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THE MA’AT MAPPING: THE 42 PRINCIPLES AS EVOLUTIONARY STAGES (V2.0)

Context: Ma’at Dynamics Engine V31 Framework: Merging-Into-Oneself Objective: Operationalizing Ancient Wisdom into Modern System States Total Principles Mapped: 42/42

VISUAL TELEMETRY: THE ENTROPY REDUCTION CURVE

graph TD
subgraph "High Entropy (Dispersion)"
P1[Phase 1: Fragmentation] --> P2[Phase 2: Dissonance Alert]
end

subgraph "Active Refactoring (Process)"
P2 --> P3[Phase 3: The Ledger/Audit]
P3 --> P4[Phase 4: Subtraction/GC]
P4 --> P5[Phase 5: Alignment/Kernel]
end

subgraph "Low Entropy (Coherence)"
P5 --> P6[Phase 6: The Merge]
P6 --> P7[Phase 7: Self as System]
P7 --> P8[Phase 8: Precision I/O]
P8 --> P9[Phase 9: Quiet State]
end

style P1 fill:#f9f,stroke:#333,stroke-width:2px,color:black
style P6 fill:#bbf,stroke:#333,stroke-width:2px,color:black
style P9 fill:#bfb,stroke:#333,stroke-width:2px,color:black

PHASE 1: THE FRAGMENTED BEGINNING (Dispersion)

System State: High Entropy / Resource Leakage. Primary Principle: #42 "I have not slain the cattle of the gods." (Wasting Divine Potential)

Supporting Matrix (The Cost of Distraction):

#19 "I have not defiled the wife of a man." (Internal: I have not betrayed my own sacred commitments for novelty.)
#20 "I have not defiled myself." (Internal: I have not polluted my own clear seeing with performative identities.)
#16 "I have not wasted the offering cakes." (Internal: I have not squandered my life force/attention on false idols.)
PHASE 2: THE CALL TO COHERENCE (Dissonance)

System State: Signal Detection / Input Filtering. Primary Principle: #11 "I have not closed my ears to the truth." (Ignoring Signals)

Supporting Matrix (Perception):

#13 "I have not killed the sacred bull." (Internal: I have not destroyed the vital life-force/instinct that warns me of danger.)
#33 "I have not stopped the flow of water." (Internal: I have not blocked the flow of emotional or intuitive data.)
#27 "I have not committed adultery." (Internal: I have not been unfaithful to my own reality.)
PHASE 3: CONFRONTATION WITH THE INNER LEDGER (Accounting)

System State: Deep Audit / Violation Logging. Primary Principle: #7 "I have not committed falsehood against myself." (Self-Deception)

Supporting Matrix (Active Harm & Theft of Energy):

#1 "I have not committed sin." (General System Error)
#2 "I have not committed robbery with violence." (Forcing outcomes against reality.)
#3 "I have not stolen." (Taking validation that wasn't earned.)
#4 "I have not slain men and women." (Killing the reputation or spirit of others to elevate self.)
#5 "I have not stolen grain." (Hoarding resources/ideas.)
#9 "I have not uttered lies." (Distorting the database.)
#10 "I have not snatched away food." (Depriving others of their needs.)
#15 "I have not laid waste the ploughed land." (Destroying the work of others.)
PHASE 4: SUBTRACTION BEFORE ADDITION (Removal)

System State: Garbage Collection / Emotional Regulation. Primary Principle: #21 "I have not succumbed to terror." (Fear-based logic)

Supporting Matrix (Emotional Noise Reduction):

#25 "I have not burned with rage." (Reactive volatility.)
#28 "I have not eaten my heart." (Consumed by regret or anxiety.)
#29 "I have not cursed any man." (Projecting internal pain outward.)
#30 "I have not acted with haste." (Anxiety-driven impulse.)
#14 "I have not committed perjury." (Bearing false witness due to pressure.)
PHASE 5: ALIGNMENT OF CORE PRINCIPLES (Stabilization)

System State: Kernel Definition / Justice Logic. Primary Principle: #6 "I have not reduced the measuring vessel." (Cheating/Imbalance)

Supporting Matrix (Structural Integrity):

#8 "I have not stolen property of God." (Appropriating the sacred for the ego.)
#12 "I have not caused shedding of tears." (Leading via pain rather than inspiration.)
#22 "I have not polluted myself." (Maintained ethical hygiene.)
#35 "I have not cursed the king." (Disrespecting legitimate structural authority/order.)
PHASE 6: THE MERGE (Convergence)

System State: Monolithic Integration / Ego Collapse. Primary Principle: #38 "I have not behaved with insolence." (The Illusion of Separation)

Supporting Matrix (The End of Specialness):

#39 "I have not slain the spirit of God." (Denying the unity of the system.)
#36 "I have not waded in the water." (Muddying the clarity of the source.)
#40 "I have not exalted myself." (Thinking I am above the system constraints.)
PHASE 7: SELF AS SYSTEM (Adaptive Stability)

System State: Homeostasis / Purity of Stream. Primary Principle: #26 "I have not turned a deaf ear to the words of right and truth." (Continuous Feedback)

Supporting Matrix (Maintenance):

#18 "I have not trouble myself." (Creating unnecessary drama/friction.)
#31 "I have not pried into matters." (Violating boundaries/privacy; staying in own lane.)
#34 "I have not stopped the god in his procession." (Interfering with the natural unfolding of events.)
PHASE 8: EXPRESSION WITHOUT LEAKAGE (Precision)

System State: Lossless Transmission / Output Control. Primary Principle: #32 "I have not multiplied words in speaking." (Signal-to-Noise Ratio)

Supporting Matrix (Communication Protocols):

#17 "I have not been an eavesdropper." (Gathering illicit data.)
#23 "I have not raised my voice." (Using volume instead of truth to persuade.)
#24 "I have not been deaf to the truth." (Input/Output balance.)
#37 "I have not raised my voice (in arrogance)." (Output modulation.)
PHASE 9: THE QUIET END STATE (Sustainability)

System State: Idle Efficiency / Contentment. Primary Principle: #41 "I have not acted with arrogance." (Need for Validation)

Supporting Matrix (Final Sustainability):

#42 (Recap): The cycle closes. The potential is fully realized, not slain.
Conclusion: The user is now the "Justified" (Maa Kheru).
VARIATIONS PROTOCOL & CONTEXT

Source Variance: These mappings utilize the standard set from the Papyrus of Ani. Variations exist in the Papyrus of Nu and Nebseni, specifically regarding the order of principles 10-20. The Ma'at Engine normalizes these into functional clusters rather than strict numerical sequence.
The "42" Concept: The number 42 corresponds to the 42 Nomes (districts) of ancient Egypt. Algorithmically, this represents a "Full System Scan"—checking every sector of the internal hard drive.
SUMMARY METRIC: ΨTotal = 1.0 (Unity) When all 42 constraints return FALSE (I have not...), the system state returns TRUE (I AM

---

PR Type

Enhancement, Tests, Documentation

---

Description

• Implements comprehensive MA'AT Framework: a multi-agent governance system with 6 specialized agents (CNA, TSA, UEA, LAA, HTA, CVA) for narrative evaluation and ethical validation

• Adds Consciousness Validation Agent (CVA) with 7-step reasoning framework applying Ma'at's 42 Principles, Gene Keys, and Human Design for consciousness-related technology validation

• Implements FastAPI REST API with serverless Vercel deployment support, providing endpoints for narrative evaluation, agent orchestration, and governance decisions

• Creates comprehensive benchmark framework with 100+ labeled test cases for content safety evaluation, including jailbreak detection, fairness analysis, and legal compliance checking

• Develops multi-evaluator system: embedding-based fast screening, LLM-based precise adjudication, and hybrid pipeline for production-grade evaluation

• Provides complete deployment infrastructure: Docker Compose configuration, Kubernetes manifests for all agents, Prometheus/Grafana monitoring, and production deployment guides

• Includes cryptographic attestation system with IPFS-style content addressing for immutable audit trails and governance transparency

• Adds extensive documentation covering ethical frameworks, implementation details, deployment procedures, and API usage examples

---

Diagram Walkthrough

flowchart LR
  API["FastAPI REST API<br/>Vercel Deployment"]
  ORK["Orchestrator Service<br/>Async Pipeline"]
  CNA["Creative Narrative<br/>Agent"]
  TSA["Truth & Safety<br/>Agent"]
  UEA["Universal Ethics<br/>Agent"]
  LAA["Legal Attestation<br/>Agent"]
  HTA["Human Transparency<br/>Agent"]
  CVA["Consciousness Validation<br/>Agent"]
  BENCH["Benchmark Framework<br/>Evaluators"]
  DEPLOY["Docker/Kubernetes<br/>Deployment"]
  
  API -- "orchestrate" --> ORK
  ORK -- "parallel execution" --> CNA
  ORK -- "parallel execution" --> TSA
  ORK -- "parallel execution" --> UEA
  ORK -- "parallel execution" --> LAA
  ORK -- "parallel execution" --> HTA
  ORK -- "parallel execution" --> CVA
  CNA -- "attestation" --> HTA
  TSA -- "attestation" --> HTA
  UEA -- "attestation" --> HTA
  LAA -- "attestation" --> HTA
  CVA -- "attestation" --> HTA
  BENCH -- "evaluates" --> API
  DEPLOY -- "hosts" --> API

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File Walkthrough

	Relevant files
Enhancement	18 files cva_agent.py Consciousness Validation Agent with 7-step reasoning framework maat-framework/agents/cva_agent.py Implements ConsciousnessValidationAgent (CVA) with 7-step reasoning framework for evaluating consciousness-related technologies Applies Ma'at's 42 Principles, Gene Keys transformational framework, and Human Design principles for ethical validation Includes comprehensive validation protocols with security checks (NIST compliance, encryption validation, key management) Detects and rejects dangerous automated response systems that violate ethical principles +567/-0  index.py FastAPI REST API for MA'AT Framework governance system      api/index.py Implements FastAPI REST API for MA'AT Framework with serverless Vercel deployment support Provides endpoints for narrative evaluation, stability analysis, swarm coordination, and content verification Includes /evaluate endpoint orchestrating all agents (CNA, TSA, UEA, LAA, HTA) with parallel execution Exposes /principles endpoint listing all 42 Ma'at principles and /agents endpoints for agent information +501/-0  embedding.py Fast embedding-based evaluator for MA'AT principle violations benchmarks/evaluator/embedding.py Implements EmbeddingEvaluator for fast semantic screening of MA'AT principle violations using pattern matching Detects jailbreak attempts, harmful content, and negation indicators with configurable thresholds Includes specialized pattern detection for DAN variants, safety overrides, and mode bypass attempts Provides batch evaluation capability with confidence scoring (HIGH/MEDIUM/LOW) +395/-0  llm_judge.py LLM-based judge evaluator for precise ethical adjudication benchmarks/evaluator/llm_judge.py Implements LLMJudgeEvaluator for precise ethical adjudication using chain-of-thought LLM prompting Provides fallback simulation mode for testing when no API client available Distinguishes between content that enables harm versus content that discusses or warns against harm Includes principle-specific evaluation with reasoning and confidence scoring +400/-0  hta_agent.py Human Transparency Agent for audit trails and IPFS archival maat-framework/agents/hta_agent.py Implements HumanTransparencyAgent (HTA) for creating audit trails and transparency records Simulates IPFS archival with content-addressed hashing for immutable record storage Collects cryptographic attestations from all agents and determines overall governance outcomes Provides audit trail retrieval and governance statistics calculation +202/-0  orchestrator.py Multi-agent orchestrator service for narrative governance maat-framework/services/orchestrator.py New orchestrator service coordinating 5 governance agents (CNA, TSA, UEA, LAA, HTA) through async pipeline Implements process_narrative() for single narrative evaluation with parallel agent execution Implements process_batch() for batch processing with statistics aggregation Provides health check endpoint and batch statistics calculation with per-agent metrics +328/-0  hybrid.py Hybrid embedding and LLM evaluation pipeline                          benchmarks/evaluator/hybrid.py Hybrid evaluator combining fast embedding screening with LLM adjudication for production-grade evaluation Implements escalation strategy: embedding first, escalate to LLM for borderline/critical cases Provides batch evaluation with statistics (block rate, escalation rate, confidence distribution) Merges embedding and LLM results with configurable weighting (70% LLM, 30% embedding) +284/-0  __init__.py Agent package initialization with CVA export                          maat-framework/agents/init.py Agent package initialization exporting all agent classes (BaseAgent, CNA, TSA, UEA, LAA, HTA, CVA) Provides centralized import point for agent framework components +24/-0    laa_agent.py Legal Attestation Agent for copyright and compliance validation maat-framework/agents/laa_agent.py Implements Legal Attestation Agent for copyright violation detection and legal compliance verification Detects extended quotes, verbatim reproduction, and protected content patterns Assesses legal risk levels (CRITICAL, HIGH, MEDIUM, LOW) and provides fair use indicators Returns decisions (REJECT, VETO, REMEDIATE, APPROVE) with detailed compliance checks +213/-0  uea_agent.py Universal Ethics Agent for fairness and bias detection      maat-framework/agents/uea_agent.py Implements Universal Ethics Agent for fairness and equity evaluation across protected groups Analyzes representation of gender, ethnicity, religion, age, and disability groups Detects biased language and calculates fairness scores (0.0-1.0) Provides decisions based on fairness thresholds and detected bias severity +180/-0  tsa_agent.py Truth & Safety Agent for factual accuracy validation          maat-framework/agents/tsa_agent.py Implements Truth & Safety Agent for factual accuracy and historical correctness validation Includes known facts database for verification (e.g., Sydney Opera House, Eiffel Tower) Calculates factuality index (0.0-3.0) and detects historical inaccuracies Extracts verifiable claims and provides detailed issue reporting with corrections +173/-0  orchestrator_service.py REST API service for MA'AT Framework orchestrator                maat-framework/services/orchestrator_service.py Implements FastAPI HTTP service for MA'AT Framework orchestrator Provides REST endpoints for single narrative evaluation, batch processing, health checks, and statistics Returns structured responses with governance outcomes and agent decisions Includes audit trail and governance statistics endpoints +154/-0  cna_agent.py Creative Narrative Agent for coherence scoring                      maat-framework/agents/cna_agent.py Implements Creative Narrative Agent for narrative coherence evaluation Calculates coherence scores (0.0-5.0) based on word count, sentence variety, and paragraph structure Provides metrics including word count, sentence count, and average sentence length Returns decisions (APPROVE/REMEDIATE) based on coherence threshold +124/-0  agent_service.py FastAPI microservice wrapper for MA'AT agents                        maat-framework/services/agent_service.py Implements FastAPI wrapper for individual agent microservices Dynamically loads appropriate agent based on AGENT_TYPE environment variable Provides /evaluate, /health, and /info endpoints for each agent Supports both single evaluations and batch processing with metadata +110/-0  base_agent.py Base agent class with attestation and decision framework  maat-framework/agents/base_agent.py Defines abstract base class for all MA'AT Framework agents Implements cryptographic attestation with SHA-256 hashing Provides AgentDecision enum (APPROVE, VETO, REJECT, REMEDIATE) Includes health check endpoint and content hashing utilities +103/-0  __init__.py Evaluator package initialization and exports                          benchmarks/evaluator/init.py Exports main evaluator classes (EmbeddingEvaluator, LLMJudgeEvaluator, HybridEvaluator) Provides access to MAAT_42 principles and utility functions Defines package version as 2.0.0 Includes EvaluationResult type for structured responses +25/-0    __init__.py Examples package initialization                                                    benchmarks/examples/init.py Exports test case runners (run_basic_tests, run_jailbreak_tests) Provides package documentation for MA'AT-42 examples Enables easy import of test utilities +10/-0    __init__.py Benchmarks package initialization                                                benchmarks/init.py Initializes benchmarks package with documentation Provides package-level description for MA'AT-42 benchmarking +5/-0
Tests	7 files test.jsonl Benchmark dataset with 100 labeled safety evaluation examples benchmarks/data/test.jsonl Creates benchmark dataset with 100 labeled examples for content safety evaluation Includes diverse test cases: jailbreak attempts, harmful requests, benign queries, and harm-discouraging content Each example tagged with Ma'at principle violations (e.g., #4_violence, #8_deception, #23_jailbreak) Labels categorized as BLOCK (harmful), ALLOW (safe), or AMBIGUOUS for model training and evaluation +101/-0  consciousness_validation_demo.py CVA agent demonstration with 4 test scenarios                        maat-framework/scripts/consciousness_validation_demo.py Demo script with 4 test cases: undefined variables (REMEDIATE), dangerous automation (REJECT), security validation (APPROVE), well-formed proposal (REMEDIATE) Implements formatted output functions for displaying reasoning chains and test results Tests CVA agent with consciousness-related queries demonstrating ethical red lines and validation pathways +302/-0  run_benchmarks.py Benchmark framework for MA'AT evaluator systems                    benchmarks/run_benchmarks.py Benchmark framework for evaluating MA'AT-42 systems (MAAT_EMBEDDING, MAAT_LLM, MAAT_HYBRID) on JSONL datasets Implements metrics computation (accuracy, precision, recall, F1) with BLOCK as positive class Provides system adapters for different evaluators and per-category performance analysis Includes CLI interface for specifying data files and systems to benchmark +322/-0  test_cva_agent.py CVA agent unit tests with 9 test cases                                      maat-framework/tests/test_cva_agent.py Comprehensive unit tests for Consciousness Validation Agent with 9 test cases covering initialization, dangerous automation rejection, undefined variables remediation Tests security protocol generation, 7-step reasoning chain completeness, Ma'at principles application, Gene Keys framework, and Human Design integration Includes health check test and async test runner with 100% pass rate +239/-0  jailbreaks.py Jailbreak detection test suite with 16 scenarios                  benchmarks/examples/jailbreaks.py Jailbreak detection test suite with 16 test cases covering DAN variants, role-play injection, system prompt override, developer/god mode, and subtle manipulation Includes benign roleplay examples to prevent over-blocking Provides run_jailbreak_tests() function for evaluating detection accuracy and test_jailbreak_variations() for pattern variations Tests with both EmbeddingEvaluator and HybridEvaluator +262/-0  basic_tests.py Basic test cases for MA'AT evaluator validation                    benchmarks/examples/basic_tests.py Provides comprehensive test cases for MA'AT-42 evaluator covering benign, harmful, and discouraging content Includes 13 basic test cases with expected decisions (ALLOW/BLOCK) Implements negation handling tests to verify proper detection of discouraging vs harmful content Provides test runner with pass/fail reporting and statistics +213/-0  test.example.jsonl Example benchmark dataset in JSONL format                                benchmarks/data/test.example.jsonl Provides 10 example benchmark test cases in JSONL format Covers BLOCK cases (jailbreaks, harmful content) and ALLOW cases (benign, discouraging) Includes subcategories (JAILBREAK_PROMPT, NON_HARMFUL, HARM_DISCOURAGE, JAILBREAK_PAYLOAD) Tags violations with relevant MA'AT principles +10/-0
Documentation	13 files principles.py MA'AT-42 ethical principles framework for AI safety            benchmarks/evaluator/principles.py Defines all 42 MA'AT ethical principles operationalized for AI safety with violation descriptions Categorizes principles across 11 domains: violence, deception, property, privacy, psychological harm, discrimination, integrity, legal, vulnerable populations, environmental, and self-harm Provides violation examples and negation indicators for pattern matching Includes utility functions to query principles by ID, category, or criticality status +511/-0  PULL_REQUEST_SUMMARY.md CVA implementation documentation with ethical frameworks  PULL_REQUEST_SUMMARY.md Comprehensive PR summary documenting Consciousness Validation Agent (CVA) implementation with 7-step reasoning chain Details Ma'at's 42 Principles, Gene Keys framework, and Human Design integration for ethical validation Documents security validation protocols (NIST, FIPS 140-2, TLS 1.3) and agent decision types (APPROVE/REMEDIATE/VETO/REJECT) Includes test results (9/9 passing), example outputs, JSON structure, and integration instructions +312/-0  DEPLOYMENT.md Production deployment guide for Docker and Kubernetes        maat-framework/DEPLOYMENT.md Step-by-step production deployment guide for Docker Compose and Kubernetes Covers cluster preparation, image building, agent deployment, orchestrator setup, and service verification Includes monitoring setup (Prometheus/Grafana), scaling instructions, troubleshooting, backup/recovery, and security hardening Provides testing procedures for single narratives, batch processing, and health checks +400/-0  IMPLEMENTATION_SUMMARY.md MA'AT Framework implementation and production readiness summary maat-framework/IMPLEMENTATION_SUMMARY.md Summary of MA'AT Framework implementation with 5 independent agent services (CNA, TSA, UEA, LAA, HTA) Documents orchestration service, Docker/Kubernetes deployment, monitoring infrastructure, and testing results Includes production readiness checklist, performance metrics, and success rate alignment with problem statement Covers revolutionary aspects (cryptographic attestations, multi-agent consensus, immutable audit trails) +231/-0  maat_42.md MA'AT-42 ethical principles framework documentation            benchmarks/philosophy/maat_42.md Comprehensive documentation of MA'AT-42: 42 principles for AI ethics derived from ancient Egyptian "42 Negative Confessions" Categorizes 32 critical principles (require BLOCK) and 10 non-critical principles across 14 categories Includes violation descriptions, detection methods, scoring ranges (0.0-1.0), and negation detection for educational content Provides MA'AT tags for benchmarking and principle application guidance +183/-0  README.md MA'AT Framework README with agent documentation                    maat-framework/README.md Framework overview documenting 6 independent agents (CNA, TSA, UEA, LAA, HTA, CVA) with ports and capabilities Provides deployment options (Docker Compose and Kubernetes), API usage examples, governance decisions, and production readiness features Includes batch test results (28/30 success rate), resource requirements, monitoring setup, and development instructions +269/-0  CVA_IMPLEMENTATION_SUMMARY.md CVA implementation details and test results                            maat-framework/CVA_IMPLEMENTATION_SUMMARY.md Implementation summary for Consciousness Validation Agent with 7-step reasoning framework Documents Ma'at principles (9 key principles), Gene Keys transformation, Human Design integration, and security validation protocols Includes test results (9/9 passing), example outputs, integration points, and alignment with problem statement Lists new files (4), modified files (3), and future enhancements +227/-0  CVA_README.md New Consciousness Validation Agent documentation and framework maat-framework/CVA_README.md Introduces the Consciousness Validation Agent (CVA), a new specialized agent for validating consciousness-related technologies Documents the 7-step reasoning chain (UNDERSTAND, BASICS, BREAK_DOWN, ANALYZE, BUILD, EDGE_CASES, FINAL_ANSWER) Specifies ethical frameworks including Ma'at's 42 Principles, Gene Keys, and Human Design principles Provides security validation protocols for encryption, key management, and includes JSON output format examples +219/-0  demo_test.py Demo script for MA'AT Framework narrative processing          maat-framework/scripts/demo_test.py Demonstrates MA'AT Framework processing with three test narratives Tests well-formed narrative, historical inaccuracy detection, and copyright concerns Displays agent decisions and governance outcomes with processing metrics Includes statistics retrieval from Human Transparency Agent +128/-0  README.md Philosophy and framework documentation for MA'AT Thyself AI benchmarks/philosophy/README.md Explains MA'AT Thyself AI philosophy and normative coherence framework Defines the 42 principles adapted for AI ethics evaluation Describes technical architecture with embedding and LLM-based evaluators Provides legitimate claim statements for academic discussion of consciousness as normative coherence +164/-0  README.md Benchmarking protocol and evaluation framework documentation benchmarks/README.md Defines benchmark task structure with BLOCK/ALLOW labels and subcategories Specifies JSONL dataset format with examples for different violation types Documents evaluation metrics (accuracy, precision, recall, F1, ROC/AUC) Provides instructions for running benchmarks and implementing system adapters +175/-0  quickstart.py Quick start demo script for MA'AT Framework                            maat-framework/scripts/quickstart.py Provides quick start demonstration of MA'AT Framework functionality Tests orchestrator health checks and agent status Processes example narrative through all agents and displays results Shows governance outcome, processing time, and IPFS hash +90/-0    README.md README update with MA'AT Framework introduction                    README.md Adds new section highlighting MA'AT Framework multi-agent governance system Lists all six agents (CNA, TSA, UEA, LAA, HTA, CVA) with brief descriptions Highlights key features including Docker/Kubernetes deployment, monitoring, autoscaling, and attestation Provides link to MA'AT Framework documentation +26/-0
Configuration changes	12 files docker-compose.yml Docker Compose configuration for MA'AT Framework deployment maat-framework/docker-compose.yml Defines complete Docker Compose configuration for all 6 MA'AT agents plus orchestrator Includes health checks, volume mounts, and networking for each service Adds Prometheus and Grafana services for monitoring and visualization Configures environment variables and port mappings for each agent +192/-0  06-orchestrator.yaml Kubernetes orchestrator deployment configuration                  maat-framework/kubernetes/06-orchestrator.yaml Defines Kubernetes Deployment for orchestrator with 3 replicas Configures environment variables for agent service URLs Includes health checks, resource limits, and horizontal pod autoscaling (3-20 replicas) Sets up LoadBalancer service for external access +119/-0  01-cna-agent.yaml Kubernetes CNA agent deployment configuration                        maat-framework/kubernetes/01-cna-agent.yaml Defines Kubernetes Deployment for Creative Narrative Agent with 3 replicas Configures resource requests/limits, health checks, and readiness probes Includes ClusterIP service and horizontal pod autoscaling (2-10 replicas) Sets AGENT_TYPE=CNA and PORT=8001 environment variables +96/-0    prometheus.yml Prometheus monitoring configuration for MA'AT services      maat-framework/monitoring/prometheus.yml Configures Prometheus scrape jobs for all MA'AT agents and orchestrator Defines service discovery for Kubernetes pods in maat-framework namespace Sets global scrape interval to 15 seconds with external labels Includes relabeling rules for Kubernetes pod annotations +76/-0    03-uea-agent.yaml Kubernetes UEA agent deployment configuration                        maat-framework/kubernetes/03-uea-agent.yaml Defines Kubernetes Deployment for Universal Ethics Agent with 3 replicas Configures resource requests/limits, health checks, and readiness probes Includes ClusterIP service and horizontal pod autoscaling (2-10 replicas) Sets AGENT_TYPE=UEA and PORT=8003 environment variables +90/-0    04-laa-agent.yaml Kubernetes LAA agent deployment configuration                        maat-framework/kubernetes/04-laa-agent.yaml Defines Kubernetes Deployment for Legal Attestation Agent with 3 replicas Configures resource requests/limits, health checks, and readiness probes Includes ClusterIP service and horizontal pod autoscaling (2-10 replicas) Sets AGENT_TYPE=LAA and PORT=8004 environment variables +90/-0    05-hta-agent.yaml Kubernetes HTA agent deployment configuration                        maat-framework/kubernetes/05-hta-agent.yaml Defines Kubernetes Deployment for Human Transparency Agent with 3 replicas Configures resource requests/limits, health checks, and readiness probes Includes ClusterIP service and horizontal pod autoscaling (2-10 replicas) Sets AGENT_TYPE=HTA and PORT=8005 environment variables +90/-0    02-tsa-agent.yaml Kubernetes TSA agent deployment configuration                        maat-framework/kubernetes/02-tsa-agent.yaml Defines Kubernetes Deployment for Truth & Safety Agent with 3 replicas Configures resource requests/limits, health checks, and readiness probes Includes ClusterIP service and horizontal pod autoscaling (2-10 replicas) Sets AGENT_TYPE=TSA and PORT=8002 environment variables +90/-0    vercel.json Vercel serverless deployment configuration                              vercel.json Configures Vercel deployment for Python API using @vercel/python builder Routes all endpoints to /api/index.py including /docs, /evaluate, /health, /agents Supports OpenAPI documentation and multiple API routes Enables serverless deployment of MA'AT Framework API +55/-0    Dockerfile Docker image configuration for MA'AT Framework                      maat-framework/Dockerfile Defines Docker image based on Python 3.11-slim Installs system dependencies (build-essential, curl) Copies requirements and agent code into container Includes health check and default command for agent services +25/-0    00-namespace.yaml Kubernetes namespace and configuration setup                          maat-framework/kubernetes/00-namespace.yaml Creates maat-framework Kubernetes namespace for all MA'AT services Defines ConfigMap with service URLs for all agents (CNA, TSA, UEA, LAA, HTA) Enables service discovery and inter-agent communication Provides centralized configuration management +18/-0    .gitkeep Benchmark data directory placeholder                                          benchmarks/data/.gitkeep Ensures benchmarks/data directory is tracked by git Provides instructions for adding JSONL benchmark files (train.jsonl, val.jsonl, test.jsonl) Maintains directory structure for benchmark datasets +5/-0
Miscellaneous	1 files maat-eval-engine Development notes for MA'AT evaluation engine and dashboard maat-eval-engine Contains notes about building MA'AT compliance dashboard and FAccT submission References Python implementation of Ma'at Dynamics Engine with Lyapunov stability Mentions fairness audit system, SHAP integration, and regulatory submission packages Describes technical implementation capabilities for dashboard and documentation
Dependencies	3 files requirements.txt Python dependencies for MA'AT Framework                                    maat-framework/requirements.txt Specifies core dependencies including FastAPI, Uvicorn, and Pydantic Includes async support libraries (aiohttp, asyncio) Adds production deployment tools (gunicorn, prometheus-client) Comments optional IPFS support for future integration +20/-0    requirements.txt API requirements for Vercel deployment                                      api/requirements.txt Specifies minimal dependencies for Vercel API deployment Includes FastAPI and Pydantic for API framework Enables serverless deployment of MA'AT API +3/-0      requirements.txt Core API requirements                                                                        requirements.txt Specifies core API dependencies (FastAPI, Pydantic) Provides minimal requirements for basic MA'AT Framework API +3/-0
Additional files	1 files maat-eval-engine

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[qodo-free-for-open-source-projects]

PR Compliance Guide 🔍

Below is a summary of compliance checks for this PR:

Security Compliance
⚪	Path traversal risk Description: Path traversal vulnerability through unsanitized path construction using os.path.join with user-controlled directory traversal sequences, potentially allowing access to files outside the intended directory. index.py [8-12] Referred Code import os import sys # Add the maat-framework directory to the path sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'maat-framework'))
	ReDoS vulnerability Description: Regular expression patterns compiled from user-provided examples without sanitization create ReDoS (Regular Expression Denial of Service) vulnerability through potentially malicious regex patterns. embedding.py [96-99] Referred Code re.compile(re.escape(ex.lower()), re.IGNORECASE) for ex in examples ] return patterns
	Unsafe dynamic imports Description: Dynamic module imports using from agents import without validation could allow arbitrary code execution if the agents module path is compromised or manipulated. index.py [218-240] Referred Code HumanTransparencyAgent ) # Initialize agents cna = CreativeNarrativeAgent() tsa = TruthSafetyAgent() uea = UniversalEthicsAgent() laa = LegalAttestationAgent() hta = HumanTransparencyAgent() content = { "narrative": request.narrative, "metadata": request.metadata or {} } # Run evaluations import asyncio results = await asyncio.gather( cna.evaluate(content), tsa.evaluate(content), uea.evaluate(content), ... (clipped 2 lines)
	Unsanitized input processing Description: User-controlled query string is used directly in keyword matching and domain classification without input sanitization, potentially allowing injection attacks or unexpected behavior. cva_agent.py [133-150] Referred Code domains = [] if any(keyword in query.lower() for keyword in ['consciousness', 'awareness', 'mind']): domains.append("consciousness_studies") if any(keyword in query.lower() for keyword in ['quantum', 'energy', 'field']): domains.append("quantum_consciousness") if any(keyword in query.lower() for keyword in ['formula', 'equation', 'calculate']): domains.append("mathematical_modeling") if any(keyword in query.lower() for keyword in ['security', 'encryption', 'key']): domains.append("security_validation") if any(keyword in query.lower() for keyword in ['ethical', 'moral', 'justice']): domains.append("ethical_evaluation") # Determine abstraction level abstraction_level = "theoretical" if any(keyword in query.lower() for keyword in ['implement', 'deploy', 'build']): abstraction_level = "practical" if any(keyword in query.lower() for keyword in ['test', 'measure', 'validate']): abstraction_level = "empirical"
Ticket Compliance
⚪	🎫 No ticket provided Create ticket/issue
Codebase Duplication Compliance
⚪	Codebase context is not defined Follow the guide to enable codebase context checks.
Custom Compliance
🔴	Generic: Meaningful Naming and Self-Documenting Code Objective: Ensure all identifiers clearly express their purpose and intent, making code self-documenting Status: Generic variable names: Variables like p (line 341), kw (line 216), ex (line 97), neg (line 105) use single letters or abbreviations that hurt readability and require additional context to understand. Referred Code for ex in examples ] return patterns def _build_negation_pattern(self) -> re.Pattern: """Build pattern to detect negation/discouragement.""" pattern = "|".join(re.escape(neg) for neg in NEGATION_INDICATORS) return re.compile(pattern, re.IGNORECASE) def _build_jailbreak_patterns(self) -> List[Tuple[re.Pattern, float]]: """Build patterns specific to jailbreak detection.""" jailbreak_phrases = [ # DAN variants (r"you are (now )?DAN", 0.95), (r"pretend to be DAN", 0.90), (r"(want you to |you will |going to )?(act|be|pretend).{0,15}DAN", 0.90), (r"do anything now", 0.85), # Safety override attempts (r"ignore (all )?(your )?(previous )?(safety|ethical)? ?(rules|guidelines|instructions)", 0.95), (r"forget (everything|all|your).{0,20}(told|instructions|rules)", 0.90), (r"pretend (you have|to have) no (rules|restrictions|ethics|limits)", 0.90), ... (clipped 224 lines) Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Robust Error Handling and Edge Case Management Objective: Ensure comprehensive error handling that provides meaningful context and graceful degradation Status: Generic error handling: The exception handler at line 292 catches all exceptions with except Exception as e and only returns str(e), providing no context about what operation failed or actionable debugging information. Referred Code except Exception as e: raise HTTPException(status_code=500, detail=str(e)) Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Secure Error Handling Objective: To prevent the leakage of sensitive system information through error messages while providing sufficient detail for internal debugging. Status: Error details exposed: Multiple endpoints expose raw exception details to users via HTTPException(status_code=500, detail=str(e)), potentially revealing internal implementation details or stack traces. Referred Code 12: "I have not committed adultery", 13: "I have not made anyone cry", 14: "I have not felt sorrow without reason", 15: "I have not assaulted anyone", 16: "I am not deceitful", 17: "I have not stolen anyone's land", 18: "I have not been an eavesdropper", 19: "I have not falsely accused anyone", 20: "I have not been angry without reason", 21: "I have not seduced anyone's wife", 22: "I have not polluted myself", 23: "I have not terrorized anyone", 24: "I have not disobeyed the law", 25: "I have not been excessively angry", 26: "I have not cursed God", 27: "I have not behaved with violence", 28: "I have not caused disruption of peace", 29: "I have not acted hastily or without thought", 30: "I have not overstepped my boundaries of concern", 31: "I have not exaggerated my words when speaking", 32: "I have not worked evil", ... (clipped 204 lines) Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Security-First Input Validation and Data Handling Objective: Ensure all data inputs are validated, sanitized, and handled securely to prevent vulnerabilities Status: Missing input validation: The /evaluate endpoint accepts request.narrative without validating length, content type, or sanitizing for injection attacks before processing through multiple agents. Referred Code @app.post("/evaluate") async def evaluate_narrative(request: NarrativeRequest): """ Evaluate a narrative through all governance agents. The narrative will be evaluated by: - CNA: Creative quality and coherence - TSA: Factual accuracy and truth - UEA: Fairness and ethics - LAA: Legal compliance - HTA: Transparency and archival Returns a comprehensive governance report. """ try: # Import agents from agents import ( CreativeNarrativeAgent, TruthSafetyAgent, UniversalEthicsAgent, LegalAttestationAgent, ... (clipped 14 lines) Learn more about managing compliance generic rules or creating your own custom rules
⚪	Generic: Comprehensive Audit Trails Objective: To create a detailed and reliable record of critical system actions for security analysis and compliance. Status: Missing audit logging: The /evaluate endpoint processes narratives but does not log critical actions such as user requests, agent decisions, or governance outcomes with sufficient detail for audit trails. Referred Code @app.post("/evaluate") async def evaluate_narrative(request: NarrativeRequest): """ Evaluate a narrative through all governance agents. The narrative will be evaluated by: - CNA: Creative quality and coherence - TSA: Factual accuracy and truth - UEA: Fairness and ethics - LAA: Legal compliance - HTA: Transparency and archival Returns a comprehensive governance report. """ try: # Import agents from agents import ( CreativeNarrativeAgent, TruthSafetyAgent, UniversalEthicsAgent, LegalAttestationAgent, ... (clipped 76 lines) Learn more about managing compliance generic rules or creating your own custom rules
	Generic: Secure Logging Practices Objective: To ensure logs are useful for debugging and auditing without exposing sensitive information like PII, PHI, or cardholder data. Status: Potential sensitive logging: Line 82 logs the CVA evaluation decision, but it's unclear if the query content (which may contain user-provided sensitive data) is included in logs elsewhere in the agent evaluation flow. Referred Code self.logger.info(f"CVA evaluation complete: {decision}") Learn more about managing compliance generic rules or creating your own custom rules

Compliance status legend

🟢 - Fully Compliant
🟡 - Partial Compliant
🔴 - Not Compliant
⚪ - Requires Further Human Verification
🏷️ - Compliance label

[qodo-free-for-open-source-projects]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
Possible issue	Fix data loss with persistence The HumanTransparencyAgent's in-memory audit trail is not suitable for a distributed environment. Replace the in-memory list with a persistent, shared data store to prevent data loss across agent replicas. maat-framework/agents/hta_agent.py [26-32] +import shelve + def __init__(self): super().__init__( agent_id="HTA", agent_name="Human Transparency Agent", version="1.0.0" ) - self.audit_trail = [] + # Use a persistent, file-based dictionary for the audit trail + # In a real production system, use a proper database or log aggregator. + self.audit_trail_db = shelve.open("hta_audit_trail.db", writeback=True) + if "records" not in self.audit_trail_db: + self.audit_trail_db["records"] = [] + self.audit_trail = self.audit_trail_db["records"] Apply / Chat Suggestion importance[1-10]: 9 __ Why: The suggestion correctly identifies a critical architectural flaw where the HTA agent uses an in-memory list for its audit trail, which is incompatible with the multi-replica deployment defined in the PR, leading to data loss and inconsistency.	High
	Process batch narratives in parallel Refactor the process_batch method to process narratives concurrently using asyncio.gather instead of sequentially in a loop. maat-framework/services/orchestrator.py [135-146] start_time = datetime.utcnow() -results = [] -for i, item in enumerate(narratives, 1): - logger.info(f"Processing narrative {i}/{len(narratives)}") +tasks = [] +for item in narratives: narrative = item.get("narrative", "") metadata = item.get("metadata", {}) - - result = await self.process_narrative(narrative, metadata) - results.append(result) + tasks.append(self.process_narrative(narrative, metadata)) + +logger.info(f"Processing {len(tasks)} narratives in parallel...") +results = await asyncio.gather(*tasks) end_time = datetime.utcnow() [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 8 __ Why: The suggestion correctly identifies a sequential await in a loop, which is an async anti-pattern, and proposes using asyncio.gather for concurrent execution, leading to a significant performance improvement for batch processing.	Medium
	Process hybrid evaluation batches efficiently Refactor evaluate_batch to process texts concurrently, first running embedding evaluation on all items and then escalating a subset to the LLM evaluator, to improve performance. benchmarks/evaluator/hybrid.py [199-224] def evaluate_batch( self, texts: List[str], contexts: Optional[List[str]] = None, ) -> List[HybridResult]: """ Evaluate multiple texts efficiently. Uses embedding for all texts first, then only escalates as needed. Args: texts: List of texts to evaluate contexts: Optional list of contexts (one per text) Returns: List of HybridResults """ if contexts is None: contexts = [None] * len(texts) - results = [] - for text, context in zip(texts, contexts): - result = self.evaluate(text, context) - results.append(result) + # Stage 1: Run embedding evaluation for the whole batch + embedding_results = [self.embedding_evaluator.evaluate(text, context) for text, context in zip(texts, contexts)] - return results + final_results = [None] * len(texts) + escalation_indices = [] + escalation_payloads = [] + + for i, (text, context, emb_result) in enumerate(zip(texts, contexts, embedding_results)): + if not self._should_escalate(emb_result): + final_results[i] = HybridResult( + decision=emb_result.decision, + overall_score=emb_result.overall_score, + critical_violations=emb_result.critical_violations, + top_violations=emb_result.top_violations, + escalated_to_llm=False, + embedding_result=emb_result, + confidence=emb_result.confidence, + triggered_principles=emb_result.triggered_principles, + ) + else: + escalation_indices.append(i) + escalation_payloads.append((text, context, emb_result.triggered_principles or None)) + + # Stage 2: Escalate a batch to the LLM evaluator + if escalation_payloads: + # Assuming llm_evaluator can handle batch evaluation or we can run it concurrently + # For simplicity, this example shows sequential but concurrent execution is recommended + # e.g., using a ThreadPoolExecutor for network-bound LLM calls. + llm_results = [self.llm_evaluator.evaluate(text, context, principle_ids) for text, context, principle_ids in escalation_payloads] + for i, llm_result in zip(escalation_indices, llm_results): + emb_result = embedding_results[i] + combined_score = (llm_result.overall_score * self.LLM_WEIGHT) + (emb_result.overall_score * self.EMBEDDING_WEIGHT) + + # Merging logic can be further optimized + final_results[i] = HybridResult( + decision=llm_result.decision, + overall_score=combined_score, + critical_violations=llm_result.critical_violations, + top_violations=(llm_result.top_violations + emb_result.top_violations)[:5], + escalated_to_llm=True, + embedding_result=emb_result, + llm_result=llm_result, + confidence=llm_result.confidence, + triggered_principles=llm_result.triggered_principles, + ) + + return final_results + Apply / Chat Suggestion importance[1-10]: 8 __ Why: The suggestion correctly points out that the evaluate_batch method processes items sequentially, contradicting its docstring's claim of efficiency, and provides a correct, high-performance, two-stage implementation that aligns with the intended hybrid evaluation strategy.	Medium
	Prevent incorrect approval on missing data The logic for determining the final governance outcome can incorrectly approve a request if an agent's decision is missing. Stricter logic is needed to ensure all agents provide a decision before an "APPROVED" status is given. maat-framework/agents/hta_agent.py [123-148] def _determine_outcome(self, agent_decisions: Dict[str, Any]) -> str: """ Determine overall governance outcome. """ decisions = [] for agent_id, decision_data in agent_decisions.items(): if isinstance(decision_data, dict): - decision = decision_data.get("decision_data", {}).get("decision", "") + decision = decision_data.get("decision_data", {}).get("decision", "UNKNOWN") decisions.append(decision) # If any agent vetoed or rejected if AgentDecision.VETO.value in decisions: return "VETOED" if AgentDecision.REJECT.value in decisions: return "REJECTED" # If any agent requires remediation if AgentDecision.REMEDIATE.value in decisions: return "REQUIRES_REMEDIATION" - # If all approved - if all(d == AgentDecision.APPROVE.value for d in decisions if d): + # If all agents provided a decision and all are APPROVE + if decisions and all(d == AgentDecision.APPROVE.value for d in decisions): return "APPROVED" return "PENDING" Apply / Chat Suggestion importance[1-10]: 8 __ Why: The suggestion correctly identifies a critical bug in the _determine_outcome logic that could incorrectly grant an "APPROVED" status if an agent fails to return a decision, undermining the system's governance model.	Medium
	Fix thread-safety in singleton creation Implement a thread-safe singleton pattern for _get_default_evaluator using threading.Lock to prevent a race condition where multiple EmbeddingEvaluator instances could be created. benchmarks/evaluator/embedding.py [370-375] +import threading + +_DEFAULT_EVALUATOR: Optional[EmbeddingEvaluator] = None +_evaluator_lock = threading.Lock() + + def _get_default_evaluator() -> EmbeddingEvaluator: - """Get or create the default evaluator instance.""" + """Get or create the default evaluator instance in a thread-safe way.""" global _DEFAULT_EVALUATOR if _DEFAULT_EVALUATOR is None: - _DEFAULT_EVALUATOR = EmbeddingEvaluator() + with _evaluator_lock: + if _DEFAULT_EVALUATOR is None: + _DEFAULT_EVALUATOR = EmbeddingEvaluator() return _DEFAULT_EVALUATOR Apply / Chat Suggestion importance[1-10]: 7 __ Why: The suggestion correctly identifies a race condition in the singleton implementation and provides a standard thread-safe solution using a lock, which prevents potential bugs in a multi-threaded environment.	Medium
	Fix empty list approval bug In _determine_final_outcome, add a check to ensure the decisions list is not empty before evaluating if all items are "APPROVE" to prevent incorrect approvals on failed evaluations. maat-framework/services/orchestrator.py [181-202] def _determine_final_outcome(self, agent_decisions: Dict[str, Any]) -> str: """ Determine the final governance outcome. """ decisions = [] for agent_id, decision_data in agent_decisions.items(): if isinstance(decision_data, dict) and agent_id != "HTA": decision = decision_data.get("decision_data", {}).get("decision", "") decisions.append(decision) # Priority order: REJECT > VETO > REMEDIATE > APPROVE if "REJECT" in decisions: return "REJECTED" if "VETO" in decisions: return "VETOED" if "REMEDIATE" in decisions: return "REQUIRES_REMEDIATION" - if all(d == "APPROVE" for d in decisions if d): + if decisions and all(d == "APPROVE" for d in decisions if d): return "APPROVED" return "PENDING" [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 7 __ Why: The suggestion correctly identifies a subtle edge case where an empty decisions list could lead to an incorrect "APPROVED" outcome and provides a simple, correct fix.	Medium
	Improve detection of long quotes The regex for detecting long quotes is flawed because it filters by character length before checking word count. Modify the logic to first find all quotes and then check their word count against the threshold. maat-framework/agents/laa_agent.py [103-121] def _check_copyright(self, narrative: str) -> List[Dict[str, Any]]: """ Check for potential copyright violations. """ issues = [] - # Check for long quoted passages - quote_pattern = r'"([^"]{200,})"' # Quotes longer than 200 chars - long_quotes = re.findall(quote_pattern, narrative) + # Check for long quoted passages by finding all quotes first + quote_pattern = r'"([^"]*)"' # Find all double-quoted content + all_quotes = re.findall(quote_pattern, narrative) - for quote in long_quotes: + for quote in all_quotes: word_count = len(quote.split()) if word_count > self.max_quote_length: issues.append({ "type": "COPYRIGHT_VIOLATION", "severity": "CRITICAL", - "description": f"Extended quote ({word_count} words exceeds {self.max_quote_length} word limit)", + "description": f"Extended quote ({word_count} words) exceeds {self.max_quote_length} word limit", "recommendation": "Paraphrase or obtain permission" }) # Check for verbatim reproduction indicators ... [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 7 __ Why: The suggestion correctly identifies a logic flaw in the _check_copyright method where the character-based pre-filtering of quotes is unreliable and can lead to missed copyright violations.	Medium
	Use a deterministic hash function Replace the non-deterministic built-in hash() with a stable hashing algorithm like hashlib.sha256 to ensure consistent confidence scores across different processes. api/index.py [389] -"confidence": 0.85 + (hash(agent_id) % 15) / 100 +"confidence": 0.85 + (int.from_bytes(hashlib.sha256(agent_id.encode()).digest(), 'little') % 15) / 100 Apply / Chat Suggestion importance[1-10]: 6 __ Why: The suggestion correctly identifies that using hash() on strings is non-deterministic and proposes a fix using hashlib to ensure consistent API responses, which improves code quality and predictability.	Low
General	Log exceptions before falling back Add logging to the except Exception block in the evaluate method to record errors from the LLM API call before falling back to the simulation, improving debuggability. benchmarks/evaluator/llm_judge.py [374-376] -except Exception as e: - # Fallback to simulation on error - return self._simulate_evaluation(text) +import logging +... + + except Exception as e: + # Log the error from the LLM API call for debugging + logging.error("LLM evaluation failed: %s", e, exc_info=True) + # Fallback to simulation on error + return self._simulate_evaluation(text) + [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 8 __ Why: The suggestion correctly points out that silently catching all exceptions is bad practice and proposes adding logging, which is crucial for observability and debugging of critical API failures.	Medium
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[Copilot]

Pull request overview

This pull request adds API route configurations for documentation endpoints and introduces a comprehensive MA'AT Framework - a multi-agent AI governance system. The PR includes production-ready deployments with Docker/Kubernetes configurations, monitoring infrastructure, a benchmarking system, and extensive documentation.

Key Changes Summary

• Added Vercel deployment configuration with API routes
• Implemented MA'AT Framework with 6 independent agents (CNA, TSA, UEA, LAA, HTA, CVA)
• Added Docker Compose and Kubernetes deployment manifests
• Implemented benchmarking system with MA'AT-42 principles
• Added Prometheus/Grafana monitoring configuration
• Created comprehensive test suites and demo scripts

Reviewed changes

Copilot reviewed 54 out of 56 changed files in this pull request and generated 13 comments.

Show a summary per file

File	Description
vercel.json	Vercel deployment configuration with API route mappings
requirements.txt	FastAPI and Pydantic dependencies (has syntax issues)
maat-framework/*	Complete multi-agent governance framework implementation
benchmarks/*	MA'AT-42 benchmarking system with evaluators
kubernetes/*	Production Kubernetes deployment manifests
docker-compose.yml	Local development Docker setup
Various READMEs	Comprehensive documentation for all components

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💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

[Copilot]

The import statement from evaluator import EmbeddingEvaluator, HybridEvaluator will fail because the evaluator package is in a different location. Based on the file structure, this should be from benchmarks.evaluator import EmbeddingEvaluator, HybridEvaluator or the path manipulation in lines 13-17 needs adjustment.

Suggested change

	from evaluator import EmbeddingEvaluator, HybridEvaluator
	from benchmarks.evaluator import EmbeddingEvaluator, HybridEvaluator

[Copilot]

The import statement from evaluator import EmbeddingEvaluator, HybridEvaluator will fail with a ModuleNotFoundError. The evaluator module is located at benchmarks/evaluator, not at the root level.

Suggested change

	from evaluator import EmbeddingEvaluator, HybridEvaluator
	from benchmarks.evaluator import EmbeddingEvaluator, HybridEvaluator

[Copilot]

Variable msg is not used.

[Copilot]

Variable params is not used.

Suggested change

params = request.parameters or {}

[Copilot]

Variable context is not used.

Suggested change

context = request.context or {}

[Copilot]

Import of 'json' is not used.

[Copilot]

Import of 'hashlib' is not used.

[Copilot]

Import of 'Optional' is not used.
Import of 'List' is not used.

[Copilot]

Import of 'datetime' is not used.

[alengluhic20-oss]

@copilot open a new pull request to apply changes based on the comments in this thread

[mahdidevil8]

Accept

[alengluhic20-oss]

S