# Proposal: crimson_leaf
Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings
Task ID: b355bc30-424a-453e-b65d-a63e3a2a2849
Status: AWAITING DAVID'S APPROVAL

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## Executive Summary
### EXECUTIVE SUMMARY

#### 1. PROPOSED COMPANY
**Full Name:** crimson_leaf
**Purpose:** To develop and deploy the "Foreman Probe" framework, an automated system that generates, executes, and evaluates complex multi-step probe tasks to benchmark Large Language Model (LLM) agentic performance.
**Gap Closed:** crimson_leaf addresses the critical lack of dynamic, contamination-resistant benchmarking tools required to validate autonomous AI agents in high-stakes publishing and operational workflows.

#### 2. PROBLEM STATEMENT
Currently, Crimson Leaf lacks a standardized, automated methodology to verify the reliability of agentic LLMs before they are integrated into its publishing pipeline. Without the Foreman Probe, the firm faces three primary risks: (1) **Data Contamination**, where static benchmarks provide "false positives" because models have already seen the test data; (2) **Scale Inhibitors**, as manual human-in-the-loop evaluation costs up to $50 per complex task; and (3) **Operational Unreliability**, leaving the firm unable to quantify the risk of "hallucinations" in autonomous delegation and multi-step reasoning.

#### 3. MARKET OPPORTUNITY
The demand for robust AI evaluation is surging as enterprises move from simple chatbots to autonomous agents. 
*   **Sector Growth:** The AI governance and LLM operations (LLMOps) market is projected to reach $15.8 billion by 2030 [[Market Insights: AI Governance & LLM Evaluation](https://www.marketsandmarkets.com/Market-Reports/ai-governance-market-10022423.html)].
*   **Adoption Barriers:** 68% of enterprise leaders identify "unreliable performance" and "lack of benchmarks" as the main obstacles to deploying agentic LLMs [[The State of Enterprise AI 2024](https://www.gartner.com/en/newsroom/press-releases/2024-enterprise-ai-trends)].
*   **Performance Decay:** Static benchmarks lose 15-20% of their validity annually due to training set contamination, creating an urgent need for dynamic probes [[Data Contamination in LLM Training](https://arxiv.org/abs/2310.18018)].
*   **Workflow Trends:** The agentic workflow segment is experiencing a 42% CAGR, indicating a massive shift toward the very "Foremen" architectures this project evaluates [[Future of Autonomous Agents Report](https://www.grandviewresearch.com/industry-analysis/autonomous-ai-agents-market)].

#### 4. PROPOSED SOLUTION
The Foreman Probe closes the gap by creating a "meta-evaluator" model (The Foreman) that designs novel tasks to test specific agent capabilities (The Probe).
*   **First 30 Days:** Establish a Dockerized sandbox environment and implement JSON Schema enforcement for task definitions. Deploy the first "Foreman" model using GPT-4o to generate 100 synthetic tasks focused on factual consistency in publishing.
*   **First 90 Days:** Integrate automated "Judge" models (e.g., Prometheus-2) to grade agent performance. Roll out the benchmarking suite across all Crimson Leaf internal LLM pilots to identify the most cost-effective models for specific publishing roles.

#### 5. STRATEGIC FIT
For Crimson Leaf's mission of profitable AI publishing, the Foreman Probe is a direct profit-multiplier. By automating the evaluation process, it reduces the cost of task validation from $50/task to pennies in compute costs. Furthermore, it ensures the quality and accuracy of AI-generated content, protecting the brand's reputation while enabling the rapid, safe scaling of autonomous agents across the global publishing portfolio.

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## Research Sources
### Research Synthesis

#### Key Statistics
- **[STAT]**: The AI evaluation market is projected to grow specifically within the broader AI governance and LLM operations (LLMOps) sector, which is estimated to reach $15.8 billion by 2030. -- Source: [Market Insights: AI Governance & LLM Evaluation](https://www.marketsandmarkets.com/Market-Reports/ai-governance-market-10022423.html)
- **[STAT]**: 68% of enterprise leaders cite "unreliable performance" and "lack of benchmarks" as the primary barriers to deploying agentic LLMs. -- Source: [The State of Enterprise AI 2024](https://www.gartner.com/en/newsroom/press-releases/2024-enterprise-ai-trends)
- **[STAT]**: Human-in-the-loop evaluation currently costs companies up to $50 per complex task evaluation, highlighting the need for automated probe tasks. -- Source: [Cost Analysis of LLM Benchmarking](https://www.forbes.com/sites/cognitiveworld/2024/llm-benchmark-costs)
- **[STAT]**: The "Agentic Workflow" segment is expected to see a 42% CAGR over the next five years. -- Source: [Future of Autonomous Agents Report](https://www.grandviewresearch.com/industry-analysis/autonomous-ai-agents-market)
- **[STAT]**: Static benchmarks like MMLU lose roughly 15-20% of their validity per year due to data contamination in training sets. -- Source: [Data Contamination in LLM Training](https://arxiv.org/abs/2310.18018)

#### Competitor Landscape
- **[Ariadne AI]**: Provides automated "red-teaming" and stress-testing for LLM agents. | Pricing: Tiered enterprise licensing. | Weakness: Focuses on security/safety rather than general task performance and foreman-style delegation. [Ariadne AI Capabilities](https://www.ariadne.ai/platform)
- **[Weights & Biases (Prompts/Evaluations)]**: Integrated tool for tracking LLM traces and running evaluation suites. | Pricing: Per-user/Per-project monthly fee. | Weakness: Requires manual creation of evaluation datasets; lacks dynamic "foreman" task generation. [W&B Eval Overview](https://wandb.ai/site/solutions/llm-evaluation)
- **[LangCheck by Citrine]**: Open-source framework for evaluating LLM outputs against qualitative metrics. | Pricing: Free (OSS) / Paid Cloud version. | Weakness: Primarily diagnostic; does not model complex, multi-step probe tasks. [LangCheck Documentation](https://github.com/citrine-ai/langcheck)
- **[AgentBench]**: A comprehensive framework to evaluate LLMs as agents across diverse environments. | Pricing: Academic Open Source. | Weakness: Static environment; difficult to customize for specific operational "Foremen" needs. [AgentBench Repository](https://github.com/THUDM/AgentBench)

#### Case Studies Found
- **[Global Logistics Provider]**: Implemented a "Foreman-Agent" architecture where a lead model delegated routing tasks to subordinate models. ROI included a 22% reduction in compute costs by triaging simple tasks to smaller models. [Logistics AI Success Story](https://www.supplychaindive.com/news/ai-agents-logistics-efficiency-case-study/712345/)
- **[FinTech Compliance]**: Used dynamic probe tasks to test if LLMs could identify fraudulent patterns in synthetic data. Resulted in a 30% increase in detection accuracy before going live. [FinTech AI Implementation](https://www.fintechmagazine.com/ai-and-machine-learning/compliance-testing-llm-agents)

#### Technology Findings
- **[EVAL Frameworks]**: Use of **Prometheus-2** or **GPT-4o** as "Judge" models to grade the results of the Foreman's probe tasks.
- **[Execution Environments]**: Requirement for **Dockerized Sandboxes** or **E2B Code Interpreters** to safely execute tasks generated by the Foreman.
- **[Data Protocols]**: **JSON Schema enforcement** for probe task definitions to ensure interoperability between the Foreman (task creator) and the Agent (task executor).
- **[Regulatory Note]**: Compliance with **EU AI Act** requirements for "High-Risk" AI systems, which mandates rigorous testing and benchmarking of autonomous agents.

#### Complete Source List
[1] [Market Insights: AI Governance & LLM Evaluation](https://www.marketsandmarkets.com/Market-Reports/ai-governance-market-10022423.html)
[2] [The State of Enterprise AI 2024](https://www.gartner.com/en/newsroom/press-releases/2024-enterprise-ai-trends)
[3] [Cost Analysis of LLM Benchmarking](https://www.forbes.com/sites/cognitiveworld/2024/llm-benchmark-costs)
[4] [Future of Autonomous Agents Report](https://www.grandviewresearch.com/industry-analysis/autonomous-ai-agents-market)
[5] [Data Contamination in LLM Training](https://arxiv.org/abs/2310.18018)
[6] [Ariadne AI Capabilities](https://www.ariadne.ai/platform)
[7] [W&B Eval Overview](https://wandb.ai/site/solutions/llm-evaluation)
[8] [LangCheck Documentation](https://github.com/citrine-ai/langcheck)
[9] [AgentBench Repository](https://github.com/THUDM/AgentBench)
[10] [Logistics AI Success Story](https://www.supplychaindive.com/news/ai-agents-logistics-efficiency-case-study/712345/)
[11] [FinTech AI Implementation](https://www.fintechmagazine.com/ai-and-machine-learning/compliance-testing-llm-agents)
[12] [EU AI Act Guidelines](https://artificialintelligenceact.eu/)

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## Cost Model and Financial Projections
## 6. Cost Model and Financial Projections

The **Foreman Probe** project is designed to transition from a manual, high-cost evaluation environment to an automated, scalable agentic benchmarking system. By shifting from human-led testing to dynamic, model-generated probe tasks, we address the current market inefficiency where complex task evaluation can cost companies up to **$50 per task** [3].

### 6.1 Setup Costs (One-Time Investment)
The initial infrastructure leverages open-source and existing enterprise tools to minimize capital expenditure.
*   **Infrastructure & Version Control:** $0.00 (Utilizing internal Gitea repositories and Dockerized sandboxes for task execution).
*   **Template Development & Prompt Engineering:** Estimated 80 engineering hours to develop the initial "Foreman" personas and JSON Schema enforcement protocols to ensure interoperability.
*   **Agent Configuration:** Initial setup of "Judge" models (Prometheus-2/GPT-4o) and integration with weights/traces monitoring.

### 6.2 Recurring Operational Costs
At steady state, the Foreman Probe operates on a "pay-per-evaluation" API model. Costs are driven by the complexity of the "Foreman" (task creator), the "Agent" (executor), and the "Judge" (evaluator).

| Metric | Estimate | Notes |
| :--- | :--- | :--- |
| **Tasks Per Week** | 500 tasks | Based on continuous integration (CI) testing cycles. |
| **Avg. Cost Per Task** | $0.12 | Includes Foreman generation, Agent execution, and Judge grading. |
| **Weekly API Budget** | $60.00 | Based on current token pricing for Tier-1 models. |
| **Monthly OPEX** | **$240.00** | Sustained operational cost for 2,000+ dynamic evaluations. |

### 6.3 Cost-Benefit Analysis
*   **Cost of Inaction:** Organizations currently face a **15-20% annual decay** in benchmark validity due to data contamination [5].
*   **Efficiency Gains:** Implementing a Foreman-Agent architecture has shown a **22% reduction in compute costs** by triaging tasks to the appropriate model size [10].
*   **Human Labor Savings:** Replacing a $50 human task with a $0.12 automated probe represents a **99.7% cost reduction per unit.**
*   **Break-Even Point:** Analysis suggests the project pays for itself within the first 150 automated tasks by replacing manual QA hours.

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## Risk Analysis and Alternatives Considered
### 5. RISK ANALYSIS AND ALTERNATIVES CONSIDERED

#### 5.1 RISKS OF PROCEEDING
*   **Model Autonomy/Safety (High):** Automated probe generation could create "jailbreak" scenarios. *Mitigation:* Strict Dockerized sandboxing.
*   **Data Contamination (Medium):** Probe tasks must be cycled to avoid leakage into future training sets [5].
*   **Competitive Risk:** While **Weights & Biases** [7] and **Ariadne AI** [6] are incumbents, they lack the specific "Foreman" delegation logic required for agentic workflows. Failing to launch cedes the 42% CAGR market [4] to these providers.

#### 5.2 ALTERNATIVES CONSIDERED
*   **A. New Template in Existing Company:** Rejected because existing subsidiaries lack the sandboxing infrastructure required for code-execution probes.
*   **B. One-time Manual Report:** Rejected; static benchmarks lose 20% validity annually [5].
*   **C. Wait:** Rejected due to explosive growth in the $15.8B AI governance market [1].

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## Proposed Company Specification
1. **COMPANY RECORD**
   - **company_id**: TBD
   - **name**: Foreman Probe
   - **slug**: foreman_probe
   - **parent_company**: crimson_leaf
   - **mission**: To design, execute, and analyze frontier model benchmarks that stress-test LLM reasoning, instruction following, and agentic workflows.
   - **type**: research
   - **status**: active

2. **PROPOSED AGENTS**
   - **The Architect (Orion)**: Design complex logic puzzles and code-interpreting tasks. (Claude 3.5 Sonnet)
   - **The Proctor (Silas)**: Execute probes across multiple model endpoints and log raw outputs. (GPT-4o)
   - **The Critic (Vesper)**: Evaluation specialist identifying reasoning flaws and hallucinations. (o1-preview)

3. **PROPOSED TEMPLATES**
   - **probe_design**: Identification of target capability and gold-standard path generation.
   - **probe_execution**: Batch API processing and log normalization.
   - **results_analysis**: Scoring outputs and generating "Red Flag" performance reports.

4. **90-DAY SUCCESS CRITERIA**
   - At least 10 distinct "Foreman Probes" completed.
   - Benchmarking of 5 major LLM families.
   - Evidence of a "Reasoning Delta" caught by proprietary dynamic probes that static benchmarks missed.

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## Signature Block
Edgar Chen certifies this proposal meets Crimson Leaf Holdings governance requirements:
- No existing subsidiary duplicates this charter
- No existing template or tool can solve this gap
- No proposal for this company has been submitted in the last 30 days
- A full business plan with 5-source web research and inline citations is provided

This proposal requires David Baity's explicit approval before any action is taken.