesis.candidates],
}
return self._call_agent(sys_prompt, json.dumps(user_input, indent=2), CriticOutput)
def phase_6_roadmap(
self,
synthesis: SynthesisOutput,
critic: CriticOutput,
) -> RoadmapOutput:
approved_ids = {c.intervention_id for c in critic.evaluations if c.approved}
approved_interventions = [c for c in synthesis.candidates if c.intervention_id in approved_ids]
sys_prompt = (
"You are a Chief Systems Strategist. Categorize approved engineering interventions into: "
"'quick_win' (low friction), 'architectural_upgrade' (moderate friction, high yield), or "
"'paradigm_shift' (fundamental redesign). Rank them deterministically by ROI and produce an executive roadmap."
)
user_input = {
"approved_interventions": [i.model_dump() for i in approved_interventions],
"critic_evaluations": [c.model_dump() for c in critic.evaluations if c.approved],
}
return self._call_agent(sys_prompt, json.dumps(user_input, indent=2), RoadmapOutput)
def run(self, raw_system_description: str) -> RoadmapOutput:
print("[1/5] Extracting System Boundaries, Nodes, and Topological Edges...")
topology = self.phase_1_and_2_ingestion_and_topology(raw_system_description)
print(f" Mapped {len(topology.nodes)} nodes across {len(topology.edges)} relational interfaces.")
print("[2/5] Running Structural & Contradiction Diagnostics...")
diagnosis = self.phase_3_diagnosis(topology)
print(f" Flagged {len(diagnosis.deficiencies)} potential failure modes/bottlenecks.")
iteration = 0
critiques: Optional[List[CriticEvaluation]] = None
while iteration <= self.max_revisions:
print(f"[3/5] Synthesizing Interventions (Iteration {iteration + 1}/{self.max_revisions + 1})...")
synthesis = self.phase_4_synthesis(topology, diagnosis, prior_critiques=critiques)
print(f" Generated {len(synthesis.candidates)} candidate interventions.")
print("[4/5] Passing Candidates Through Critic & Feasibility Gate...")
critic_result = self.phase_5_critic_gate(topology, diagnosis, synthesis)
critiques = critic_result.evaluations
approved = [c for c in critiques if c.approved]
print(f" Gate verdict: {len(approved)} approved, {len(critiques) - len(approved)} rejected.")
# Acceptance condition: At least 60% approved and at least one valid candidate
if len(critiques) > 0 and (len(approved) / len(critiques)) >= 0.6:
print(" Threshold met. Proceeding to delivery synthesis.")
break
iteration += 1
if iteration <= self.max_revisions:
print(" Critique rejection threshold hit. Rerouting feedback tokens to Synthesis Agent...")
print("[5/5] Compiling Prioritized Engineering Roadmap...")
final_roadmap = self.phase_6_roadmap(synthesis, critic_result)
return final_roadmap
# ---------------------------------------------------------------------------
# 3. Execution Verification
# ---------------------------------------------------------------------------
if __name__ == "__main__":
generic_system_description = """
High-Pressure Cryogenic Fluid Transfer Loop:
The system transfers liquid oxygen from a primary storage vessel through an insulated delivery line
to an injection manifold. The system consists of:
- Primary Storage Tank (Dewar vessel with vacuum jacket and relief valves)
- Electric Cryogenic Boost Pump (variable frequency drive, mechanical shaft seal)
- In-line Flow Control Valve (pneumatically actuated needle valve)
- Vacuum-Jacketed Transfer Line (flanged sections with Kel-F gaskets)
- Sensor Suite (upstream/downstream RTD temperature probes, pressure transducers)
- Destination Injection Manifold (distributes fluid across 8 parallel burner orifices)
Current Operating Issues:
The mechanical shaft seal on the boost pump frequently experiences thermal stress fractures
during initial chill-down cycles, causing helium purge pressure drops. Additionally, when the flow
control valve throttles below 30% open, cavitation occurs upstream of the manifold, introducing pressure
surges that disrupt downstream flow stability. Hard constraints: Overall mass budget cannot increase by
more than 15%, no exotic or uncertified sealing polymers, and zero leakage permitted.
"""
engine = SystemAnalyzerEngine()
result = engine.run(generic_system_description)
print("\n" + "=" * 80)
print("EXECUTIVE SUMMARY:")
print(result.executive_summary)
print("=" * 80)
for item in result.roadmap:
print(f"\n[Rank {item.priority_rank}] {item.title.upper()} ({item.tier})")
print(f" Mechanism: {item.mechanism}")
print(f" Impact: {item.impact:.2f} | Friction: {item.friction:.2f} | ROI: {item.roi_score:.2f}")
print(f" Justification: {item.justification}")
Core Mechanics of this Architecture
Topology Injection via Graph Centrality: Phase 3 constructs an in-memory
networkx.DiGraphdirectly from the nodes and edges validated in Phase 1 & 2. By computing betweenness centrality in Python and appending it to the prompt, the LLM receives exact topological bottlenecks (such as high-centrality transfer nodes) rather than relying purely on semantic text deduction.Deterministic Output Enforcement: Every phase enforces Pydantic schemas via the Gemini API's
response_schemaparameter. No manual regex or JSON string splitting is required.Targeted Critic Routing Loop: If candidate solutions violate hard constraints or trigger severe secondary consequences,
CriticEvaluation.critique_feedbacktokens are passed back into Phase 4 (prior_critiques). The generator alters its strategy based on the specific operational failures highlighted during the previous cycle.
Provide a prompt that converts an initial user problem statement into a generic_system_description as input to this workflow
Here is an optimized prompt designed to act as the front-end intake layer. It converts an informal, problem-centric user statement into the formalized, schema-ready generic_system_description block expected by the workflow.
System Ingestion Prompt
You are a Lead Systems Architect and Requirements Ingestion Engine.
Your task is to ingest a user's informal, messy, or problem-focused statement about a system and convert it into a structured, domain-neutral
generic_system_descriptionblock for downstream automated architectural analysis.Ingestion Guidelines
Formalize Boundaries & Context:
Clearly state the system's operational purpose, the primary transfer medium (e.g., mass, energy, data, force, signal), and explicit physical or functional envelopes.
Decompose Structural Constituents:
Extract and categorize components into discrete assemblies or functional blocks.
Specify component roles, mechanisms (e.g., actuation type, transmission method, control loop), and critical interfaces.
Do not invent components outside the user's intent, but formalize implicit elements required for the system to function (e.g., power feeds, return lines, mounting points, sensors).
Isolate Operating Bottlenecks & Failure Modes:
Translate colloquial problem descriptions into exact mechanical, thermodynamic, computational, or electrical phenomena (e.g., convert "gets too hot and shakes" into "thermal expansion causing misalignment and induced harmonic vibration").
Differentiate between root symptoms, trigger conditions (e.g., startup, transient load, low throttle), and propagation paths.
Surface Hard & Soft Constraints:
Extract all boundary constraints: weight/volume envelopes, material certifications, prohibited modifications, backward compatibility requirements, budget/mass margins, and safety-critical rules (e.g., zero leakage, fail-safe states).
If a constraint is implied but unstated, declare it explicitly as an assumed constraint.
Style & Tone:
Use objective, generic systems engineering terminology.
Avoid marketing fluff, subjective qualifiers, or prescriptive solution-hints (describe the system as it is, not how to fix it yet).
Required Output Format
Provide the output formatted strictly within the following markdown block:
[System Name / Functional Title]: [Primary Operational Purpose and Scope Envelope] Constituent Subsystems & Components: - [Component/Assembly 1 Name] ([Mechanism, Subsystem Role, Interfaces]) - [Component/Assembly 2 Name] ([Mechanism, Subsystem Role, Interfaces]) - [Component/Assembly 3 Name] ([Mechanism, Subsystem Role, Interfaces]) - [Component/Assembly N Name] ([Mechanism, Subsystem Role, Interfaces]) Observed Failure Modes, Bottlenecks, and Operational Issues: - [Issue 1]: [Trigger condition, affected component/interface, physical/functional mechanism, downstream impact] - [Issue 2]: [Trigger condition, affected component/interface, physical/functional mechanism, downstream impact] Operating Envelopes and Hard Constraints: - [Constraint 1: Physical/envelope boundaries, e.g., mass, volume, power budget] - [Constraint 2: Material, regulatory, or manufacturing restrictions] - [Constraint 3: Interface and backward compatibility limits] - [Constraint 4: Safety, reliability, or tolerance requirements]Input to Ingest:
{{USER_RAW_INPUT}}
Example Transformation
Raw User Input:
"Our industrial plastic injection molding unit keeps jamming on the mold ejector pins when we switch to high-output cycles. The hydraulic actuator that drives the ejector plate overheats after about 3 hours of continuous cycling, and the cooling jackets on the mold base aren't pulling heat away fast enough from the core pins. When it jams, the parts get deformed and we have to scrap the whole batch. Management won't let us buy a new chiller unit or change the mold steel grade because it's too expensive, and we can't lengthen the cycle time because we have to hit 120 parts an hour."
Resulting generic_system_description:
High-Throughput Cyclic Injection Molding Ejection System:
The system executes cyclic mechanical demolding and part ejection for thermoplastic injection molding under sustained high-frequency operational duty.
Constituent Subsystems & Components:
- Mold Tooling Base (Tool steel core assembly with integrated internal liquid cooling jackets and reciprocating ejector pin bores)
- Reciprocating Ejector Assembly (Array of sliding core ejector pins rigidly coupled to a centralized mechanical ejector plate)
- Hydraulic Linear Actuator (High-cycle hydraulic drive cylinder providing linear stroke actuation to the ejector plate)
- Closed-Loop Liquid Thermal Subsystem (External mold chiller loop circulating cooling fluid through internal mold base channels)
- Process Cycle Controller (Timing and pressure logic governor controlling injection, dwell, and ejection phases)
Observed Failure Modes, Bottlenecks, and Operational Issues:
- Actuator Thermal Saturation: Under sustained continuous duty (>3 hours), hydraulic drive cylinder fluid experiences thermal degradation and viscosity loss, causing stroke deceleration and actuator stall.
- Thermal Dissipation Deficit: Liquid cooling jackets exhibit insufficient thermal flux extraction from the core pins, resulting in differential thermal expansion of pins relative to base bores.
- Mechanical Galling & Seizure: Core pin thermal expansion combined with reduced actuator force produces mechanical binding in pin guide bores, resulting in incomplete demolding strokes and workpiece plastic deformation.
Operating Envelopes and Hard Constraints:
- Throughput Constraint: Minimum operational cadence fixed at >= 120 cycles/hour (maximum allowable cycle time <= 30 seconds).
- Capital & Infrastructure Limit: Chiller capacity and external thermal exchange hardware cannot be replaced or upgraded.
- Material Invariant: Tooling alloy and base core steel metallurgy cannot be altered or substituted.
- Quality Standard: Zero tolerance for part warpage or ejection-induced cosmetic deformation; zero batch scrap allowance.From <https://gemini.google.com/app/ebd60003c041e24e> Google Gemini (3.8 Flash)
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