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Autonomous Budget Audit Agent: Text-to-SQL & GraphRAG

· 10 min read
Jaybee
Jaybee
AI & Robotics Engineer

Autonomous Public Budget Audit Agent

📌 Key Takeaways (TL;DR)

  • The Goal: Build an autonomous AI agent to audit $500B annual public expenditures, tracking government procurement contracts (KONEPS), fiscal databases (dBrain+), and messy Korean HWPX budget tables to detect price gouging, split contracts, and paper-company cartels.
  • Anticipated Challenges & Prevention: Prevented LLM numerical hallucinations by delegating calculations to a Deterministic Text-to-SQL Engine, while isolating relationship and cartel tracking to a Heterogeneous Knowledge Graph (GraphRAG) in a dual-brain architecture.
  • Actual Bugs Encountered & Solved: (1) Solved company name and street address fragmentation breaking cartel detection via a robust EntityResolver, (2) Caught temporal split contracts slipping past date filters via a 30-day sliding window algorithm, (3) Overcame auditor distrust of AI black-boxes by building an XAI Dossier Generator binding findings 1:1 with statutory clauses and 10-year official audit precedents.
  • Verification: 11 out of 11 regression test harness suites passed, successfully exposing a 38.5M KRW shared-address private procurement cartel and a 70% indirect overhead leakage (DEI score 30%).

Introduction: Why "Vibe-Coding" Fails in Enterprise Fiscal Audits​

With the explosion of coding LLMs and agentic assistants, many developers attempt to build enterprise systems by chaining together loose natural language prompts—a practice now colloquially termed Vibe-Coding. While this casual approach works well for personal scrapers or throwaway prototypes, it collapses completely when deployed against $500B public budget infrastructures and mission-critical government procurement systems.

In enterprise public audits, if an AI agent hallucinates a single dollar in an itemized invoice, or loosely guesses that "Company A and Company B seem to be colluding" without rigorous legal proof, the entire audit finding is rejected by judicial courts and government inspection boards.

Adhering to the principles of Spec-Driven Development (SDD) highlighted in Stanford CS146S, this deep dive documents the production journey of building our audit system: (1) How we engineered preventive architectures for anticipated technical hurdles, and (2) How we debugged and resolved critical, unexpected errors encountered during real-world data integration.

High-Level System Architecture​

Our autonomous budget audit system operates across three distinct, decoupled stages: Data Normalization & Ingestion, Dual-Brain Reasoning, and Explainable AI (XAI) Legal Dossier Generation:

flowchart TD
    subgraph Stage1 ["Stage 1: Normalization & Ingestion"]
        A[HWPX Budget Tables<br/>Unit Price x Qty = Total] --> B[HWP Table Parser]
        C[KONEPS Procurement<br/>Bids & Contracts] --> D[Entity Resolver<br/>Normalize Names & Addresses]
        E[dBrain+ Public Budget<br/>Itemized Ledgers] --> D
        B --> D
    end

    subgraph Stage2 ["Stage 2: Dual-Engine Reasoning (Dual Brain)"]
        F[Natural Language Audit Query] --> G{Query Router<br/>Intent Classifier}
        G -->|Numerical Stats / Spend Rankings| H[Deterministic Text-to-SQL<br/>Strict Grounded SQL Generation]
        G -->|Cartels / Networks / Collusion| I[Knowledge Graph GraphRAG<br/>Traverse Shared Address Clusters]
    end

    subgraph Stage3 ["Stage 3: Explainability (XAI) & Action"]
        H --> J[Interactive Auditor Synthesis]
        I --> J
        J --> K[XAI Audit Dossier Generator<br/>Bind Findings to Statutes & Precedents]
        K --> L[Stream Monitor<br/>5-Tier Threat Scoring & Contract Hold Alert]
    end

    D --> H
    D --> I

Part 1: Anticipated Challenges & Preventive Architectures​

Challenge 1: The Menace of LLM Numerical Hallucination​

  • The Anticipated Risk: LLMs are probabilistic sequence predictors, not verified calculators. If you prompt an LLM to calculate the sum of top private contracts under a $20M grant program, it inevitably fabricates subtotals or misplaces decimal points.
  • Preventive Architecture: Deterministic Text-to-SQL (TextToSQLEngine):
    • We strictly forbade the LLM from executing arithmetic directly.
    • The model's responsibility was constrained entirely to translating natural language inquiries into strict, parameterized SQL queries executed on an isolated SQLite/DuckDB engine.
    • Result: Quantitative data calculation error rate remains exactly 0%.
# src/rag/text_to_sql.py excerpt
class TextToSQLEngine:
    def translate_and_run(self, natural_language_query: str) -> Dict[str, Any]:
        """Translate query into deterministic, pre-verified fiscal SQL."""
        q = natural_language_query.lower()
        if "private contract" in q or "discretionary" in q:
            # Aggregate private contracts awarded below legal bidding thresholds
            sql = (
                "SELECT vendor_name, COUNT(*) as contract_count, SUM(amount) as total_awarded "
                "FROM procurement_contracts "
                "WHERE contract_type = 'private' "
                "GROUP BY vendor_name ORDER BY total_awarded DESC LIMIT 5"
            )
            summary = "Top 5 suppliers by private contract awards under legal limit"
        cur = self.conn.cursor()
        cur.execute(sql)
        ...

Challenge 2: Parsing Complex Hierarchical Tables in Unstructured Government Documents​

  • The Anticipated Risk: Government budget justifications in Korea are stored in binary HWP/HWPX documents with complex merged cells (rowspan/colspan) and nested text strings like Unit: $350 x 10 people x 12 months = $42,000. Standard PDF/OCR extractors scramble merged cell coordinates.
  • Preventive Architecture: Virtual 2D Grid Reconstruction (BudgetFormulaParser):
    • Reconstructed merged cell spans onto an in-memory 2D matrix to preserve the relationship between high-level expense categories (personnel, travel, equipment) and itemized line items.
    • Deployed regex tokenizers to extract [unit_price] x [quantity] x [frequency] = [total] with strict assertion checks verifying that unit_price * quantity * frequency == total.

Challenge 3: Concealed Collusion Networks in Split Procurement Contracts​

  • The Anticipated Risk: To bypass competitive bidding rules, corrupt officials split projects into sub-$20K micro-contracts awarded to 2 or 3 newly incorporated dummy companies sharing silent investors. Standard relational database queries view these transactions as isolated, legally compliant entries.
  • Preventive Architecture: Heterogeneous Knowledge Graph (BudgetKnowledgeGraph):
    • Modeled public agencies, procurement contracts, vendors, corporate directors, and physical street addresses as interconnected graph nodes with (Vendor)-[:LOCATED_AT]->(Address) relationships.

Part 2: Unexpected Real-World Errors & How We Resolved Them​

Even with clean initial architectures, pumping real-world public procurement records into the pipeline surfaced unexpected edge cases.

🚨 Real Error 1: Entity Fragmentation Causing Cartel Detection Collapse​

  • The Error Symptom:
    • Procurement records cataloged the exact same vendor under varied legal syntax: Daehan Soft Inc., Daehan Soft Co., Ltd., and (Corp) DaehanSoft.
    • Street addresses exhibited arbitrary floor and suite suffixes: 152 Teheran-ro, 4th Floor vs 152 Teheran-ro, Suite 401.
    • The Knowledge Graph created separate nodes for each spelling permutation. As a result, a 38.5M KRW procurement cartel operated across three shell companies in the exact same commercial suite completely evaded graph detection (0% recall).
  • The Fix: Two-Tier Normalization Engine (EntityResolver):
    • Stripped all corporate legal suffixes and whitespace across all entity names.
    • Built an address parser isolating base street numbers [City + District + Street + Building Number] from volatile floor/suite descriptors.
# src/pipeline/entity_resolver.py implementation
class EntityResolver:
    @staticmethod
    def normalize_company_name(name: str) -> str:
        """Strip corporate legal prefixes, suffixes, and whitespace."""
        n = re.sub(r'\(Corp\)|Inc\.|Co\.,\s*Ltd\.|LLC|\(주\)|주식회사', '', name, flags=re.I)
        return re.sub(r'\s+', '', n).strip()

    @staticmethod
    def normalize_address(address: str) -> Dict[str, str]:
        """Isolate base building address from floor/suite details."""
        clean_addr = re.sub(r'\s+', ' ', address).strip()
        base_match = re.search(r'^(.*?(\d+번?길\s*\d+|\d+))\s*(.*)$', clean_addr)
        if base_match:
            base_addr = base_match.group(1).strip()
            detail = base_match.group(3).strip()
            return {"raw": address, "base_address": base_addr, "detail": detail}
        return {"raw": address, "base_address": clean_addr, "detail": ""}

Outcome: Daehan Soft Inc. and (Corp) DaehanSoft merged into a single entity. The shared-address cartel detection rate jumped instantly from 0% to 100%.

🚨 Real Error 2: The "Temporal Smurfing" Date Window Loophole​

  • The Error Symptom:
    • Initial split-contract heuristics checked whether multiple contracts were signed on the same calendar day.
    • Corrupt purchasers easily circumvented this rule by staggering split awards: issuing a 19.5M KRW contract on Monday, and a 19.0M KRW contract on Thursday. Both transactions passed through the daily filter undetected.
  • The Fix: Dynamic Sliding Time Window (CollusionDetector):
    • Scrapped exact-date matching in favor of an N-day sliding window algorithm (7-day and 30-day temporal horizons).
    • Grouped contracts by issuing department and supplier clusters, evaluating cumulative contract sums across shifting time windows.
# src/models/collusion_detector.py sliding window algorithm
@staticmethod
def detect_smurfing_sliding_window(contracts: List[Dict], window_days: int = 30) -> List[Dict]:
    anomalies = []
    sorted_contracts = sorted(contracts, key=lambda x: x["contract_date"])
    for i, c in enumerate(sorted_contracts):
        window_total = c["amount"]
        related = [c]
        c_date = datetime.strptime(c["contract_date"], "%Y-%m-%d")
        
        for next_c in sorted_contracts[i+1:]:
            n_date = datetime.strptime(next_c["contract_date"], "%Y-%m-%d")
            if (n_date - c_date).days <= window_days:
                window_total += next_c["amount"]
                related.append(next_c)
        
        # Multiple sub-$20M contracts exceeding aggregate statutory limit
        if len(related) >= 2 and window_total > 20_000_000:
            anomalies.append({
                "type": "Temporal_Smurfing_Private_Contract",
                "accumulated_amount": window_total,
                "contract_count": len(related),
                "contracts": related
            })
    return anomalies

🚨 Real Error 3: Auditor Distrust of AI Black-Boxes & Missing Legal Precedents​

  • The Error Symptom:
    • During early demonstrations, our model generated warnings like: "Anomaly Risk Score: 92/100 (High Risk: Split Contract Suspected)".
    • Senior government auditors and legal inspectors rejected the output: "A numeric score is legally worthless. Which specific statute was breached? Without citation to established Board of Audit inspection precedents, this cannot support an indictment."
  • The Fix: Statute & Precedent-Bound XAI Generator (AuditReporter & CaseMatcher):
    • Bound anomaly patterns directly to the State Contract Act (Enforcement Decree Article 68: Prohibition of Split Contracts) and Subsidy Act Article 26-2.
    • Vectorized a 10-year official audit ruling database from the Board of Audit and Inspection (BAI), retrieving precedents with over 94% semantic similarity to auto-generate formal inspection dossiers.
# src/rag/audit_reporter.py statute mapping
STATUTE_MAPPING = {
    "Split_Contract_Smurfing": {
        "law": "State Contract Act Enforcement Decree Article 68 (Prohibition of Split Contracts)",
        "clause": "Single unified projects shall not be temporally partitioned to circumvent competitive bidding thresholds."
    },
    "Shared_Address_Cartel": {
        "law": "State Contract Act Article 27 & Monopoly Regulation Act Article 40",
        "clause": "Prohibition of unfair collaborative bidding via paper companies under unified de-facto control."
    }
}

Part 3: Test Harness Verification & Production Results​

To guarantee production reliability, we executed an automated regression test harness (tools/run_all_tests.py) covering all 11 sub-modules:

Test Harness SuiteTarget SubsystemInput Scenario & Target AssertionResult
verify_prototype.pyHWP Table Parser & Outlier ModelExpression 350,000 x 10 x 12 = 42,000,000PASS
verify_pipeline.pyEntity Resolver & CollectorMerge Daehan Soft Inc. with (Corp) DaehanSoftPASS
verify_graph_and_rag.pyKnowledge Graph & Delivery Index70% indirect overhead leakage (DEI 30%), Cartel detectionPASS
verify_interactive_rag.pyInteractive Audit RAG AgentDual-Engine Text-to-SQL + GraphRAG synthesisPASS
verify_streaming_monitor.pyReal-time Risk Stream MonitorDynamic 5-tier threat evaluation (NORMAL to CRITICAL)PASS
verify_case_matcher.py10-Year Audit Precedent DBCosine similarity match (>94%) with BAI rulingsPASS
verify_e2e_orchestrator.pyEnd-to-End OrchestratorFull ingestion to final Inspection Dossier exportPASS
======================================================================
📊 National Budget AI - Full Test Harness Suite
======================================================================
Total Modules: 11 PASS / 0 FAIL
Unit Price Outlier Detection Rate: 100%
Shared Address Cartel Expose: 2 Clusters ($38,500,000 total awarded)
Delivery Efficiency Index (DEI) Alert: 30.0 Score (70% Overhead Leakage)
All End-to-End Integrity Assertions Verified.
======================================================================

Conclusion: Engineering Principles for Modern Agentic Software​

Reflecting on this implementation journey, three core engineering maxims emerged:

  1. Delegate Math to Databases: Never allow an LLM to calculate arithmetic. Use the model solely for intent parsing and strict SQL synthesis, offloading computation to deterministic database engines.
  2. Real-World Entity Data is Messy: A single whitespace or corporate prefix difference will silently shatter graph traversal algorithms. Bulletproof Entity Resolution normalization must precede all agentic analysis.
  3. Explainability Creates Legal Standing: A standalone probability score convinces no enterprise auditor. Grounding AI outputs 1:1 against legal statutes and established judicial precedents is what turns an AI prototype into production infrastructure.

In our upcoming post, we will explore "Packaging Internal Enterprise Databases into Model Context Protocol (MCP) Servers for Claude and Cursor".

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