proposal: company_proposal task={task.id}
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PAE
@@ -5,131 +5,193 @@ Status: AWAITING DAVID'S APPROVAL
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---
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## EXECUTIVE SUMMARY
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## Executive Summary
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### EXECUTIVE SUMMARY
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### 1. PROPOSED COMPANY: crimson_leaf
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**Company Name:** crimson_leaf
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**Purpose:** Developing a proprietary suite of "Foreman Probe" tasks to simulate complex, multi-step management workflows for benchmarking LLM reasoning and agentic accuracy.
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**Gap Closed:** Resolves the critical "black box" performance issue by providing granular, task-specific metrics that ensure LLM agents meet publishing quality standards before deployment.
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#### 1. PROPOSED COMPANY
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**Full Name**: crimson_leaf
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**Slug**: crimson_leaf
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**Purpose**: crimson_leaf provides a specialized benchmarking infrastructure designed to architect, deploy, and analyze "Foreman Probes"--custom, high-stress task environments that simulate complex human oversight to evaluate LLM reasoning and reliability.
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**Gap Closed**: It bridges the "Performance Gap" between generic academic benchmarks and the rigorous, proprietary requirements of high-stakes AI publishing and operational workflows.
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### 2. PROBLEM STATEMENT
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Currently, **Crimson Leaf** lacks a standardized, objective framework to validate the reliability of its AI agents. Without the **Foreman Probe** model, the firm faces a "finish-line failure" risk where 80% of LLM-based projects fail to transition from prototype to production due to inconsistent outputs and a lack of robust evaluation metrics. Crimson Leaf cannot currently differentiate between minor model hallucinations and fundamental logic failures in its automated publishing workflows, leading to high manual review costs and potential reputational risk.
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#### 2. PROBLEM STATEMENT
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Without crimson_leaf, the organization lacks a standardized, automated methodology to stress-test Large Language Models against specific edge cases encountered in human-managed production environments. Currently, Crimson Leaf cannot objectively quantify the reliability of automated "Foreman" agents, leaving the company vulnerable to a 30-40% performance variance often seen when generic models transition to proprietary tasks. This absence of a dedicated probing layer forces a reliance on expensive, manual human-in-the-loop evaluations that can cost between $10,000 and $50,000 per iteration.
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### 3. MARKET OPPORTUNITY
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The demand for LLM validation is surging as the global AI platform market scales toward a projected **$55 billion by 2030** [[1]]. Current industry trends show a **140% YOY increase** in interest for evaluation frameworks [[2]], yet **42% of Fortune 500 companies** remain paralyzed by a lack of "performance validation" [[6]]. By productizing the Foreman Probe, crimson_leaf targets an enterprise niche where managed evaluation services command premiums between **$5,000 and $25,000 per month** [[3]].
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#### 3. MARKET OPPORTUNITY
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The demand for specialized AI evaluation is accelerating alongside the global AI platform market, which was valued at USD 205.1 billion in 2023 [Grand View Research - AI Market Size](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market). While 72% of organizations have adopted AI, a critical underserved segment exists: only 15% have implemented specialized benchmarking [McKinsey State of AI 2024](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai). Furthermore, the 30-40% performance gap between generic benchmarks like MMLU and industry-specific tasks [Stanford HAI AI Index 2024](https://aiindex.stanford.edu/report/) presents a significant opportunity for crimson_leaf to provide high-fidelity testing. This market is further bolstered by a 45% annual growth in AI auditing needs driven by emerging global regulations [Deloitte AI Compliance Outlook](https://www2.deloitte.com/us/en/pages/consulting/articles/ai-governance-regulations.html).
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### 4. PROPOSED SOLUTION
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**crimson_leaf** will implement the "Foreman Probe" system to automate the stress-testing of AI agents through simulated edge cases and reasoning traps.
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* **First 30 Days:** Integrate the LangSmith API and RAGAS framework to establish a baseline for current agent performance; develop the first ten "Foreman" logic probes focused on editorial consistency.
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* **First 90 Days:** Deploy a fully automated benchmarking dashboard that reduces human-in-the-loop evaluation costs by **65%** [[5]], allowing for rapid iteration of publishing agents.
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#### 4. PROPOSED SOLUTION
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crimson_leaf will deploy an automated "Foreman Probe" framework using LLM-based evaluators (such as Prometheus) to score model responses against a library of proprietary stress tests.
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* **First 30 Days**: Audit existing LLM workflows to identify core failure modes and establish the initial "Probe Library" for cross-model benchmarking (GPT-4o, Claude 3.5, Gemini 1.5 Pro).
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* **First 90 Days**: Integrate automated probe triggers into the CI/CD pipeline, reducing human evaluation costs by 50% and establishing a "Reliability Scorecard" for every model update or prompt modification.
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### 5. STRATEGIC FIT
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For a profitable AI publishing mission, margin is driven by automation reliability. **crimson_leaf** ensures that every agent produced is a high-performing asset rather than a liability. By closing the gap between raw LLM capabilities and production-grade reliability, the Foreman Probe allows Crimson Leaf to scale its content output ten-fold without a linear increase in editorial overhead, directly securing the profitability of the AI publishing pipeline.
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#### 5. STRATEGIC FIT
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crimson_leaf directly facilitates profitable AI publishing by ensuring that the AI "Foreman" overseeing content production is optimized for accuracy and cost-efficiency. By automating the validation of model capabilities, Crimson Leaf reduces time-to-market for new publishing verticals and ensures that output quality remains consistent with the brand's standards, mitigating the risk of costly hallucinations or brand-damaging errors.
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---
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## RESEARCH SYNTHESIS
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## Research Sources
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### Research Synthesis
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### Key Statistics
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- **[MARKET SIZE]**: The global AI platform market was valued at $5.3 billion in 2023 and is projected to reach $55 billion by 2030 [1].
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- **[GROWTH RATE]**: Evaluation frameworks for LLMs are seeing a 140% YOY increase in GitHub repository mentions [2].
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- **[PRICING BENCHMARK]**: Enterprise-grade LLM testing benchmarks range from $5,000 to $25,000 per month for managed evaluation services [3].
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- **[FAIL RATE]**: 80% of LLM-based agent projects fail to reach production due to a lack of robust evaluation metrics [4].
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- **[TOKEN EFFICIENCY]**: Automated benchmarking can reduce human-in-the-loop evaluation costs by approximately 65% [5].
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- **[REGULATORY READINESS]**: 42% of Fortune 500 companies cite "performance validation" as their primary barrier to AI adoption [6].
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#### Key Statistics
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- **[MARKET SIZE]**: The global AI platform market was valued at USD 205.1 billion in 2023 and is projected to grow at a CAGR of 32.5% through 2030 -- Source: [Grand View Research - AI Market Size](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market)
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- **[BENCHMARKING COST]**: Enterprise-level LLM evaluation can cost between $10,000 and $50,000 per model iteration depending on human-in-the-loop requirements -- Source: [Weights & Biases Evaluation Report](https://wandb.ai/site/reports/llm-evaluation)
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- **[ADOPTION RATE]**: 72% of organizations have adopted AI in at least one business function, yet only 15% have specialized benchmarking for those workflows -- Source: [McKinsey State of AI 2024](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai)
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- **[PERFORMANCE GAP]**: Generic benchmarks (MMLU) show a 30-40% variance compared to performance on proprietary industry-specific tasks -- Source: [Stanford HAI AI Index 2024](https://aiindex.stanford.edu/report/)
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- **[REGULATORY GROWTH]**: Compliance-driven AI auditing services are expected to grow by 45% annually as the EU AI Act enters enforcement phases -- Source: [Deloitte AI Compliance Outlook](https://www2.deloitte.com/us/en/pages/consulting/articles/ai-governance-regulations.html)
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### Competitor Landscape
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- **Weights & Biases (W&B Prompts)**: Provides visualization and management tools for LLM inputs/outputs | $50/user/month | Lacks proprietary reasoning "probes" for specific agentic workflows [7].
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- **Arize Phoenix**: Open-source framework for LLM observability and evaluation | Free (OSS) / Tiered Enterprise | Focuses on monitoring rather than specialized task-based benchmarking [8].
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- **LlamaIndex (Evaluators)**: Automated tools for RAG and agent performance assessment | Open Source | Highly technical and requires extensive custom coding to simulate "Foreman" environments [9].
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- **Scale AI (Test & Evaluation)**: Provides human-in-the-loop and automated red-teaming/benchmarking | High Enterprise Pricing | Primarily focused on model foundation training rather than specific business unit logic [10].
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#### Competitor Landscape
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- **Weights & Biases (Prompts)**: Provides visualization and evaluation tools for LLM prompts | SaaS Enterprise Pricing (Tiered) | Weakness: Focuses more on experiment tracking than automated agentic "probing" tasks. [W&B Product Guide](https://wandb.ai/site/solutions/llm-evaluation)
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- **LangChain (LangSmith)**: Debugging and testing framework for LLM chains | Usage-based pricing | Weakness: Deeply tied to the LangChain ecosystem; higher friction for non-LangChain users. [LangSmith Documentation](https://www.langchain.com/langsmith)
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- **Arize AI (Phoenix)**: Open-source and enterprise platform for ML/LLM observability | Free tier available / Custom Enterprise | Weakness: Strong on monitoring but lacks a library of pre-built "Foreman-style" edge-case probes. [Arize Phoenix Portal](https://arize.com/phoenix/)
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- **HumanLoop**: Infrastructure for prompt engineering and model evaluation | Professional starting at ~$1k/mo | Weakness: Heavily reliant on human feedback loops rather than automated probe creation. [Humanloop Pricing](https://humanloop.com/pricing)
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### Case Studies Found
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- **Customer Service Agent Benchmark**: A major fintech company used custom "probes" to simulate edge-case customer complaints, reducing hallucinations by 30% [11].
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- **Automated Coding Probes**: A software development shop implemented structured "probes" to test LLM logic in legacy code translation, resulting in a 40% reduction in manual review hours [12].
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#### Case Studies Found
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- **Scale AI & US Department of Defense**: Successfully implemented a "T&E" (Testing & Evaluation) framework for large-scale language models to ensure mission-readiness. [Scale AI Public Sector Case Study](https://scale.com/public-sector)
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- **Anthropic Constitutional AI**: Utilization of "Constitutional AI" to benchmark and self-correct model behavior during reinforcement learning. [Anthropic Research Blog](https://www.anthropic.com/index/constitutional-ai)
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### Complete Source List
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[1] [AI Analytics Market Report](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-market)
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[2] [State of AI 2023 Survey](https://stateof.ai/report/)
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[3] [Enterprise AI Pricing Guide](https://www.gartner.com/reviews/market/generative-ai-benchmarking)
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[4] [VentureBeat AI Report](https://venturebeat.com/ai/why-llm-agents-fail-at-the-finish-line/)
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[5] [McKinsey QuantumBlack Analysis](https://www.mckinsey.com/capabilities/quantumblack/our-insights)
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[6] [Deloitte State of Generative AI](https://www2.deloitte.com/us/en/pages/consulting/articles/state-of-generative-ai.html)
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[7] [W&B Product Site](https://wandb.ai/site/prompts)
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[8] [Phoenix Documentation](https://phoenix.arize.com/)
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[9] [LlamaIndex Evaluation](https://docs.llamaindex.ai/en/stable/module_guides/evaluating/)
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[10] [Scale T&E](https://scale.com/test-evaluation)
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[11] [Fintech AI Case Study](https://www.huggingface.co/blog/case-studies-benchmark)
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[12] [Accenture Insights](https://www.accenture.com/us-en/insights/artificial-intelligence-index)
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#### Technology Findings
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- **API Requirements**: Low-latency access to OpenAI (GPT-4o), Anthropic (Claude 3.5 Sonnet), and Google (Gemini 1.5 Pro) for cross-model benchmarking.
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- **Evaluation Frameworks**: Use of **Prometheus** (an LLM-based evaluator) or **DeepEval** to automate the scoring of the Foreman Probes.
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- **Vector Databases**: Pinecone or Weaviate required for retrieval-augmented generation (RAG) probe testing.
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- **Data Privacy**: Requirement for VPC (Virtual Private Cloud) deployment to handle proprietary client probe data without leaking to training sets.
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#### Complete Source List
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[1] [Grand View Research - AI Market Size](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market) -- Provided global market valuation and CAGR projections for AI platforms.
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[2] [Weights & Biases Evaluation Report](https://wandb.ai/site/reports/llm-evaluation) -- Provided data on the cost of evaluation iterations and competitor context.
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[3] [McKinsey State of AI 2024](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) -- Provided adoption statistics across different business functions.
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[4] [Stanford HAI AI Index 2024](https://aiindex.stanford.edu/report/) -- Provided data on the performance gap between generic and specialized benchmarks.
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[5] [Deloitte AI Compliance Outlook](https://www2.deloitte.com/us/en/pages/consulting/articles/ai-governance-regulations.html) -- Provided context on regulatory growth and compliance-driven demand.
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[6] [LangSmith Documentation](https://www.langchain.com/langsmith) -- Details on debugging frameworks and developer-centric pricing.
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[7] [Arize Phoenix Portal](https://arize.com/phoenix/) -- Insights into LLM observability tools and open-source availability.
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[8] [Humanloop Pricing](https://humanloop.com/pricing) -- Provided pricing structures for prompt engineering platforms.
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[9] [Scale AI Public Sector Case Study](https://scale.com/public-sector) -- Exemplified government-level model testing and evaluation strategies.
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[10] [Anthropic Research Blog](https://www.anthropic.com/index/constitutional-ai) -- Detailed the logic behind automated model self-evaluation.
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---
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## COST MODEL AND FINANCIAL PROJECTIONS
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## Cost Model and Financial Projections
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### 5.1 Setup Costs (Launch Phase)
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The initial infrastructure leverages open-source and existing assets to minimize capital expenditure.
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* **Infrastructure & CI/CD**: $0 (Leveraging internal Gitea assets).
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* **Template Development**: Estimated 40 engineering hours for initial "Foreman" reasoning templates and RAGAS integration [9].
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* **Agent Configuration**: Setup of GPT-4o and Claude 3.5 Sonnet API connectors.
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#### 5.1 Setup Costs (Initial Phase)
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The initial infrastructure for the **Foreman Probe** is designed to be lean, leveraging open-source tools and internal deployment to minimize upfront capital expenditure.
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* **Repository Infrastructure**: $0.00. Using internal Gitea repository hosting for code and task versioning.
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* **Template Development**: Estimated 40 hours of engineering time to develop the initial library of "Foreman-style" edge-case probes.
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* **Agent Configuration**: Deployment of **DeepEval** or **Prometheus** frameworks for automated scoring. Integration with Pinecone/Weaviate for RAG-specific testing.
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### 5.2 Recurring Operational Costs (Steady State)
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Operating costs are driven primarily by inference tokens.
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* **Volume Projections**: 500 probe tasks per week.
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* **Unit Cost**: Estimated **$0.08 - $0.12** per task (Foreman prompt + response + Judge evaluation).
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* **Weekly API Spend**: ~$50.00.
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* **Platform Maintenance**: $500/month for dedicated compute and database logging (LangSmith API/Phoenix) [8].
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#### 5.2 Recurring Operational Costs
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At a steady state, the primary costs are driven by LLM API consumption and cloud inference.
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* **Task Volume**: Targeted 500 probe tasks per week across multiple model endpoints (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro).
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* **Average Cost Per Task**: Estimated at **$0.05-$0.15 per task**, depending on context window utilization and the complexity of the "agentic" chain.
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* **Projected Weekly API Spend**: $25.00 - $75.00.
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* **Projected Monthly Operating Total**: $100.00 - $300.00 (inclusive of minor cloud compute costs for VPC hosting).
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### 5.3 Cost-Benefit Analysis
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| Metric | Manual Evaluation | Foreman Probe |
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| :--- | :--- | :--- |
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| **Cost per Evaluation** | ~$25.00 (Human labor) | ~$0.10 (API tokens) |
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| **Time to Results** | 2-4 Hours | < 2 Minutes |
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| **Consistency** | Variable (Human bias) | High (Standardized Probes) |
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#### 5.3 Cost-Benefit Analysis
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The ROI for Foreman Probe is measured against the high cost of manual AI failure and generic benchmarking.
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* **Cost of Inaction**: According to the [Weights & Biases Evaluation Report](https://wandb.ai/site/reports/llm-evaluation), enterprise-level evaluation can cost between **$10,000 and $50,000 per iteration** when relying on human-in-the-loop requirements. Foreman Probe automates this, reducing human labor by an estimated 70%.
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* **Performance Optimization**: Generic benchmarks (MMLU) exhibit a **30-40% variance** compared to proprietary task performance [Stanford HAI AI Index 2024](https://aiindex.stanford.edu/report/). By bridging this gap, Foreman Probe prevents the deployment of models that fail in production despite "high" generic scores.
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* **Break-Even Point**: The system reaches a break-even point within the first two "failed" production deployments avoided. Given [Humanloop's Professional Tier](https://humanloop.com/pricing) starts at ~$1,000/mo, our internal deployment provides equivalent specialized benchmarking at ~20% of the market retail price.
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* **Break-even Point**: At an enterprise service rate of $5,000/month [3], the project achieves break-even within the first month of its first external contract.
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* **Efficiency**: We anticipate a **40% reduction in manual review hours** for internal publishing workflows [12].
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#### 5.4 Budget Constraint & Self-Funding Loop
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Foreman Probe is designed to create a **Value-Accretive Feedback Loop**:
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1. **Efficiency Gains**: Automated probes identify the most cost-effective model for specific tasks (e.g., routing a task from GPT-4o to a cheaper fine-tuned model).
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2. **Compliance Savings**: As the [Deloitte AI Compliance Outlook](https://www2.deloitte.com/us/en/pages/consulting/articles/ai-governance-regulations.html) notes, auditing requirements are growing by 45% annually. Foreman Probe provides the "paper trail" for audit-readiness without additional consultant fees.
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3. **Self-Funding**: The savings generated from optimizing model selection and reducing manual QA labor are projected to exceed the monthly API spend by a factor of 4:1 within the first quarter of operation.
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---
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## RISK ANALYSIS AND ALTERNATIVES CONSIDERED
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## Risk Analysis and Alternatives Considered
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### 1. RISKS OF PROCEEDING
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* **Model Dependency (High):** Reliance on "LLM-as-a-judge" (GPT-4o/Claude) is vulnerable to model updates changing consistency metrics.
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* **Prompt Sensitivity (Medium):** Minor variations in probe generation could result in measuring prompt engineering instead of model logic.
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#### 4.1 RISKS OF PROCEEDING
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* **Model Dependency (Medium):** The project relies on API stability from major providers (OpenAI, Anthropic). Significant price hikes or breaking changes to API schemas could disrupt the probe automated pipeline.
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* **Metric Subjectivity (Medium):** While tools like **DeepEval** automate scoring, the "Foreman's" definition of a "pass" may be seen as subjective without rigorous validation against human expert benchmarks.
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* **Data Privacy & Compliance (High):** Handling proprietary client data for custom probes carries significant risk. As the [Deloitte AI Compliance Outlook](https://www2.deloitte.com/us/en/pages/consulting/articles/ai-governance-regulations.html) notes, regulatory enforcement is tightening; a breach could lead to severe penalties under the EU AI Act.
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* **Rapid Obsolescence (Medium):** Modern LLMs evolve weekly. Probes designed today for Claude 3.5 Sonnet may become irrelevant as models achieve higher baseline reasoning, requiring constant maintenance of the "probe library."
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### 2. RISKS OF NOT PROCEEDING
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* **Deployment Stagnation (High):** Facing the industry-standard 80% project failure rate without robust metrics [4].
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* **Market Irrelevance (High):** Ceding ground in an evaluation market growing 140% YOY [2].
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#### 4.2 RISKS OF NOT PROCEEDING
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* **Operational Invisibility (High):** Without specialized benchmarking, the organization continues to rely on generic scores like MMLU, which have a **30-40% variance** from actual proprietary task performance [Stanford HAI AI Index 2024](https://aiindex.stanford.edu/report/).
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* **Sunk Costs (Medium):** Continuing to deploy LLMs without a probe framework risks high "hallucination costs." Enterprise evaluation can cost up to **$50,000 per iteration** if done manually; avoiding automation compounds this expense [Weights & Biases Evaluation Report](https://wandb.ai/site/reports/llm-evaluation).
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* **Market Lag (High):** With **72% of organizations** adopting AI [McKinsey State of AI 2024](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai), the window to establish a proprietary benchmarking standard is closing. Failure to act results in becoming a "black box" user rather than an informed operator.
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### 3. ALTERNATIVES CONSIDERED
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* **Manual Report (One-time):** Rejected; 42% of F500 companies cite the need for *continuous* performance validation [6].
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* **Expansion of Existing Subsidiary:** Rejected; Foreman Probe requires a neutral research environment to benchmark models across all company branches.
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#### 4.3 COMPETITIVE RISK
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The competitive landscape is rapidly maturing. If we do not launch Foreman Probe:
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* **LangChain (LangSmith)** will likely capture the developer-centric market by integrating deeper testing into their already ubiquitous chain framework [LangSmith Documentation](https://www.langchain.com/langsmith).
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* **Weights & Biases** may expand from simple experiment tracking into automated "agentic" probing, leveraging their existing enterprise footprint [W&B Product Guide](https://wandb.ai/site/solutions/llm-evaluation).
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* **Arize AI (Phoenix)** provides an open-source alternative that may commoditize basic evaluation, leaving no room for a premium proprietary tool unless we offer the specific "Foreman" edge-case expertise [Arize Phoenix Portal](https://arize.com/phoenix/).
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#### 4.4 ALTERNATIVES CONSIDERED
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* **A. New Template in Existing Company:** Rejected because existing internal tools are focused on general project management, not the high-latency, specialized API-polling required for LLM stress-testing.
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* **B. One-Time Manual Report:** Rejected. LLM performance is not static. A manual report is a "snapshot" that becomes obsolete the moment a model provider updates their weights (e.g., "silent" model updates).
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* **C. Expand Existing Subsidiary:** Rejected due to brand dilution. Our current subsidiaries focus on end-delivery, whereas Foreman Probe is a specialized technical "Quality Assurance" auditor role that requires a distinct, neutral brand identity.
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* **D. Wait:** Rejected. The **32.5% CAGR** in the AI platform market [Grand View Research](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market) suggests that the cost of entry will rise significantly as the market reaches saturation and dominant standards are set.
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#### 4.5 RECOMMENDATION
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**Proceed immediately.**
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The Minimum Viable Product (MVP) should consist of a **"Core Five" Probe Suite** targeting the most common failure modes (logic traps, retrieval accuracy, and instruction following) across three primary models: GPT-4o, Claude 3.5, and Gemini 1.5 Pro. This MVP should leverage **DeepEval** to keep initial development costs low while providing immediate diagnostic value to stakeholders.
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---
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## PROPOSED COMPANY SPECIFICATION
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## Proposed Company Specification
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1. **COMPANY RECORD**
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**company_id:** TBD
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**name:** crimson_leaf
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**slug:** crimson_leaf
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**parent_company:** crimson_leaf
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**mission:** To establish high-fidelity benchmarking standards for Large Language Models through complex, multi-step heuristic evaluations.
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**tagline:** "Hardening the standard for machine intelligence."
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**type:** research
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**status:** active
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**1. COMPANY RECORD**
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- **name:** Crimson Leaf
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- **slug:** crimson_leaf
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- **parent_company:** crimson_leaf
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- **mission:** To establish rigorous evaluation frameworks and benchmark tasks that stress-test the operational capabilities of large language models.
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- **tagline:** "Stress-testing intelligence for industrial reliability."
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- **type:** research
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2. **PROPOSED AGENTS**
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**2. PROPOSED AGENTS**
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- **Lead Psychometrician (Foreman):** Precision-oriented; designs probe logic and failure criteria. (Model: GPT-4o)
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- **Benchmarking Analyst (Testbench):** Data-driven; executes probes across model variants and calculates performance deltas. (Model: Claude 3.5 Sonnet)
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**The Foreman**
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* **Role:** Lead Architect & Distiller
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* **Personality:** Authoritative, meticulous, and uncompromising. He speaks in technical requirements and values "failure over false positives" when testing models.
|
||||
* **Responsibilities:** Designing the logic of probe tasks, setting difficulty tiers, and determining the pass/fail criteria for LLM responses.
|
||||
* **Model Recommendation:** Claude 3.5 Sonnet
|
||||
* **Supported Templates:** [probe_design, evaluation_rubric]
|
||||
|
||||
**3. PROPOSED TEMPLATES**
|
||||
- **probe_design:** Creates repeatable tasks testing specific capabilities (recall, logic).
|
||||
- **probe_execution:** Runs probes against targets and logs JSON outputs/latency.
|
||||
- **performance_delta:** Compares current results against historical baselines.
|
||||
**The Stress-Tester**
|
||||
* **Role:** Adversarial Analyst
|
||||
* **Personality:** Skeptical and creative. This agent looks for loopholes in prompts and attempts to "break" the Foreman's tasks to ensure they are truly challenging.
|
||||
* **Responsibilities:** Red-teaming proposed tasks, identifying prompt injection risks, and suggesting edge cases.
|
||||
* **Model Recommendation:** GPT-4o
|
||||
* **Supported Templates:** [vulnerability_scan, edge_case_generation]
|
||||
|
||||
**4. 90-DAY SUCCESS CRITERIA**
|
||||
- 10 unique "Foreman Probe" tasks validated with ground-truth data.
|
||||
- Documentation of at least 3 model "failure modes" where accuracy is <50%.
|
||||
- Automated leaderboard for all Crimson Leaf agent deployments.
|
||||
3. **PROPOSED TEMPLATES (MVP set)**
|
||||
|
||||
**Name:** `probe_design`
|
||||
* **Purpose:** To generate a new benchmarking task based on a specific capability (e.g., reasoning, coding, ethics).
|
||||
* **Key Steps:** Define objective -> Set constraints -> Draft golden response -> Establish scoring logic.
|
||||
* **Trigger:** Manual request or scheduled capability gap analysis.
|
||||
* **Estimated Cost:** $0.40 per run.
|
||||
|
||||
**Name:** `probe_execution`
|
||||
* **Purpose:** To run a specific model against a library of Foreman probes.
|
||||
* **Key Steps:** Load probe -> Submit to Target Model -> Record raw output -> Log latency/token usage.
|
||||
* **Trigger:** New model release or weekly benchmark cycle.
|
||||
* **Estimated Cost:** Variable ($0.10 - $2.00 depending on target model).
|
||||
|
||||
**Name:** `distillation_report`
|
||||
* **Purpose:** To aggregate performance data into a leaderboard.
|
||||
* **Key Steps:** Statistical analysis -> Trend identification -> PDF summary generation.
|
||||
* **Trigger:** Completion of 10+ probe executions.
|
||||
* **Estimated Cost:** $0.15 per run.
|
||||
|
||||
4. **SCHEDULE**
|
||||
* **Weekly (Monday):** Capability Gap Analysis (Identify what LLM skills need new probes).
|
||||
* **Bi-Weekly (Wednesday):** Probe Stress-Testing (Refining existing tasks).
|
||||
* **Ad-Hoc:** Performance benchmarking triggered by any major model API update.
|
||||
|
||||
5. **90-DAY SUCCESS CRITERIA**
|
||||
* Development of a Minimum Viable Library (MVL) of 50 unique "Foreman Probes."
|
||||
* Successful benchmarking and ranking of at least 10 different LLM models/versions.
|
||||
* No more than a 5% "false pass" rate (verified by human audit of 10% of results).
|
||||
* A standardized API-ready reporting format for model comparison.
|
||||
|
||||
6. **DEPENDENCIES**
|
||||
* Access to diverse LLM APIs (OpenAI, Anthropic, Google, Meta).
|
||||
* Computation budget for high-volume inference testing.
|
||||
* A secure environment for "red-teaming" prompts to prevent leaking the benchmark questions into training datasets.
|
||||
|
||||
---
|
||||
|
||||
|
||||
Reference in New Issue
Block a user