#4785 AI Agent Team to Recommend Needed Improvements

#4785 AI Agent Team to Recommend Needed Improvements

#4785 Describe an AI agent team that evaluates a user-specified system and recommends needed improvements

An effective AI agent team for system evaluation operates as a coordinated, multi-role review board rather than a single monolithic model. By assigning distinct analytical personas, strict evaluation boundaries, and closed-loop feedback mechanisms, the team can dissect complex technical architectures, surface failure modes, and generate prioritized, actionable engineering recommendations.

Core Team Architecture & Roles

                     ┌──────────────────────────────┐
                     │     System Ingestion &       │
                     │    Decomposition Agent       │
                     └──────────────┬───────────────┘
                                    │
           ┌────────────────────────┼────────────────────────┐
           ▼                        ▼                        ▼
┌─────────────────────┐  ┌─────────────────────┐  ┌─────────────────────┐
│ Domain / Compliance │  │ Hazard & Risk       │  │ Performance & Scal- │
│ Evaluator           │  │ Assessor            │  │ ability Specialist  │
└──────────┬──────────┘  └──────────┬──────────┘  └──────────┬──────────┘
           │                        │                        │
           └────────────────────────┼────────────────────────┘
                                    │
                                    ▼
                     ┌──────────────────────────────┐
                     │    Synthesis & Solution      │
                     │          Architect           │
                     └──────────────┬───────────────┘
                                    │
                                    ▼
                     ┌──────────────────────────────┐
                     │      Critic / Auditor        │◄──┐ (Refinement Loop)
                     │          (Gate)              │───┘
                     └──────────────┬───────────────┘
                                    │ Passed Quality Threshold
                                    ▼
                     ┌──────────────────────────────┐
                     │   Executive Recommendations  │
                     │           Report             │
                     └──────────────────────────────┘

1. System Ingestion & Decomposition Agent

• Objective: Ingest system models, requirement specifications, API contracts, or architectural diagrams and translate them into a structured, queryable schema.

• Function: Breaks the target system into hierarchical tiers (subsystems, interfaces, functional components, data flows, and state machines). It establishes a shared graph or structural state so subsequent agents evaluate identical primitives.

2. Specialized Evaluation Panel (Parallel Review)

These agents assess the decomposed system concurrently against orthogonal axes:

• Domain & Standards Compliance Evaluator: Audits the design against domain constraints, interface standards, data schemas, and regulatory/functional specifications. It flags missing validations, contract mismatches, or non-compliant flows.

• Hazard & Failure Modes Assessor: Executes automated failure scenario analysis (e.g., single points of failure, unhandled edge cases, cascading timeouts, telemetry dropouts, race conditions). It ranks vulnerabilities by severity and likelihood.

• Performance & Operational Specialist: Examines latency paths, throughput bottlenecks, operational footprint, resource contention, and lifecycle maintainability.

3. Synthesis & Solution Architect

• Objective: Consolidate disparate evaluation findings into coherent improvement strategies.

• Function: Ingests the raw findings from the evaluation panel, clusters correlated deficiencies, and maps each root cause to concrete remediation patterns (e.g., circuit breakers, schema decoupling, caching strategies, redundancy paths). It drafts the initial system improvement roadmap.

4. The Critic / Verification Auditor (The Gate)

• Objective: Act as an adversarial reviewer to prevent hallucinated fixes, over-engineering, or conflicting recommendations.

• Function: Evaluates the proposed solutions against the original design constraints and resource trade-offs.

  • Refinement Flow: If an improvement introduces secondary risks or fails to resolve the underlying finding, the Critic routes actionable feedback directly back to the Synthesis Architect (or the respective Domain Specialist) for revision rather than approving sub-par recommendations.

  • Termination Flow: Once all critical issues meet predefined convergence thresholds, it signs off for final artifact generation.

End-to-End Operational Workflow

1. Baseline Specification: The user provides design artifacts (e.g., SysML diagrams, microservice specifications, state transition tables, or requirement sets).

2. Decomposition & Graph Mapping: The Ingestion Agent extracts components and dependencies, establishing an explicit model of components, interfaces, and boundary assumptions.

3. Parallel Multi-Lens Audit:

   • The Compliance Evaluator checks contracts and invariants.

   • The Hazard Assessor tests fault tolerance and edge cases.

   • The Performance Specialist stress-tests concurrency and throughput models.

4. Iterative Synthesis & Audit Loop: The Synthesis Architect drafts concrete modifications, which the Critic stress-tests. Sub-optimal remediations are rejected and revised through a controlled loop (capped at a fixed number of iterations to guarantee termination).

5. Report Delivery: Outputs a structured improvement roadmap categorized by implementation urgency (Critical, High, Advisory), accompanied by a trace linking every recommendation back to the original finding.

Structured Recommendation Output Schema

The final deliverable is organized into a verifiable, prioritized matrix:

Priority	Component / Interface	Identified Finding	Proposed Remediation	Trade-off / Impact
P1 - Critical	Ingestion Pipeline / Stream Worker	Lack of backpressure control causes unhandled memory exhaustion under burst conditions.	Implement a leaky-bucket rate limiter with dead-letter queue routing for unprocessable events.	Introduces minor message processing latency during burst spikes; requires storage for DLQ.
P2 - High	Authentication & State Store	Distributed locks fail open upon network partitions, risking concurrent write collisions.	Transition to a fencing-token pattern or consensus-backed leases (e.g., Raft-based coordination).	Slight increase in write transaction overhead; simplifies consistency verification.
P3 - Medium	Configuration Lifecycle	Dynamic property updates require a full service restart, dropping inflight tasks.	Decouple runtime configurations via atomic pointer swapping and hot-reloading watchers.	Requires rigorous state sanitization on reload to avoid orphan handles.

Provide the exact Pydantic/JSON schemas for inter-agent communication between the Decomposition, Evaluator, and Critic agents.

The inter-agent communication protocol relies on typed data models representing each lifecycle stage: system decomposition, domain-specific evaluation findings, synthesized remediation proposals, and critic gate verdicts.

Pydantic Data Models (Python 3.11+)

from __future__ import annotations
from enum import Enum
from typing import Annotated, Dict, List, Literal, Optional
from pydantic import BaseModel, Field


# ---------------------------------------------------------
# Common Enums & Primitives
# ---------------------------------------------------------

class SeverityLevel(str, Enum):
    CRITICAL = "Critical"
    HIGH = "High"
    MEDIUM = "Medium"
    LOW = "Low"
    ADVISORY = "Advisory"


class EvaluatorLens(str, Enum):
    COMPLIANCE = "Domain & Standards Compliance"
    HAZARDS = "Hazard & Failure Modes"
    PERFORMANCE = "Performance & Scalability"


class GateDecision(str, Enum):
    APPROVED = "Approved"
    REVISE_SYNTHESIS = "Revise_Synthesis"
    REVISE_EVALUATION = "Revise_Evaluation"
    REJECT_TERMINAL = "Reject_Terminal"


# ---------------------------------------------------------
# 1. Decomposition Agent -> Evaluator Panel
# ---------------------------------------------------------

class InterfaceContract(BaseModel):
    interface_id: str = Field(..., description="Unique ID for the interface")
    source_component_id: str
    target_component_id: str
    protocol_or_mechanism: str = Field(
        ..., description="Protocol or transport mechanism (e.g., gRPC, REST, IPC, SPI)"
    )
    payload_schema_ref: Optional[str] = None
    synchronous: bool = True


class SystemComponent(BaseModel):
    component_id: str = Field(..., description="Unique hierarchical identifier")
    name: str
    tier: str = Field(..., description="e.g., Subsystem, Service, Module, Worker")
    responsibilities: List[str]
    invariants: List[str] = Field(
        default_factory=list, description="Guaranteed system states or safety conditions"
    )
    dependencies: List[str] = Field(
        default_factory=list, description="List of component_ids this component depends on"
    )


class SystemDecompositionPayload(BaseModel):
    system_id: str
    system_name: str
    version: str
    global_constraints: List[str] = Field(
        default_factory=list, description="Global system boundary assumptions and limits"
    )
    components: List[SystemComponent]
    interfaces: List[InterfaceContract]


# ---------------------------------------------------------
# 2. Evaluator Agents -> Synthesis Architect
# ---------------------------------------------------------

class EvaluationFinding(BaseModel):
    finding_id: str = Field(..., description="Unique finding ID (e.g., FIND-HAZ-001)")
    lens: EvaluatorLens
    affected_component_ids: List[str]
    affected_interface_ids: List[str] = Field(default_factory=list)
    severity: SeverityLevel
    root_cause_hypothesis: str
    observed_gap: str = Field(
        ..., description="Difference between current specification and required behavior/standard"
    )
    evidence: List[str] = Field(
        ..., description="Specific references in the decomposition that confirm the issue"
    )


class EvaluationReportPayload(BaseModel):
    system_id: str
    evaluator_lens: EvaluatorLens
    findings: List[EvaluationFinding]


# ---------------------------------------------------------
# 3. Synthesis Architect -> Critic Agent
# ---------------------------------------------------------

class ProposedRemediation(BaseModel):
    remediation_id: str = Field(..., description="Unique remediation ID (e.g., REM-001)")
    addresses_finding_ids: List[str] = Field(
        ..., min_length=1, description="Traceability link to original evaluation findings"
    )
    priority: SeverityLevel
    target_component_ids: List[str]
    architectural_pattern: str = Field(
        ..., description="Design pattern applied (e.g., Circuit Breaker, Outbox Pattern, Token Bucket)"
    )
    detailed_specification: str
    introduced_tradeoffs: List[str] = Field(
        ..., description="Known trade-offs (e.g., added compute latency, increased memory foot-print)"
    )
    rollback_or_fallback_strategy: Optional[str] = None


class SynthesisProposalPayload(BaseModel):
    system_id: str
    iteration_index: int = Field(default=1, ge=1)
    remediations: List[ProposedRemediation]


# ---------------------------------------------------------
# 4. Critic Agent -> Synthesis Architect / Pipeline Gate
# ---------------------------------------------------------

class RemediationAudit(BaseModel):
    remediation_id: str
    verdict: Literal["Accept", "Reject", "Needs_Refinement"]
    hallucination_or_feasibility_flags: List[str] = Field(
        default_factory=list,
        description="Flags if fix assumes non-existent primitives or breaks system boundaries"
    )
    secondary_hazards_detected: List[str] = Field(
        default_factory=list,
        description="Side effects introduced by the fix that violate other constraints"
    )
    actionable_critique: Optional[str] = Field(
        None, description="Clear modification instructions for the Synthesis Architect"
    )


class CriticGatePayload(BaseModel):
    system_id: str
    iteration_index: int
    decision: GateDecision
    audits: List[RemediationAudit]
    next_action_routing: Literal["Synthesis_Architect", "Domain_Evaluators", "Publish_Report", "Halt"]
    overall_rationalization: str

Representative Wire JSON Instances

1. DecompositionPayload

{
  "system_id": "SYS-STREAM-01",
  "system_name": "Telemetry Ingestion Pipeline",
  "version": "1.2.0",
  "global_constraints": [
    "Maximum end-to-end processing latency < 500ms",
    "Zero data loss on node termination"
  ],
  "components": [
    {
      "component_id": "COMP-INGEST",
      "name": "Edge Event Gateway",
      "tier": "Ingress",
      "responsibilities": ["Receive raw event bursts", "Perform signature validation"],
      "invariants": ["Unauthenticated payloads must be dropped immediately"],
      "dependencies": []
    },
    {
      "component_id": "COMP-STORAGE",
      "name": "Timeseries Buffer Store",
      "tier": "Persistence",
      "responsibilities": ["Persist validated telemetry chunks"],
      "invariants": ["Write operations must be durable"],
      "dependencies": ["COMP-INGEST"]
    }
  ],
  "interfaces": [
    {
      "interface_id": "IF-INGEST-STORAGE",
      "source_component_id": "COMP-INGEST",
      "target_component_id": "COMP-STORAGE",
      "protocol_or_mechanism": "gRPC Streaming",
      "payload_schema_ref": "proto.telemetry.v1.Payload",
      "synchronous": true
    }
  ]
}

2. EvaluationFinding (Hazard Assessor output)

{
  "system_id": "SYS-STREAM-01",
  "evaluator_lens": "Hazard & Failure Modes",
  "findings": [
    {
      "finding_id": "FIND-HAZ-001",
      "lens": "Hazard & Failure Modes",
      "affected_component_ids": ["COMP-INGEST", "COMP-STORAGE"],
      "affected_interface_ids": ["IF-INGEST-STORAGE"],
      "severity": "Critical",
      "root_cause_hypothesis": "Synchronous streaming gRPC without backpressure triggers thread and buffer exhaustion during storage compaction phases.",
      "observed_gap": "Ingestion crashes with OutOfMemory errors when Timeseries Buffer experiences write latencies > 200ms.",
      "evidence": [
        "COMP-INGEST has no buffering mechanism defined in responsibilities.",
        "IF-INGEST-STORAGE synchronous property is true without rate-limit constraints."
      ]
    }
  ]
}

3. CriticGatePayload (Routing rejection back to Synthesis)

JSON

{
  "system_id": "SYS-STREAM-01",
  "iteration_index": 1,
  "decision": "Revise_Synthesis",
  "next_action_routing": "Synthesis_Architect",
  "overall_rationalization": "Remediation REM-001 proposes an in-memory ring buffer on COMP-INGEST, but violates the global constraint 'Zero data loss on node termination' due to lack of non-volatile spillover.",
  "audits": [
    {
      "remediation_id": "REM-001",
      "verdict": "Needs_Refinement",
      "hallucination_or_feasibility_flags": [],
      "secondary_hazards_detected": [
        "Uncommitted in-memory ring buffer events are destroyed during unexpected SIGKILL or host crash."
      ],
      "actionable_critique": "Replace volatile ring buffer with a local write-ahead log (WAL) or durable distributed queue (Kafka/Pulsar partition lease) before returning an ACK to the edge emitter."
    }
  ]
}

Provide this as a Python application using Google Gemini API

Below is a complete, self-contained Python application implementing the multi-agent system evaluation workflow using the current Google GenAI SDK (google-genai) and Pydantic.

It enforces structured outputs via response_schema at each step, runs the evaluation panel concurrently, passes proposed remediations through a critical audit gate, and loops back to the Synthesis Architect if the Critic flags defects or constraint violations.

Prerequisites

Install the official SDK and Pydantic:

pip install google-genai pydantic

Ensure your API key is exported:

export GEMINI_API_KEY="your-api-key-here"

Python Application (system_evaluator.py)

import os
import json
from enum import Enum
from typing import List, Literal, Optional
from concurrent.futures import ThreadPoolExecutor
from pydantic import BaseModel, Field
from google import genai
from google.genai import types

# -----------------------------------------------------------------------------
# 1. Pydantic Schemas for Inter-Agent Contracts
# -----------------------------------------------------------------------------

class SeverityLevel(str, Enum):
    CRITICAL = "Critical"
    HIGH = "High"
    MEDIUM = "Medium"
    LOW = "Low"
    ADVISORY = "Advisory"


class EvaluatorLens(str, Enum):
    COMPLIANCE = "Domain & Standards Compliance"
    HAZARDS = "Hazard & Failure Modes"
    PERFORMANCE = "Performance & Scalability"


class GateDecision(str, Enum):
    APPROVED = "Approved"
    REVISE_SYNTHESIS = "Revise_Synthesis"
    REJECT_TERMINAL = "Reject_Terminal"


class InterfaceContract(BaseModel):
    interface_id: str
    source_component_id: str
    target_component_id: str
    protocol_or_mechanism: str
    synchronous: bool


class SystemComponent(BaseModel):
    component_id: str
    name: str
    tier: str
    responsibilities: List[str]
    invariants: List[str]
    dependencies: List[str]


class SystemDecompositionPayload(BaseModel):
    system_id: str
    system_name: str
    version: str
    global_constraints: List[str]
    components: List[SystemComponent]
    interfaces: List[InterfaceContract]


class EvaluationFinding(BaseModel):
    finding_id: str
    lens: EvaluatorLens
    affected_component_ids: List[str]
    affected_interface_ids: List[str]
    severity: SeverityLevel
    root_cause_hypothesis: str
    observed_gap: str
    evidence: List[str]


class EvaluationReportPayload(BaseModel):
    system_id: str
    evaluator_lens: EvaluatorLens
    findings: List[EvaluationFinding]


class ProposedRemediation(BaseModel):
    remediation_id: str
    addresses_finding_ids: List[str]
    priority: SeverityLevel
    target_component_ids: List[str]
    architectural_pattern: str
    detailed_specification: str
    introduced_tradeoffs: List[str]


class SynthesisProposalPayload(BaseModel):
    system_id: str
    iteration_index: int
    remediations: List[ProposedRemediation]


class RemediationAudit(BaseModel):
    remediation_id: str
    verdict: Literal["Accept", "Reject", "Needs_Refinement"]
    hallucination_or_feasibility_flags: List[str]
    secondary_hazards_detected: List[str]
    actionable_critique: Optional[str] = None


class CriticGatePayload(BaseModel):
    system_id: str
    iteration_index: int
    decision: GateDecision
    audits: List[RemediationAudit]
    overall_rationalization: str


# -----------------------------------------------------------------------------
# 2. Agent Node Implementations
# -----------------------------------------------------------------------------

MODEL_NAME = "gemini-2.5-flash"

class SystemEvaluatorWorkflow:
    def __init__(self):
        # Uses GEMINI_API_KEY from environment automatically
        self.client = genai.Client()

    def run_decomposition(self, raw_system_spec: str) -> SystemDecompositionPayload:
        """Decomposition Agent: Parses raw specification into a formal system model."""
        prompt = f"""
You are the System Ingestion & Decomposition Agent.
Analyze the following system specification and decompose it into components, interfaces,
invariants, dependencies, and explicit global constraints.

Raw Specification:
{raw_system_spec}
"""
        response = self.client.models.generate_content(
            model=MODEL_NAME,
            contents=prompt,
            config=types.GenerateContentConfig(
                response_mime_type="application/json",
                response_schema=SystemDecompositionPayload,
                temperature=0.1,
            ),
        )
        return SystemDecompositionPayload.model_validate_json(response.text)

    def run_evaluator_lens(
        self, decomposition: SystemDecompositionPayload, lens: EvaluatorLens
    ) -> EvaluationReportPayload:
        """Domain Evaluator: Audits decomposition against a specific technical lens."""
        prompt = f"""
You are the {lens.value} Evaluator.
Analyze the provided system decomposition strictly through your analytical specialty.
Identify structural gaps, unhandled failure modes, or non-functional risks.
Back every finding with specific components, interfaces, or invariants as evidence.

System Model:
{decomposition.model_dump_json(indent=2)}
"""
        response = self.client.models.generate_content(
            model=MODEL_NAME,
            contents=prompt,
            config=types.GenerateContentConfig(
                response_mime_type="application/json",
                response_schema=EvaluationReportPayload,
                temperature=0.2,
            ),
        )
        return EvaluationReportPayload.model_validate_json(response.text)

    def run_synthesis(
        self,
        decomposition: SystemDecompositionPayload,
        all_findings: List[EvaluationFinding],
        iteration: int,
        critic_feedback: Optional[CriticGatePayload] = None,
    ) -> SynthesisProposalPayload:
        """Synthesis Architect: Crafts architectural remediations addressing audit findings."""
        feedback_context = (
            f"\nPrevious Critic Audits & Rejections:\n{critic_feedback.model_dump_json(indent=2)}"
            if critic_feedback
            else "No prior audit feedback; this is the initial draft."
        )

        prompt = f"""
You are the Lead Solution Architect.
Design high-reliability remediations addressing the reported evaluation findings.
Every proposed remediation MUST trace back to specific finding IDs and respect the global constraints.
If resolving previous critique, address every flagged secondary hazard and feasibility issue directly.

Iteration: {iteration}
Global Constraints: {json.dumps(decomposition.global_constraints)}
{feedback_context}

Consolidated Findings:
{json.dumps([f.model_dump() for f in all_findings], indent=2)}
"""
        response = self.client.models.generate_content(
            model=MODEL_NAME,
            contents=prompt,
            config=types.GenerateContentConfig(
                response_mime_type="application/json",
                response_schema=SynthesisProposalPayload,
                temperature=0.2,
            ),
        )
        return SynthesisProposalPayload.model_validate_json(response.text)

    def run_critic(
        self,
        decomposition: SystemDecompositionPayload,
        proposal: SynthesisProposalPayload,
    ) -> CriticGatePayload:
        """Critic Agent (The Gate): Stress-tests remediations against boundary conditions."""
        prompt = f"""
You are the Adversarial Verification Auditor and Quality Gate.
Rigorously critique the proposed remediations against the original system constraints.
Check for:
1. Feasibility & Hallucinations: Does the fix assume non-existent primitives or unrealized protocols?
2. Secondary Hazards: Does this fix introduce deadlocks, excessive latency, or data loss risks?
3. Trade-offs: Are operational impacts acceptable?

If any Critical or High priority item is deficient or introduces secondary hazards,
set decision to 'Revise_Synthesis' and provide actionable critique.
Only set decision to 'Approved' if all proposed remediations are architecturally sound.

System Constraints:
{json.dumps(decomposition.global_constraints, indent=2)}

Proposed Remediations (Iteration {proposal.iteration_index}):
{proposal.model_dump_json(indent=2)}
"""
        response = self.client.models.generate_content(
            model=MODEL_NAME,
            contents=prompt,
            config=types.GenerateContentConfig(
                response_mime_type="application/json",
                response_schema=CriticGatePayload,
                temperature=0.1,
            ),
        )
        return CriticGatePayload.model_validate_json(response.text)

    def execute_evaluation_workflow(
        self, raw_system_spec: str, max_iterations: int = 3
    ) -> tuple[SynthesisProposalPayload, CriticGatePayload]:
        """Orchestrates decomposition, parallel evaluations, and the synthesis-critic loop."""
        print("[1/4] Running Decomposition Agent...")
        decomposition = self.run_decomposition(raw_system_spec)
        print(f"      Decomposed system: {decomposition.system_name} ({len(decomposition.components)} components, {len(decomposition.interfaces)} interfaces)")

        print("[2/4] Running Parallel Evaluator Panel...")
        lenses = [
            EvaluatorLens.COMPLIANCE,
            EvaluatorLens.HAZARDS,
            EvaluatorLens.PERFORMANCE,
        ]
        
        all_findings: List[EvaluationFinding] = []
        with ThreadPoolExecutor(max_workers=3) as executor:
            futures = [
                executor.submit(self.run_evaluator_lens, decomposition, lens)
                for lens in lenses
            ]
            for future in futures:
                report = future.result()
                all_findings.extend(report.findings)
                print(f"      {report.evaluator_lens.value}: surfaced {len(report.findings)} findings.")

        # Iterative Synthesis <-> Critic Loop
        critic_payload: Optional[CriticGatePayload] = None
        synthesis_proposal: Optional[SynthesisProposalPayload] = None

        for iteration in range(1, max_iterations + 1):
            print(f"[3/4] Running Synthesis Architect (Iteration {iteration}/{max_iterations})...")
            synthesis_proposal = self.run_synthesis(
                decomposition=decomposition,
                all_findings=all_findings,
                iteration=iteration,
                critic_feedback=critic_payload,
            )

            print(f"[4/4] Critic Gate evaluating proposed remediations...")
            critic_payload = self.run_critic(decomposition, synthesis_proposal)
            print(f"      Gate Decision: {critic_payload.decision.value}")

            if critic_payload.decision == GateDecision.APPROVED:
                print("      Architectural sign-off achieved.")
                break
            elif iteration < max_iterations:
                print(f"      Feedback routed back to Synthesis Architect: {critic_payload.overall_rationalization[:120]}...")
            else:
                print("      Reached maximum refinement iterations. Finalizing current state.")

        return synthesis_proposal, critic_payload


# -----------------------------------------------------------------------------
# 3. Execution Entrypoint
# -----------------------------------------------------------------------------

if __name__ == "__main__":
    sample_system_specification = """
    System: Edge Telemetry Ingestion Pipeline (v1.0.0)
    
    Architecture Overview:
    - Ingress Gateway (INGEST-01): Receives bursts of raw binary telemetry over UDP from external 
      devices. Decodes packets into JSON. Forwards parsed events synchronously via HTTP POST 
      to the Storage Writer. Drops packets if the internal thread pool is saturated.
    - Storage Writer (STORAGE-01): Batches incoming events and executes synchronous insert 
      transactions to an on-premise PostgreSQL cluster.
    - Notification Hub (NOTIF-01): Queries the database every 10 seconds for critical alarms 
      and sends batched webhook alerts to external responders.
      
    Global Constraints & Requirements:
    - Zero data loss tolerance for telemetry packets under network partitions or burst traffic.
    - Telemetry end-to-end processing latency must stay strictly below 250ms.
    - The edge environment has 16GB total RAM and intermittent WAN connectivity.
    """

    evaluator = SystemEvaluatorWorkflow()
    final_proposal, final_audit = evaluator.execute_evaluation_workflow(
        sample_system_specification, max_iterations=2
    )

    print("\n" + "=" * 80)
    print("FINAL APPROVED SYSTEM REMEDIATIONS")
    print("=" * 80)
    for rem in final_proposal.remediations:
        print(f"\n[{rem.priority.value}] {rem.remediation_id}: {rem.architectural_pattern}")
        print(f"Addresses Findings : {', '.join(rem.addresses_finding_ids)}")
        print(f"Target Components  : {', '.join(rem.target_component_ids)}")
        print(f"Specification      : {rem.detailed_specification}")
        print(f"Trade-offs         : {'; '.join(rem.introduced_tradeoffs)}")

    print("\n" + "=" * 80)
    print("CRITIC AUDIT SUMMARY")
    print("=" * 80)
    print(f"Decision: {final_audit.decision.value}")
    print(f"Rationalization: {final_audit.overall_rationalization}")

Implementation Details

1. Native Structured Outputs: Each call passes the Pydantic class directly into config=types.GenerateContentConfig(response_schema=..., response_mime_type="application/json"). The API guarantees the output conforms to the schema, bypassing regex and parsing failures.

2. Concurrent Evaluation: The three evaluator lenses (Domain Compliance, Hazard & Failure Modes, and Performance) run in parallel via Python's standard ThreadPoolExecutor.

3. Closed-Loop Convergence: The CriticGatePayload enforces a feedback loop. If the Critic issues a Revise_Synthesis verdict, the exact audit findings and rationalizations are injected into the subsequent Synthesis Architect prompt.

Write an artifact exporter that compiles the approved remediations and critic audit into an executive Markdown report.

I will now generate an artifact exporter module that takes the typed payloads from the evaluation workflow and compiles them into an executive Markdown report.

System Evaluation Report Exporter

from system_evaluator import (

CriticGatePayload,

EvaluationFinding,

GateDecision,

SeverityLevel,

SynthesisProposalPayload,

SystemDecompositionPayload,

)

class ExecutiveReportExporter:

"""Compiles structured agent evaluation outputs into an executive markdown report."""

def __init__(
    self,
    decomposition: SystemDecompositionPayload,
    findings: List[EvaluationFinding],
    proposal: SynthesisProposalPayload,
    critic_audit: CriticGatePayload,
    generated_at: Optional[datetime] = None,
):
    self.decomposition = decomposition
    self.findings = findings
    self.proposal = proposal
    self.critic_audit = critic_audit
    self.generated_at = generated_at or datetime.now(timezone.utc)

def _render_badge(self, severity: SeverityLevel) -> str:
    colors = {
        SeverityLevel.CRITICAL: "🔴 **CRITICAL**",
        SeverityLevel.HIGH: "🟠 **HIGH**",
        SeverityLevel.MEDIUM: "🟡 **MEDIUM**",
        SeverityLevel.LOW: "🔵 **LOW**",
        SeverityLevel.ADVISORY: "⚪ **ADVISORY**",
    }
    return colors.get(severity, f"**{severity.value}**")

def _render_gate_verdict(self, decision: GateDecision) -> str:
    icons = {
        GateDecision.APPROVED: "✅ **APPROVED (Sign-off Granted)**",
        GateDecision.REVISE_SYNTHESIS: "⚠️ **REVISION REQUIRED (Gate Withheld)**",
        GateDecision.REJECT_TERMINAL: "⛔ **REJECTED (Structural Infeasibility)**",
    }
    return icons.get(decision, decision.value)

def generate_markdown(self) -> str:
    total_findings = len(self.findings)
    critical_findings = sum(1 for f in self.findings if f.severity == SeverityLevel.CRITICAL)
    high_findings = sum(1 for f in self.findings if f.severity == SeverityLevel.HIGH)

    # Finding lookup map for traceability links
    finding_map = {f.finding_id: f for f in self.findings}

    doc = []

    # Document Header
    doc.append(f"# Executive Technical Evaluation & Architecture Review")
    doc.append(f"**Target System:** `{self.decomposition.system_name}` (v{self.decomposition.version})  ")
    doc.append(f"**Evaluation System ID:** `{self.decomposition.system_id}`  ")
    doc.append(f"**Audit Timestamp:** {self.generated_at.strftime('%Y-%m-%d %H:%M:%S UTC')}  ")
    doc.append(f"**Sign-off Status:** {self._render_gate_verdict(self.critic_audit.decision)}  ")
    doc.append(f"**Audit Cycles Completed:** {self.proposal.iteration_index}")
    doc.append("\n---\n")

    # 1. Executive Summary
    doc.append("## 1. Executive Summary")
    doc.append(
        f"An automated multi-agent architecture panel evaluated **{self.decomposition.system_name}** "
        f"across functional, hazard/reliability, and performance domains. "
        f"The evaluation surfaced **{total_findings} distinct findings** "
        f"({critical_findings} Critical, {high_findings} High)."
    )
    doc.append("")
    doc.append("### Auditor Verification Gate")
    doc.append(f"> **Verdict: {self.critic_audit.decision.value}**  \n>  \n> {self.critic_audit.overall_rationalization}")
    doc.append("")

    # 2. System Baseline Overview
    doc.append("## 2. System Baseline & Boundary Model")
    doc.append(f"- **Components Ingested:** {len(self.decomposition.components)}")
    doc.append(f"- **Inter-service Interfaces:** {len(self.decomposition.interfaces)}")
    if self.decomposition.global_constraints:
        doc.append("- **Enforced Global Constraints:**")
        for c in self.decomposition.global_constraints:
            doc.append(f"  - `{c}`")
    doc.append("")

    # 3. Master Remediation Roadmap
    doc.append("## 3. Approved Technical Remediations")
    doc.append(
        "The following architectural remedies were synthesized by the Solution Architect "
        "and passed verification against operational boundaries and hazard models.\n"
    )

    doc.append("| Priority | Remediation ID | Pattern | Target Component(s) | Addressed Finding(s) |")
    doc.append("| :--- | :--- | :--- | :--- | :--- |")
    for rem in sorted(self.proposal.remediations, key=lambda r: r.priority.value):
        doc.append(
            f"| {self._render_badge(rem.priority)} | `{rem.remediation_id}` | {rem.architectural_pattern} | "
            f"`{', '.join(rem.target_component_ids)}` | `{', '.join(rem.addresses_finding_ids)}` |"
        )
    doc.append("\n")

    # Detailed Remediation Breakdown
    doc.append("### Remediation Deep-Dives")
    for idx, rem in enumerate(self.proposal.remediations, 1):
        audit_item = next((a for a in self.critic_audit.audits if a.remediation_id == rem.remediation_id), None)
        verdict_badge = f"`{audit_item.verdict}`" if audit_item else "`Unreviewed`"

        doc.append(f"#### 3.{idx}. [{rem.priority.value}] {rem.remediation_id}: {rem.architectural_pattern}")
        doc.append(f"- **Target Components:** `{', '.join(rem.target_component_ids)}`")
        doc.append(f"- **Critic Verification:** {verdict_badge}")
        doc.append(f"- **Specification:**\n  {rem.detailed_specification}")
        
        if rem.introduced_tradeoffs:
            doc.append("- **Accepted Trade-offs & Operational Impact:**")
            for t in rem.introduced_tradeoffs:
                doc.append(f"  - {t}")

        if audit_item and audit_item.actionable_critique:
            doc.append(f"- **Critic Audit Notes:** {audit_item.actionable_critique}")

        # Linked findings evidence
        linked = [finding_map[fid] for fid in rem.addresses_finding_ids if fid in finding_map]
        if linked:
            doc.append("- **Deficiency Context Resolved:**")
            for lf in linked:
                doc.append(f"  - *{lf.finding_id}* ({lf.lens.value}): {lf.observed_gap}")
        doc.append("")

    # 4. Appendix: Complete Findings Ledger
    doc.append("## 4. Evaluation Findings Ledger (Appendix)")
    doc.append("| Finding ID | Lens | Severity | Component(s) | Root Cause Hypothesis |")
    doc.append("| :--- | :--- | :--- | :--- | :--- |")
    for f in self.findings:
        doc.append(
            f"| `{f.finding_id}` | {f.lens.value} | {self._render_badge(f.severity)} | "
            f"`{', '.join(f.affected_component_ids)}` | {f.root_cause_hypothesis} |"
        )
    doc.append("\n---\n*Report compiled via Gemini Multi-Agent System Evaluation Pipeline.*")

    return "\n".join(doc)

def write_to_file(self, output_filepath: str | Path) -> Path:
    path = Path(output_filepath)
    path.parent.mkdir(parents=True, exist_ok=True)
    content = self.generate_markdown()
    path.write_text(content, encoding="utf-8")
    return path

How to integrate with the workflow

To attach the report exporter directly to your existing system_evaluator.py, update execute_evaluation_workflow or call it after completion:

from report_exporter import ExecutiveReportExporter

# At the conclusion of execute_evaluation_workflow:
exporter = ExecutiveReportExporter(
    decomposition=decomposition,
    findings=all_findings,
    proposal=final_proposal,
    critic_audit=final_audit,
)

report_path = exporter.write_to_file("reports/system_evaluation_report.md")
print(f"Executive report successfully written to {report_path.resolve()}")

The generated report features:

• Executive Header & Gate Verdict: Clear sign-off status and auditor rationale at the top.

• Traceability Matrix: Explicit cross-linking connecting each proposed architectural fix to its root finding IDs and target components.

• Accepted Trade-offs: Highlights latency, memory, or throughput impacts introduced by the fixes.

• Audit Ledger: Tabular catalog of all findings categorized by evaluation lens and severity.

Bring it altogether as a Python application using Google Gemini API

Here is the unified, single-file Python application combining the Pydantic schemas, the Gemini API multi-agent evaluation workflow (decomposition, parallel review panel, synthesis-critic feedback loop), and the Executive Markdown Report Exporter.

Prerequisites

pip install google-genai pydantic

export GEMINI_API_KEY="your-api-key-here"

Complete Python Application (system_evaluator_suite.py)

import os
import json
from datetime import datetime, timezone
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
from concurrent.futures import ThreadPoolExecutor

from pydantic import BaseModel, Field
from google import genai
from google.genai import types

# =============================================================================
# 1. Pydantic Schemas & Inter-Agent Contracts
# =============================================================================

class SeverityLevel(str, Enum):
    CRITICAL = "Critical"
    HIGH = "High"
    MEDIUM = "Medium"
    LOW = "Low"
    ADVISORY = "Advisory"


class EvaluatorLens(str, Enum):
    COMPLIANCE = "Domain & Standards Compliance"
    HAZARDS = "Hazard & Failure Modes"
    PERFORMANCE = "Performance & Scalability"


class GateDecision(str, Enum):
    APPROVED = "Approved"
    REVISE_SYNTHESIS = "Revise_Synthesis"
    REJECT_TERMINAL = "Reject_Terminal"


class InterfaceContract(BaseModel):
    interface_id: str
    source_component_id: str
    target_component_id: str
    protocol_or_mechanism: str
    synchronous: bool


class SystemComponent(BaseModel):
    component_id: str
    name: str
    tier: str
    responsibilities: List[str]
    invariants: List[str]
    dependencies: List[str]


class SystemDecompositionPayload(BaseModel):
    system_id: str
    system_name: str
    version: str
    global_constraints: List[str]
    components: List[SystemComponent]
    interfaces: List[InterfaceContract]


class EvaluationFinding(BaseModel):
    finding_id: str
    lens: EvaluatorLens
    affected_component_ids: List[str]
    affected_interface_ids: List[str]
    severity: SeverityLevel
    root_cause_hypothesis: str
    observed_gap: str
    evidence: List[str]


class EvaluationReportPayload(BaseModel):
    system_id: str
    evaluator_lens: EvaluatorLens
    findings: List[EvaluationFinding]


class ProposedRemediation(BaseModel):
    remediation_id: str
    addresses_finding_ids: List[str]
    priority: SeverityLevel
    target_component_ids: List[str]
    architectural_pattern: str
    detailed_specification: str
    introduced_tradeoffs: List[str]


class SynthesisProposalPayload(BaseModel):
    system_id: str
    iteration_index: int
    remediations: List[ProposedRemediation]


class RemediationAudit(BaseModel):
    remediation_id: str
    verdict: Literal["Accept", "Reject", "Needs_Refinement"]
    hallucination_or_feasibility_flags: List[str]
    secondary_hazards_detected: List[str]
    actionable_critique: Optional[str] = None


class CriticGatePayload(BaseModel):
    system_id: str
    iteration_index: int
    decision: GateDecision
    audits: List[RemediationAudit]
    overall_rationalization: str


# =============================================================================
# 2. Executive Markdown Report Exporter
# =============================================================================

class ExecutiveReportExporter:
    """Compiles evaluation payloads, critic audits, and remediation proposals into a Markdown report."""

    def __init__(
        self,
        decomposition: SystemDecompositionPayload,
        findings: List[EvaluationFinding],
        proposal: SynthesisProposalPayload,
        critic_audit: CriticGatePayload,
    ):
        self.decomposition = decomposition
        self.findings = findings
        self.proposal = proposal
        self.critic_audit = critic_audit

    def generate_markdown(self) -> str:
        timestamp = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC")
        status_banner = "PASSED" if self.critic_audit.decision == GateDecision.APPROVED else "FAILED / REVISION NEEDED"

        md = [
            f"# Executive Architecture Review: {self.decomposition.system_name}",
            f"**System ID:** `{self.decomposition.system_id}` | **Version:** `{self.decomposition.version}` | **Generated:** `{timestamp}`",
            "",
            "## 1. Executive Summary & Gate Verdict",
            "",
            f"> **Audit Gate Status: {status_banner}**  ",
            f"> **Decision:** `{self.critic_audit.decision.value}` (Evaluated across {self.proposal.iteration_index} iteration(s))  ",
            f"> **Auditor Rationalization:** {self.critic_audit.overall_rationalization}",
            "",
            "---",
            "",
            "## 2. System Baseline & Global Boundaries",
            "",
            "### Global Constraints",
            *(f"- {c}" for c in self.decomposition.global_constraints),
            "",
            "### System Topology Overview",
            "| Component ID | Tier | Dependencies | Primary Responsibilities |",
            "| :--- | :--- | :--- | :--- |",
        ]

        for comp in self.decomposition.components:
            deps = ", ".join(comp.dependencies) if comp.dependencies else "*None*"
            resps = "<br>".join(f"• {r}" for r in comp.responsibilities)
            md.append(f"| `{comp.component_id}` | {comp.tier} | {deps} | {resps} |")

        md.extend([
            "",
            "---",
            "",
            "## 3. Approved Architectural Remediations",
            "",
            "| Remediation ID | Priority | Pattern | Target Components | Addresses Findings |",
            "| :--- | :--- | :--- | :--- | :--- |",
        ])

        audit_lookup = {a.remediation_id: a for a in self.critic_audit.audits}

        for rem in self.proposal.remediations:
            targets = ", ".join(f"`{c}`" for c in rem.target_component_ids)
            findings = ", ".join(f"`{f}`" for f in rem.addresses_finding_ids)
            md.append(f"| `{rem.remediation_id}` | **{rem.priority.value}** | {rem.architectural_pattern} | {targets} | {findings} |")

        md.extend(["", "### Remediation Specifications & Trade-off Analysis", ""])

        for rem in self.proposal.remediations:
            audit = audit_lookup.get(rem.remediation_id)
            verdict_badge = audit.verdict if audit else "Unknown"

            md.extend([
                f"#### [{rem.priority.value}] `{rem.remediation_id}`: {rem.architectural_pattern}",
                f"- **Gate Audit Verdict:** `{verdict_badge}`",
                f"- **Specification:** {rem.detailed_specification}",
                "- **Introduced Architectural Trade-offs:**",
                *(f"  - {to}" for to in rem.introduced_tradeoffs),
            ])

            if audit and audit.secondary_hazards_detected:
                md.append("- **Secondary Hazards Flagged:**")
                for hazard in audit.secondary_hazards_detected:
                    md.append(f"  - ⚠️ {hazard}")

            if audit and audit.actionable_critique:
                md.append(f"- **Auditor Critique Note:** *{audit.actionable_critique}*")

            md.append("")

        md.extend([
            "---",
            "",
            "## 4. Discovered Vulnerability & Findings Ledger",
            "",
            "| Finding ID | Lens | Severity | Affected Components | Root Cause Hypothesis |",
            "| :--- | :--- | :--- | :--- | :--- |",
        ])

        for f in sorted(self.findings, key=lambda x: x.severity.value):
            comps = ", ".join(f"`{c}`" for c in f.affected_component_ids)
            md.append(f"| `{f.finding_id}` | {f.lens.value} | **{f.severity.value}** | {comps} | {f.root_cause_hypothesis} |")

        md.append("")
        return "\n".join(md)

    def write_to_file(self, output_filepath: str | Path) -> Path:
        path = Path(output_filepath)
        path.parent.mkdir(parents=True, exist_ok=True)
        path.write_text(self.generate_markdown(), encoding="utf-8")
        return path


# =============================================================================
# 3. Multi-Agent Evaluation Engine (Gemini SDK)
# =============================================================================

MODEL_NAME = "gemini-2.5-flash"

class SystemEvaluatorSuite:
    def __init__(self):
        self.client = genai.Client()

    def run_decomposition(self, raw_system_spec: str) -> SystemDecompositionPayload:
        prompt = f"""
You are the System Ingestion & Decomposition Agent.
Deconstruct the following system specification into components, interfaces, invariants,
dependencies, and global system boundary constraints.

Raw Specification:
{raw_system_spec}
"""
        response = self.client.models.generate_content(
            model=MODEL_NAME,
            contents=prompt,
            config=types.GenerateContentConfig(
                response_mime_type="application/json",
                response_schema=SystemDecompositionPayload,
                temperature=0.1,
            ),
        )
        return SystemDecompositionPayload.model_validate_json(response.text)

    def run_evaluator_lens(
        self, decomposition: SystemDecompositionPayload, lens: EvaluatorLens
    ) -> EvaluationReportPayload:
        prompt = f"""
You are the {lens.value} Evaluator.
Rigorously audit the provided system decomposition strictly through your technical specialty.
Identify structural gaps, unhandled failure modes, or non-functional risks.
Back every finding with specific components, interfaces, or invariants as evidence.

System Model:
{decomposition.model_dump_json(indent=2)}
"""
        response = self.client.models.generate_content(
            model=MODEL_NAME,
            contents=prompt,
            config=types.GenerateContentConfig(
                response_mime_type="application/json",
                response_schema=EvaluationReportPayload,
                temperature=0.2,
            ),
        )
        return EvaluationReportPayload.model_validate_json(response.text)

    def run_synthesis(
        self,
        decomposition: SystemDecompositionPayload,
        all_findings: List[EvaluationFinding],
        iteration: int,
        critic_feedback: Optional[CriticGatePayload] = None,
    ) -> SynthesisProposalPayload:
        feedback_context = (
            f"\nPrior Critic Feedback to Address:\n{critic_feedback.model_dump_json(indent=2)}"
            if critic_feedback
            else "Initial draft round. No prior critic feedback."
        )

        prompt = f"""
You are the Lead Solution Architect.
Design high-reliability remediations addressing all reported evaluation findings.
Every remediation MUST trace back to specific finding IDs and respect the global constraints.
If resolving previous critique, address every flagged secondary hazard and feasibility issue directly.

Iteration: {iteration}
Global Constraints: {json.dumps(decomposition.global_constraints)}
{feedback_context}

Consolidated Findings:
{json.dumps([f.model_dump() for f in all_findings], indent=2)}
"""
        response = self.client.models.generate_content(
            model=MODEL_NAME,
            contents=prompt,
            config=types.GenerateContentConfig(
                response_mime_type="application/json",
                response_schema=SynthesisProposalPayload,
                temperature=0.2,
            ),
        )
        return SynthesisProposalPayload.model_validate_json(response.text)

    def run_critic(
        self,
        decomposition: SystemDecompositionPayload,
        proposal: SynthesisProposalPayload,
    ) -> CriticGatePayload:
        prompt = f"""
You are the Adversarial Verification Auditor and Quality Gate.
Rigorously critique the proposed remediations against original system constraints.
Check for:
1. Feasibility & Boundary Invariants: Does the fix assume non-existent primitives or break resource budgets?
2. Secondary Hazards: Does this fix introduce deadlocks, excessive latency, or data loss paths?
3. Trade-offs: Are operational impacts acceptable?

If any Critical or High priority item is deficient or introduces secondary hazards,
set decision to 'Revise_Synthesis' and provide actionable critique.
Only set decision to 'Approved' if all proposed remediations are sound and complete.

System Constraints:
{json.dumps(decomposition.global_constraints, indent=2)}

Proposed Remediations (Iteration {proposal.iteration_index}):
{proposal.model_dump_json(indent=2)}
"""
        response = self.client.models.generate_content(
            model=MODEL_NAME,
            contents=prompt,
            config=types.GenerateContentConfig(
                response_mime_type="application/json",
                response_schema=CriticGatePayload,
                temperature=0.1,
            ),
        )
        return CriticGatePayload.model_validate_json(response.text)

    def execute_and_export(
        self,
        raw_system_spec: str,
        output_report_path: str = "reports/system_evaluation_report.md",
        max_iterations: int = 3,
    ) -> Path:
        print("[1/4] Ingesting and decomposing system specification...")
        decomposition = self.run_decomposition(raw_system_spec)
        print(f"      Mapped: {decomposition.system_name} ({len(decomposition.components)} components, {len(decomposition.interfaces)} interfaces)")

        print("[2/4] Launching parallel evaluation panel...")
        lenses = [
            EvaluatorLens.COMPLIANCE,
            EvaluatorLens.HAZARDS,
            EvaluatorLens.PERFORMANCE,
        ]
        
        all_findings: List[EvaluationFinding] = []
        with ThreadPoolExecutor(max_workers=3) as executor:
            futures = [
                executor.submit(self.run_evaluator_lens, decomposition, lens)
                for lens in lenses
            ]
            for future in futures:
                report = future.result()
                all_findings.extend(report.findings)
                print(f"      {report.evaluator_lens.value}: {len(report.findings)} findings surfaced.")

        critic_payload: Optional[CriticGatePayload] = None
        synthesis_proposal: Optional[SynthesisProposalPayload] = None

        for iteration in range(1, max_iterations + 1):
            print(f"[3/4] Running Solution Architect (Iteration {iteration}/{max_iterations})...")
            synthesis_proposal = self.run_synthesis(
                decomposition=decomposition,
                all_findings=all_findings,
                iteration=iteration,
                critic_feedback=critic_payload,
            )

            print(f"[4/4] Critic Gate evaluating iteration {iteration} proposals...")
            critic_payload = self.run_critic(decomposition, synthesis_proposal)
            print(f"      Decision: {critic_payload.decision.value}")

            if critic_payload.decision == GateDecision.APPROVED:
                print("      Architectural sign-off achieved.")
                break
            elif iteration < max_iterations:
                print(f"      Rejection notes routed back to Architect: {critic_payload.overall_rationalization[:100]}...")
            else:
                print("      Reached maximum iteration limit. Proceeding with latest proposal.")

        print("\n[Export] Generating Executive Markdown Report...")
        exporter = ExecutiveReportExporter(
            decomposition=decomposition,
            findings=all_findings,
            proposal=synthesis_proposal,
            critic_audit=critic_payload,
        )
        saved_file = exporter.write_to_file(output_report_path)
        print(f"Report exported to: {saved_file.resolve()}")
        return saved_file


# =============================================================================
# 4. Main Entrypoint
# =============================================================================

if __name__ == "__main__":
    system_spec = """
    System: Edge Telemetry Ingestion Pipeline (v1.0.0)
    
    Architecture Overview:
    - Ingress Gateway (INGEST-01): Receives high-frequency UDP telemetry packets from remote sensors.
      Decodes payloads into JSON and forwards each event synchronously over HTTP POST to the Persistence Worker.
      Discards incoming packets when internal worker pool threads are busy.
    - Persistence Worker (STORAGE-01): Receives HTTP events and executes immediate single-row INSERT statements
      into an on-premises PostgreSQL instance.
    - Alert Dispatcher (ALERT-01): Runs a poll loop every 5 seconds executing a full-table query on the database
      to identify error states and broadcasts unauthenticated webhook alerts to external endpoints.

    Global Constraints & Requirements:
    - Zero data loss tolerance for telemetry packets under network spikes or downstream delays.
    - Maximum end-to-end event latency from arrival at INGEST-01 to DB commit must be under 200ms.
    - Host device is an edge gateway constrained to 8 CPU cores, 16GB RAM, and volatile uplink connectivity.
    """

    suite = SystemEvaluatorSuite()
    report_path = suite.execute_and_export(
        raw_system_spec=system_spec,
        output_report_path="reports/edge_telemetry_eval_report.md",
        max_iterations=2,
    )

What Happens During Execution

1. Structured Decomposition: gemini-2.5-flash structures the unstructured text into a formal topology of components, dependencies, interfaces, and boundary constraints.

2. Concurrent Multi-Lens Audit: Three parallel evaluation threads independently evaluate the system for standards compliance, safety/hazard failure modes, and performance bottlenecks using strict Pydantic schemas.

3. Closed-Loop Adversarial Gate: The Synthesis Architect builds remediation plans that trace back to specific findings. If the Critic rejects the plan (e.g., if a fix violates memory limits or introduces unhandled failure modes), the prompt feeds the exact critique back to the architect for revision.

4. Executive Markdown Report: Outputs a timestamped report featuring an executive sign-off status banner, component inventory, remediation matrix, trade-off analysis, and a vulnerability ledger.

From <https://gemini.google.com/app/89be71775a9410da>  Google Gemini (3.8 Flash)