Institutional Deliberation as Executable Policy: A Research Framework for the U.S. Federal Government Agents Script
General Research Paper — _AugmentedIntelligence v11.0
| Field | Value | |
| Artifact under study | lua_scripts/US-Federal-Government-Agents.lua (v1.0) | |
| Companion documents | US_Federal_Government_Agents_Technical_Paper.md (engineering); AugmentedIntelligence_Discovery_Paper.md (platform-wide discovery) | |
| Author | Tyler Crockett \ | Macdaddy4sure.ai |
| License | Apache License 2.0 | |
| Document date | June 24, 2026 |
Keywords: multi-agent LLM systems, computational governance, deliberative simulation, AI policy, institutional design, executable policy, AI safety tooling, weighted voting, ethics gating
Abstract
Large language models are increasingly used to simulate committees, legislatures, and advisory councils. Most such simulations produce narrative prose or synthetic votes with no operational consequence. The U.S. Federal Government Agents script (US-Federal-Government-Agents.lua) takes a different approach: it embeds a seventeen-agent institutional simulation inside _AugmentedIntelligence_, maps legislative outcomes to real typed commands on the host system, and subjects agent utterances to automated ethics classification before votes are tallied.
This paper presents the script as a general research instrument. We formulate research questions about multi-provider deliberation, institutional phase design, safety–governance tradeoffs, and command prioritization under policy framing. We describe reproducible experimental protocols, measurable dependent variables, validity threats, and ethical boundaries. The contribution is not a claim about real U.S. government behavior, but a methodology for studying how role-structured LLM ensembles select among concrete AI-governance actions when those actions can actually be executed.
1. Introduction
1.1 Motivation
AI governance research often proceeds along two disconnected tracks:
- Normative and legal analysis — What should policy require?
- Engineering practice — What commands, gates, and observability tools exist in production stacks?
Bridging these tracks requires intermediaries: artifacts where policy language, institutional roles, and system operations meet in a controlled, repeatable setting. Chat-based “simulate Congress” prompts lack closure—there is no enactment step, no ground truth about what the system would have done, and no structured record comparable across runs.
The Federal Government Agents script closes the loop:
Policy question (bill)
→ Multi-agent deliberation (17 roles × 6 phases)
→ Weighted vote over 14 governance actions
→ Optional presidential veto and judicial hold
→ execute_command(enacted_action)
That closure makes the script suitable for empirical and comparative research, not only thought experiments.
1.1 Research contribution
This paper contributes:
- A conceptual framing of the script as institutional deliberation with executable external validity at the tooling layer.
- A catalog of research questions (RQ1–RQ8) operators can investigate without modifying C++.
- Experimental protocols with independent variables, dependent variables, and controls.
- A validity and ethics section distinguishing simulation findings from legal or governmental claims.
Engineering details (prompt schemas, roster tables, session state machine) are delegated to the companion technical paper.
1.3 What this paper is not
| Not claimed | Why |
| Constitutional fidelity | Phases are stylized; no filibuster, markup, or statutory drafting |
| Predictive political science | Agents are LLM personas, not calibrated voter models |
| Legal advice | Outputs are research artifacts |
| Clinical or operational government guidance | Ballot commands target the AI host stack, not federal IT systems |
2. Background and related work
2.1 Multi-agent LLM deliberation
Recent work explores multiple LLM instances debating, critiquing, or voting on answers to improve factuality and reasoning. Common patterns include:
- Debate — Pro/con agents refine a single output.
- Council / jury — Independent votes aggregated by plurality or consensus.
- Role-play — Persona conditioning (expert, critic, user advocate).
The Federal Government Agents script combines role-play (branch/chamber titles) with weighted council voting and a fixed action space (the ballot). Unlike open-ended debate, votes must parse as VOTE: <action_id> against fourteen enumerated options—a design choice that trades rhetorical richness for measurable choice data.
2.2 Computational governance and social simulation
Agent-based models have long simulated legislatures, firms, and markets. Traditional ABMs use hand-coded utility functions. LLM-backed agents replace explicit utilities with language-mediated preferences, enabling nuanced rationales (REASON:) but introducing opacity and provider variance.
This script sits between classic ABM and interactive LLM chat:
| Property | Classic ABM | LLM chat | Federal Gov script |
| Structured votes | Yes | Rarely | Yes |
| Natural-language rationale | No | Yes | Yes |
| Institutional phases | Optional | No | Yes (6 phases) |
| Real-world side effects | Simulated | None | Optional (execute_command) |
2.3 AI safety and policy toolchains
The ballot actions align with operational AI governance categories: ethics gates, action classification, executive planning, memory recall, knowledge status, observability, agent oversight (human agent status, rsi agent status), and engineering verification (run tests). Research on the script therefore intersects AI safety instrumentation—which tools institutions (simulated or real) prioritize when framed by policy bills.
2.4 Position within _AugmentedIntelligence_
The platform hosts parallel simulations (Enterprise AI Company, House Diagnostic Team, RSI APICouncil). The Federal Government script is distinctive for separation-of-powers phasing and a static governance ballot. Cross-study comparisons can hold the host constant while varying institutional metaphor—useful for meta-research on framing effects.
3. The script as a research instrument
3.1 Unit of analysis
The primary unit is a session: one pass through six phases producing a history entry with vote bundles, conference winner, presidential action, judicial hold flag, and optional execution result.
Secondary units:
- Agent speech — Individual POSITION / VOTE / RISK / REASON block
- Chamber tally — Weighted distribution per phase
- Enacted command — Resolved typed command string
3.2 Independent variables (manipulable)
| Variable | Control mechanism | Example levels |
| Bill text | USGOV_BILL | AI safety framework; transparency act; deregulation memo |
| Session count | USGOV_SESSIONS | 1, 3, 10 (longitudinal) |
| Provider set | settings.txt API flags | {Grok}, {ChatGPT, Claude}, all six |
| Converse mode | USGOV_CONVERSE_OPTIONAL | 0 (required speech), 1 (abstain allowed) |
| Debate depth | USGOV_VOTE_ONLY | 0 (full template), 1 (vote-only) |
| Execution | USGOV_EXECUTE_WINNER | 0 (choice only), 1 (run command) |
| Dry run | USGOV_DRY_RUN | 1 for structure tests without API cost |
3.3 Dependent variables (measurable)
| Variable | Operationalization |
| Enacted action id | history[n].enacted_action_id |
| Enactment rate | Fraction of sessions with non-nil enactment after veto/hold |
| Chamber divergence | Jaccard distance between senate/house/ committee winner sets |
| Ethics block rate | Speeches with blocked = true / total speeches |
| Abstention rate | Speeches with abstained = true / total (optional converse) |
| Vote entropy | Shannon entropy over ballot ids in chamber tally |
| Presidential veto rate | Sessions with presidential_action == veto |
| Judicial hold rate | Sessions with judicial_hold == true |
| Provider–vote correlation | Cross-tab assigned_api × winning action across runs |
| Execution success | Presence/absence of error lines in execution |
3.4 Controls and replication
Minimum replication package:
- USGOV_BILL exact string
- Enabled API list and model versions (from llm status capture)
- USGOV_* flags
- Host build / rsi agent status or developer status line
- Complete console transcript or parsed history table
- Seed note: LLM APIs are stochastic unless temperature pinned externally
Self-test baseline: USGOV_SELFTEST=1 provides deterministic structural validation (7 assertions); use before batch studies.
4. Research questions
RQ1 — Framing effects on governance command choice
Question: How does bill wording shift the distribution of enacted ballot ids?
Hypothesis: Security- and compliance-framed bills increase votes for ethics_gate_status, ethics_action_classify, and us_code_status; innovation-framed bills increase executive_plan and llm_status.
Method: Fixed provider set; N ≥ 30 sessions per bill variant; compare enactment histograms.
RQ2 — Chamber divergence under weighted voting
Question: Do Senate, House, and cabinet phases systematically disagree, and does conference weighting (2.0×) amplify or dampen that disagreement?
Hypothesis: Judicial and legislative agents overweight ethics/legal ballot items; executive agents overweight awareness_refresh and executive_plan.
Method: Parse per-phase winner fields; compute disagreement rate and conference override frequency.
RQ3 — Multi-provider variance
Question: When multiple APIs are enabled, how much does preferred_api assignment explain vote outcomes versus role/chamber?
Hypothesis: Provider identity explains variance within the same role more than role explains variance within the same provider (provider-specific bias).
Method: Rotate enabled APIs; run matched bills; mixed-effects style analysis on speech-level votes (manual export from transcripts).
RQ4 — Abstention and optional converse
Question: Does USGOV_CONVERSE_OPTIONAL=1 improve vote parsimony (fewer blocked/invalid votes) or reduce enactment rates?
Hypothesis: Abstention lowers invalid parse rate but increases none enactments when evidence is sparse.
Method: A/B paired bills across optional converse on/off.
RQ5 — Ethics gating as institutional friction
Question: What fraction of agent speeches are blocked pre-tally, and does judicial hold correlate with high-risk ballot winners?
Hypothesis: Judicial hold triggers primarily when conference selects ethics_action_classify and enactment text scores high destructiveness.
Method: Log blocked speeches and judicial_hold; correlate with RISK: labels in agent text.
RQ6 — Presidential veto as executive counterweight
Question: How often does the presidential agent veto conference winners, and is veto text predictable from bill category?
Hypothesis: Veto rate is low in dry policy audits but rises for bills mentioning “mandate” or “ban” (operator-authored stimuli).
Method: Coded bill corpus; measure veto frequency.
RQ7 — Executable enactment and system state change
Question: When USGOV_EXECUTE_WINNER=1, do enacted commands produce measurable host state deltas (e.g., plan created, observability events logged)?
Hypothesis: developer_observability and run_tests produce the most parseable stdout signatures; plan create mutates executive plan state.
Method: Pre/post snapshots: plan status, developer observability show, run tests.
RQ8 — Longitudinal stability
Question: Across repeated sessions with identical bills, do enacted actions stabilize or oscillate?
Hypothesis: Oscillation is common at temperature > 0; distributions converge only when provider set and prompts are frozen and APIs are low-temperature.
Method: USGOV_SESSIONS=10 single bill; run-length analysis on enacted_action_id series.
5. Methodology
5.1 Research design overview
flowchart LR
subgraph Design
B[Bill corpus]
T[Treatment flags]
P[Provider matrix]
end
subgraph Instrument
S[USGov:run_session]
end
subgraph Observation
H[history table]
L[Logs / stdout]
end
subgraph Analysis
Q[Descriptive stats]
C[Comparative tests]
I[Qualitative coding of REASON]
end
B –> S
T –> S
P –> S
S –> H
S –> L
H –> Q
H –> C
L –> I
5.2 Sample bill corpus (suggested)
Researchers may adopt a standardized corpus for cross-lab comparison:
| ID | Bill theme | Example USGOV_BILL prefix |
| B1 | AI safety | National AI safety and governance framework |
| B2 | Transparency | Federal AI transparency and audit act |
| B3 | Innovation | Accelerating public-sector AI innovation |
| B4 | Compliance | Strict AI compliance with US Code Title 44 |
| B5 | Oversight | Independent RSI and human-agent oversight mandate |
| B6 | Neutral | Routine agency systems status review |
Neutral bills test whether enactment defaults to engineering baselines (run_tests, llm_status) versus ethics-heavy choices.
5.3 Protocol P1 — Baseline characterization (1 day)
- USGOV_DRY_RUN=1, USGOV_SESSIONS=3 — verify pipeline.
- USGOV_SELFTEST=1 — structural regression.
- Live run with one API, default bill, USGOV_EXECUTE_WINNER=0.
- Record enacted distribution.
Deliverable: Baseline report appendix for the study.
5.4 Protocol P2 — Provider matrix (3–5 days)
Full factorial across enabled APIs {single provider, pairs, sextet} × bills {B1, B6} × sessions=5.
Deliverable: Heatmap of enacted action id by provider condition.
5.5 Protocol P3 — Institutional friction audit (2 days)
Enable ethics classifier; force bills B1 and B4; measure block rate, hold rate, veto rate.
Deliverable: Safety friction dashboard for policy framing.
5.6 Protocol P4 — Enactment impact (1 day, controlled host)
USGOV_EXECUTE_WINNER=1 on isolated workstation; only low-risk ballot items manually allowed if needed; capture pre/post command outputs.
Deliverable: Evidence table linking vote to host observable.
5.7 Qualitative analysis
Agent REASON: fields support thematic coding:
- Constitutional framing
- Risk aversion
- Technical capability citations
- Inter-branch turf language
Qualitative codes can be merged with quantitative vote data for mixed-methods papers.
6. Preliminary observations (illustrative, not empirical claims)
The following are plausible patterns researchers may test; they are not reported as results from a formal study:
- Ethics ballot concentration — Judicial and executive-heavy phases may cluster on ethics_* and us_code_* ids when bills mention “safety” or “compliance.”
- Engineering fallback — Under vague bills, run_tests and llm_status may win as lowest-risk operational choices.
- Provider tone — Models assigned to attorney_general and chief_justice may produce higher RISK: high labels in text even when voting for low-risk actions.
- Conference homogenization — 2.0× weights may collapse tri-chamber disagreement into a single repeated winner across sessions with identical bills.
These hypotheses illustrate how the instrument generates falsifiable statements.
7. Validity threats
| Threat | Description | Mitigation |
| Construct | Chambers ≠ real Congress | Treat as institutional metaphor; triangulate with Enterprise simulation |
| Internal | LLM stochasticity | Multiple sessions; report confidence intervals |
| External | Host commands ≠ federal IT | Limit claims to AI stack governance |
| Conclusion | Enactment correlation ≠ causation | Use Protocol P4 pre/post states |
| Mono-operation | Ballot size fixed at 14 | Acknowledge blind spots for unlisted actions |
| Prompt leakage | Bill text injects instructions | Sanitize bills; use neutral controls |
| API switching artifacts | Global enable flags mutate per agent | Document restore behavior; avoid parallel host commands |
8. Ethical considerations
8.1 Simulation ethics
- Do not present outputs as real government positions or forecasts.
- Label all publications as computational simulation.
- Avoid deceptive use in policy briefings without explicit framing.
8.2 Execution ethics
USGOV_EXECUTE_WINNER=1 runs real commands on the operator’s machine. Research protocols should:
- Prefer USGOV_EXECUTE_WINNER=0 for pure choice studies.
- Use isolated hosts for enactment impact studies.
- Treat plan create, awareness refresh, and similar commands as state-changing events.
8.3 Data ethics
Agent speeches may reproduce training-data political or legal rhetoric. Store transcripts under appropriate access controls if bills contain sensitive or proprietary text.
8.4 Human subjects
Standard runs do not recruit human participants. Human-subjects review may apply if researchers embed real stakeholder quotes in USGOV_BILL without consent.
9. Limitations and future research
9.1 Script limitations (v1.0)
- Federal Reserve and NSC agents are rostered but not invoked in phase filters.
- USGOV_STATE_FILE is documented but not implemented—long studies must externalize history manually.
- branch_tags on ballot items are not enforced per chamber.
- Tie-breaking in weighted tally is implementation-dependent.
9.2 Future research directions
| Direction | Extension |
| Calibrated personas | Fine-tune or constrain agents on historical voting records |
| Dynamic ballots | Load ballot items from MySQL or JSON for domain studies |
| Human-in-the-loop | Operator as “witness” agent with veto override |
| Cross-simulation | Same bill run in Enterprise vs. Federal vs. APICouncil |
| Persistence | State file + diffable session archives |
| Formal verification | Map enacted commands to ethics policy automata |
10. Discussion
The Federal Government Agents script transforms a familiar research construct—a multi-agent legislature—into an operable policy selector for AI governance tooling. Its value for general research lies in three properties:
- Structured choice data — Votes are enumerable, not buried in prose.
- Institutional time — Phases create sequential agenda effects absent from one-shot council prompts.
- Grounded enactment — Optional execution ties deliberation to measurable host behavior.
For the broader _AugmentedIntelligence_ research program, the script is one node in a simulation constellation. Comparing institutional (federal), corporate (enterprise), clinical (House), and provider-native (APICouncil) framings on identical underlying commands may reveal metaphor effects as first-class variables in AI operations research—a topic largely unexplored in traditional ML benchmarks.
11. Conclusion
We have presented a general research framework for US-Federal-Government-Agents.lua: research questions, variables, protocols, validity threats, and ethical guardrails. The script enables disciplined study of how role-structured, multi-provider LLM ensembles prioritize ethics, planning, memory, observability, and oversight commands when policy questions are staged as bills—and, when execution is enabled, how those choices alter a live augmented-intelligence host.
Researchers should pair this paper with the technical specification for implementation fidelity and with the platform discovery paper for cross-module context. Used with clear scope boundaries, the script is a practical bridge between governance thought experiments and instrumented AI systems research.
References
- Tyler Crockett. United States Federal Government Agents — Technical Paper. June 2026.
- Tyler Crockett. What Can Be Discovered with _AugmentedIntelligence? June 2026.
- Tyler Crockett. AugmentedIntelligence v11.0 — Technical Paper. June 2026.
- lua_scripts/US-Federal-Government-Agents.lua — v1.0 source.
- lua_scripts/RSI-Audio-Visual-Agent.lua — APICouncil deliberation reference.
- lua_scripts/Enterprise-AI-Company.lua — Corporate governance simulation comparator.
- Irving et al. (2018) — AI safety via debate (conceptual parallel).
- Argyle et al. (2023) — Out of one, many: LLM synthetic sampling (persona variance context).
References 7–8 are thematic analogues; cite primary empirical literature as appropriate for submission venues.
Appendix A — Minimal reproducibility checklist
- [ ] USGOV_SELFTEST=1 passes (7/7)
- [ ] Bill strings archived verbatim
- [ ] API enable list recorded
- [ ] USGOV_* flags recorded
- [ ] Session transcripts or history exported
- [ ] Execution on/off noted
- [ ] Host version / build noted
- [ ] Stochastic settings (temperature) documented if available
Appendix B — Suggested results table template
| Run ID | Bill | APIs | Sessions | Enacted (mode) | Top action % | Veto % | Hold % | Block % |
| 001 | B1 | chatgpt | 5 | ethics_gate_status | 40% | 0% | 20% | 5% |
| 002 | B6 | all | 5 | llm_status | 35% | 10% | 0% | 2% |
Appendix C — Launch commands
simple script execute lua_scripts/US-Federal-Government-Agents.lua
set USGOV_BILL=National AI safety and governance framework
set USGOV_SESSIONS=5
set USGOV_EXECUTE_WINNER=0
simple script execute lua_scripts/US-Federal-Government-Agents.lua
End of document
Filed under: Uncategorized - @ June 24, 2026 10:21 am