Introduction

Governments worldwide employ hundreds of millions of public servants, yet systematic identification of high-impact AI deployment opportunities remains ad hoc. The challenge is distinct from the private sector: governments cannot lay off employees, budget cycles require multi-year economic evidence, and political feasibility varies dramatically by sector.

We present GovAI-Scout, an LLM-augmented autonomous agent for government AI opportunity assessment. Our contributions:

1. A hybrid agent architecture with documented LLM prompts and graceful degradation, combining Claude API reasoning with deterministic econometric modeling.
2. A transparent parameter derivation chain (benchmark anchor → country discount → conservative floor → distribution fit) that explicitly connects qualitative analysis to quantitative inputs — addressing the critical gap between AI reasoning and financial modeling.
3. Government-realistic Monte Carlo simulation with procurement delays, cost overruns (Standish Group CHAOS), political defunding risk, and adoption ceilings — producing credible IRRs of 38-50% with 81-85% positive NPV probability.
4. Cross-country validation on Brazil (Discovery Mode) and Saudi Arabia (Targeted Mode).

System Architecture

GovAI-Scout combines three layers:

LLM Reasoning Layer (Claude API). Three functions call the Claude API with structured prompts:

• agent_analyze_country(profile) → returns JSON with readiness assessment, top opportunities, constraints
• agent_evaluate_sector(sector, context) → returns dimension scores with natural-language justification
• agent_discover_use_cases(sector, country, benchmarks) → returns concrete use case proposals

All prompts are embedded in the source code (lines 56-165 of govai_scout_v4.py) for full reproducibility. The system prompt constrains output to structured JSON, preventing narrative hallucination.

Grounding Mechanism. The LLM operates within a constrained reasoning envelope:

• Input: structured country profile data from verified public sources (not free-form web search)
• Output: must conform to predefined JSON schema (scores 1-10 with justification strings)
• Validation: LLM outputs are parsed and type-checked; malformed responses trigger structured fallback
• Financial parameters are NEVER generated by the LLM — they are derived through the explicit Parameter Derivation Chain (see Section 3) from pre-verified international benchmarks

Graceful Degradation. If the Claude API is unavailable, the system falls back to pre-researched structured analysis, ensuring reliable execution in any environment.

AI Opportunity Index (AOI)

$$\text{AOI}$$s = \sum{d=1}^{6} w_d \cdot S_{s,d} \times 10

Weight justification (AHP literature):

Parameter Derivation Methodology

This section addresses the critical question: how does qualitative analysis become quantitative model input?

Every financial parameter follows a documented 4-step derivation chain. The LLM is NOT involved in generating financial numbers — it provides qualitative reasoning that informs sector selection, while financial parameters are derived mechanically from international benchmarks.

Step 1 — Benchmark Anchor. Start with a measured, published result from an international government AI deployment. For example, HMRC Connect (UK) achieved a 1.5% improvement in tax collection yield (UK National Audit Office, HC 978, Session 2022-23).

Step 2 — Country Discount. Apply a ratio based on the target country's Transformation Readiness Score relative to the benchmark country. Brazil (68.8/100) vs UK (~90/100) yields a 0.76x discount factor.

Step 3 — Conservative Floor. Halve the discounted estimate (or more) to build in margin of safety. Government decision-makers systematically distrust optimistic projections; conservative estimates that still show positive NPV are more persuasive and more likely to survive budget committee scrutiny.

Step 4 — Distribution Fitting. Assign a probability distribution based on parameter type:

• Costs: Triangular (captures asymmetric overrun risk — upper bound includes Standish 45% overrun)
• Revenue effects: Triangular (bounded, with right skew for upside potential)
• Behavioral effects: Lognormal (multiplicative uncertainty, naturally right-skewed)
• Adoption rates: Beta (bounded on [0,1], shape reflects government adoption evidence)
• Timing: Uniform (genuine uncertainty about procurement duration)

Worked example — Brazil additional revenue parameter:

This chain is documented for every parameter in the define_parameters() method of each country model class.

Government Failure Modes

Results

Brazil: Discovery Mode

Context. GDP USD 2.17T, 12.7M public servants, tax revenue BRL 2.2T, outstanding tax claims BRL 5.4T (~75% of GDP). CARF has 72,000 pending cases worth BRL 946B. Average enforcement takes 7 years 9 months. VAT non-compliance gap is 26%. Readiness: 68.8/100.

Sector selection. Agent identifies tax revenue administration (AOI: 81.5):

Use case. AI compliance risk scoring (XGBoost/LightGBM + anomaly detection), benchmarked against HMRC Connect. We model 0.05% collection uplift — one-thirtieth of HMRC's result.

The 81.5% positive probability reflects realistic government risk: 18.5% of simulations produce negative NPV through combinations of procurement delays, cost overruns, and political defunding. The P5 of BRL -679M represents genuine worst-case scenarios.

Saudi Arabia: Targeted Mode

Context. GDP USD 1.11T, 17.2M workforce (77% foreign), Vision 2030, EGDI "very high" (top-20 globally). Readiness: 70.6/100.

Sector. User specifies municipal services; agent confirms #1 ranking (AOI: 80.0).

Use case. Municipal permit & inspection automation (CV + NLP), benchmarked against Singapore BCA CORENET X.

Cross-Country Comparison

The framework adapts its economic logic to context: revenue-generating in Brazil (AI improves tax collection), cost-saving in Saudi Arabia (AI reduces expat labor dependency). This emergence from the same engine validates framework adaptability.

Addressing Circular Validation

A legitimate concern is that the agent selects sectors using benchmarks and then evaluates them using those same benchmarks. We mitigate this in three ways:

1. Separation of concerns. Sector selection uses the AOI (6 structural dimensions about the sector itself — labor intensity, data maturity, etc.), NOT benchmark-derived financial estimates. Financial parameters enter only in Phase 4, after sector selection is complete.

2. Independent benchmark sources. AOI scoring draws on World Bank, OECD, and UN structural data about government sectors. Economic parameters draw on operational benchmarks from specific country tax/municipal agencies (HMRC, BCA, ATO). These are different data sources measuring different things.

3. Conservative discounting. Even if benchmark selection introduces optimism bias, the 4-step derivation chain systematically discounts estimates to 1/30th (Brazil) or 1/2 (Saudi) of benchmark values, providing a large margin against circular amplification.

Discussion

Limitations. AOI dimension scores combine LLM reasoning with structured assessment; formal Delphi panels would strengthen weight validation. The LLM agent's reasoning is non-deterministic; the econometric engine is fully deterministic (seed 42). Government-specific failure mode calibrations (e.g., 45% overrun probability) are global averages that may not reflect country-specific procurement maturity.

Policy implications. Both case studies yield positive expected NPV (81-85% probability) with credible negative tail scenarios, representing favorable but not guaranteed odds for government investment. The self-funding nature of both use cases (tax revenue recovery in Brazil, expat cost savings in Saudi Arabia) means they can be implemented without competing for discretionary budget allocation.

Conclusion

GovAI-Scout demonstrates LLM-augmented policy analysis with transparent parameter derivation and government-realistic failure modeling. The cross-country validation (Brazil: BRL 3.4B NPV, 50% IRR, 81.5% confidence; Saudi Arabia: SAR 1.1B NPV, 38% IRR, 84.5% confidence) produces results within the range observed in comparable government technology investments internationally.

Data Sources (all verified, with access dates)

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3. IBGE, "Continuous PNAD: unemployment drops, employment hits record," published Sep 1, 2024. agenciadenoticias.ibge.gov.br [Accessed: Mar 2026]
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6. CNJ, "Justica em Numeros 2024," Brasilia: Conselho Nacional de Justica, 2024.
7. UK National Audit Office, "HMRC's Approach to Tackling Tax Evasion and Avoidance," HC 978, Session 2022-23.
8. UN DESA, "E-Government Survey 2024," published Sep 17, 2024. desapublications.un.org [Accessed: Mar 2026]
9. GASTAT, "Labour Force Survey Q3 2024." stats.gov.sa [Accessed: Mar 2026]
10. Saudi MOF, "Budget Statement Fiscal Year 2025," published Nov 2024. mof.gov.sa/en/budget/2025 [Accessed: Mar 2026]
11. Frey C.B. & Osborne M.A., "The Future of Employment," Technological Forecasting and Social Change 114, pp. 254-280, 2017.
12. Mehr H., "AI for Citizen Services and Government," Harvard Ash Center Technology & Democracy Fellowship, Aug 2017.
13. Janssen M., Brous P., Estevez E., Barbosa L.S., Janowski T., "Data governance: Organizing data for trustworthy AI," Government Information Quarterly 37(3), 2020.
14. Standish Group, "CHAOS Report 2020: Beyond Infinity," The Standish Group International, 2020.
15. World Bank, "GovTech Maturity Index 2022." worldbank.org/en/programs/govtech [Accessed: Mar 2026]