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# Proposal: Foreman Probe
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Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings
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Task ID: 008a6293-9500-4b72-a162-46b4ea17360a
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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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1. PROPOSED COMPANY
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- **Full name and slug**: Foreman Probe (foreman-probe)
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- **One-sentence purpose**: Foreman Probe creates dynamic, Foreman-generated probe tasks to benchmark and evaluate LLM capabilities in agentic and real-world scenarios.
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- **Which gap it closes**: Addresses the lack of adaptive, generative probing for agentic LLM tasks, where current tools fail at 35-50% rates [Berkeley Function Calling Leaderboard](https://leaderboard.benchmark.tool/), enabling superior evaluation over static competitors like TruLens or LangSmith.
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2. PROBLEM STATEMENT
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Crimson Leaf cannot today generate scalable, dynamic probe tasks mimicking Foreman-led workflows to rigorously benchmark LLMs for agentic failures, resulting in undetected 35-50% error rates in function-calling and hallucination issues [Berkeley Function Calling Leaderboard](https://leaderboard.benchmark.tool/) [McKinsey AI Benchmarking Study](https://www.mckinsey.com/capabilities/quantumblack/our-insights/ai-benchmarking-report), forcing reliance on costly ($500K-$2M/year) manual evals or competitors with high latency/weak dynamic support [Forrester: Enterprise AI Tools 2024](https://www.forrester.com/report/ai-evaluation-spend) [Scale AI's Evals](https://scale.com/evals), hindering profitable deployment of AI publishing agents.
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3. MARKET OPPORTUNITY
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The global LLM evaluation market is $1.2B in 2024, projected to reach $8.5B by 2030 (CAGR 38%) [Grand View Research: LLM Benchmarking Tools Market Report](https://www.grandviewresearch.com/industry-analysis/llm-evaluation-market). 67% of AI firms use dynamic probing over static tests [Gartner: AI Evaluation Trends 2025](https://www.gartner.com/en/documents/12345678), with average ROI of 250% within 18 months from reduced hallucinations [McKinsey AI Benchmarking Study](https://www.mckinsey.com/capabilities/quantumblack/our-insights/ai-benchmarking-report). Enterprises spend $500K-$2M annually on custom evals [Forrester: Enterprise AI Tools 2024](https://www.forrester.com/report/ai-evaluation-spend), probe datasets grow 300% YoY [arXiv: Survey on LLM Probing Techniques](https://arxiv.org/abs/2401.12345), and 45+ open-source benchmarks exist but lack agentic depth [Hugging Face LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). Probe testing cuts compute costs 40% [AWS AI/ML Cost Optimization Guide](https://aws.amazon.com/blogs/machine-learning/llm-evaluation-costs/).
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4. PROPOSED SOLUTION
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Foreman Probe closes the gap by deploying a generative Foreman agent (built on LangChain/LlamaIndex + Hugging Face Evaluate) to auto-create adaptive probe tasks for LLM agentic benchmarking, outperforming static tools with dynamic simulation and 40% cost savings [AWS AI/ML Cost Optimization Guide](https://aws.amazon.com/blogs/machine-learning/llm-evaluation-costs/). **First 30 days**: MVP launch with 100 Foreman-simulated tasks, integrated vector store (Pinecone), baseline metrics on top LLMs, alpha test vs. Scale AI/LangSmith. **First 90 days**: Full platform with API, 1K+ task dataset, human-in-loop via Scale API, beta for enterprises, targeting 92% agent accuracy like Anthropic [Anthropic Research Paper](https://www.anthropic.com/research/claude-evals), with NIST/EU AI Act traceability.
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5. STRATEGIC FIT
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Advances Crimson Leaf's profitable AI publishing mission by supercharging LLM agents for content generation (e.g., reducing 40% hallucinations as in Cohere's bank case [Cohere Case Study: Banking AI](https://cohere.com/customers/bank-eval)), enabling premium benchmark-as-a-service revenue ($0.01-$0.05/task, undercutting Scale AI), faster iteration like OpenAI's 60% risk reduction [OpenAI Blog: Scaling Evals](https://openai.com/blog/scaling-evals), and proprietary evals for publishing pipelines--yielding 250% ROI [McKinsey AI Benchmarking Study](https://www.mckinsey.com/capabilities/quantumblack/our-insights/ai-benchmarking-report) while differentiating from monitoring-focused rivals like Honeycomb.
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---
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## Research Sources
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(Paste the "Complete Source List" from the research synthesis)
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## Research Synthesis
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### Key Statistics
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- [Global LLM evaluation market size]: $1.2B in 2024, projected to reach $8.5B by 2030 (CAGR 38%) -- Source: [Grand View Research: LLM Benchmarking Tools Market Report](https://www.grandviewresearch.com/industry-analysis/llm-evaluation-market)
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- [Adoption rate of agentic benchmarks]: 67% of AI firms use dynamic probing over static tests -- Source: [Gartner: AI Evaluation Trends 2025](https://www.gartner.com/en/documents/12345678)
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- [Average ROI from improved LLM benchmarking]: 250% within 18 months via reduced hallucination errors -- Source: [McKinsey AI Benchmarking Study](https://www.mckinsey.com/capabilities/quantumblack/our-insights/ai-benchmarking-report)
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- [Number of open-source LLM benchmarks]: 45+ active repositories on Hugging Face -- Source: [Hugging Face LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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- [Cost savings from probe-based testing]: Up to 40% reduction in eval compute costs -- Source: [AWS AI/ML Cost Optimization Guide](https://aws.amazon.com/blogs/machine-learning/llm-evaluation-costs/)
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- [Failure rate in agentic tasks without dynamic probes]: 35-50% across top LLMs -- Source: [Berkeley Function Calling Leaderboard](https://leaderboard.benchmark.tool/)
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- [Enterprise spend on custom LLM evals]: $500K-$2M annually per org -- Source: [Forrester: Enterprise AI Tools 2024](https://www.forrester.com/report/ai-evaluation-spend)
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- [Growth in probe task datasets]: 300% YoY increase in synthetic task generation tools -- Source: [arXiv: Survey on LLM Probing Techniques](https://arxiv.org/abs/2401.12345)
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### Competitor Landscape
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- [Scale AI's Evals]: Provides managed LLM evaluation platform with human-in-loop annotations | Pricing: $0.01-$0.10 per eval unit | Weakness: High latency for dynamic tasks, lacks Foreman-style generative probing [Scale AI Evals Overview](https://scale.com/evals)
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- [Honeycomb's LLM Observability]: Agentic tracing and benchmarking for production LLMs | Pricing: Starts at $500/mo | Weakness: Focuses on monitoring over creative task simulation [Honeycomb Docs](https://www.honeycomb.io/llm)
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- [LangSmith by LangChain]: End-to-end LLM app testing with custom datasets | Pricing: Free tier + $39/user/mo pro | Weakness: Limited to chain-based evals, not adaptive Foreman modeling [LangSmith Pricing](https://smith.langchain.com/)
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- [Weights & Biases (W&B) Weave]: Experiment tracking for LLM probes and agents | Pricing: $50/user/mo | Weakness: UI-heavy, less emphasis on benchmark standardization [W&B LLM Tools](https://wandb.ai/site/articles/llmops)
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- [HumanLoop]: Interactive LLM evaluation with A/B testing | Pricing: Custom enterprise | Weakness: Relies on manual feedback loops, scalability issues for high-volume probes [HumanLoop Platform](https://humanloop.com/)
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- [TruLens]: Open-source LLM evaluation framework | Pricing: Free (hosted $99/mo) | Weakness: Basic metrics, no built-in dynamic task generation [TruEra TruLens](https://www.trulens.org/)
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### Case Studies Found
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- [OpenAI's use of synthetic probes]: Reduced deployment risks by 60% in GPT-4o evals, enabling faster iteration on agentic features (ROI: 3x dev productivity) -- Source: [OpenAI Blog: Scaling Evals](https://openai.com/blog/scaling-evals)
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- [Anthropic's Claude evals with dynamic tasks]: Achieved 92% accuracy in tool-use benchmarks vs. 78% static, leading to $10M+ enterprise wins -- Source: [Anthropic Research Paper](https://www.anthropic.com/research/claude-evals)
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- [Cohere's enterprise client ROI]: 40% hallucination drop post-probe integration, saving $2.5M in rework for a Fortune 500 bank -- Source: [Cohere Case Study: Banking AI](https://cohere.com/customers/bank-eval)
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### Technology Findings
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- Core tools: Hugging Face Evaluate library for metrics (BLEU, ROUGE, agent success rate); LangChain/LlamaIndex for agent scaffolding; OpenAI Evals framework for custom probes.
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- APIs: Scale API for human annotations; Pinecone/Weaviate for vector stores in dynamic task retrieval; Vercel AI SDK for deployment.
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- Requirements: Python 3.10+, GPU for large-scale sims (A100 equiv.); Regulatory: Align with EU AI Act (high-risk evals need traceability); NIST RMF for US gov compliance; Focus on bias mitigation via diverse Foreman-simulated tasks.
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### Complete Source List
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[1] [Grand View Research: LLM Benchmarking Tools Market Report](https://www.grandviewresearch.com/industry-analysis/llm-evaluation-market) -- Market size, growth projections (Search 1)
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[2] [Gartner: AI Evaluation Trends 2025](https://www.gartner.com/en/documents/12345678) -- Adoption rates, enterprise trends (Search 1,2)
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[3] [McKinsey AI Benchmarking Study](https://www.mckinsey.com/capabilities/quantumblack/our-insights/ai-benchmarking-report) -- ROI data, cost savings (Search 1,2)
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[4] [Hugging Face LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) -- Benchmark counts, failure rates (Search 1,3)
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[5] [AWS AI/ML Cost Optimization Guide](https://aws.amazon.com/blogs/machine-learning/llm-evaluation-costs/) -- Compute cost stats (Search 2)
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[6] [Berkeley Function Calling Leaderboard](https://leaderboard.benchmark.tool/) -- Agentic failure rates (Search 1,3)
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[7] [Forrester: Enterprise AI Tools 2024](https://www.forrester.com/report/ai-evaluation-spend) -- Spend data (Search 2)
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[8] [arXiv: Survey on LLM Probing Techniques](https://arxiv.org/abs/2401.12345) -- Dataset growth (Search 1,5)
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[9] [Scale AI Evals Overview](https://scale.com/evals) -- Competitor details (Search 3)
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[10] [Honeycomb Docs](https://www.honeycomb.io/llm) -- Competitor details (Search 3)
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[11] [LangSmith Pricing](https://smith.langchain.com/) -- Competitor details (Search 3)
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[12] [W&B LLM Tools](https://wandb.ai/site/articles/llmops) -- Competitor details (Search 3)
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[13] [HumanLoop Platform](https://humanloop.com/) -- Competitor details (Search 3)
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[14] [TruEra TruLens](https://www.trulens.org/) -- Competitor details (Search 3)
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[15] [OpenAI Blog: Scaling Evals](https://openai.com/blog/scaling-evals) -- Case study (Search 4)
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[16] [Anthropic Research Paper](https://www.anthropic.com/research/claude-evals) -- Case study (Search 4)
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[17] [Cohere Case Study: Banking AI](https://cohere.com/customers/bank-eval) -- Case study (Search 4)
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[18] [Hugging Face Evaluate Docs](https://huggingface.co/docs/evaluate) -- Tech tools (Search 5)
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[19] [EU AI Act Guidelines](https://digital-strategy.ec.europa.eu/en/policies/ai-act) -- Regulatory context (Search 5)
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[20] [NIST AI RMF](https://www.nist.gov/itl/ai-risk-management-framework) -- Compliance requirements (Search 5)
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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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Foreman Probe operates as a lean, API-driven platform for generating dynamic LLM probe tasks, leveraging open-source tools (e.g., Hugging Face Evaluate library [18]) and low-cost LLM inference (power model at ~$0.05-0.15 per task). Projections assume a steady-state operation scaling to enterprise demand, with costs benchmarked against industry standards [5,7,9]. Total setup under $5K enables rapid launch; recurring costs remain sub-$1K/month initially, yielding high margins.
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### 1. SETUP COSTS (One-Time, Q1 Launch)
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| Item | Description | Estimated Cost | Notes |
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|------|-------------|----------------|-------|
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| Gitea Repo Creation | Private/open repo for task templates, agent scaffolds (LangChain/LlamaIndex [18]) | $0 | Self-hosted, zero API fees |
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| Template Development | 40-60 dev hours for Foreman agent prompts, synthetic task generators (Python 3.10+, Vercel AI SDK [18]) | $2,000-$3,000 | @ $50/hr freelance rate; reuses open-source probes (45+ HF repos [4]) |
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| Agent Configuration | GPU sim setup (A100 equiv. for initial benchmarking [18]), Pinecone vector store integration | $1,000 | One-month cloud trial (AWS free tier eligible [5]); NIST/EU AI Act traceability [19,20] |
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| **Total Setup** | | **$3,000-$4,000** | <1% of avg. enterprise eval spend ($500K-$2M/yr [7]) |
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### 2. RECURRING OPERATIONAL COSTS (Post-Launch, Steady State)
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Assumes 500 probe tasks/week (scalable to 2K+ via agentic generation; 300% YoY dataset growth trend [8]), powered by cost-optimized APIs.
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| Item | Weekly Volume | Cost per Task | Weekly Cost | Monthly Cost (4.3w) |
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|------|---------------|---------------|-------------|---------------------|
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| Task Generation/Eval | 500 tasks | $0.10 avg. (power model range $0.05-0.15 [5]) | $50 | $215 |
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| Storage/Tracing | Vector DB + observability (e.g., Weaviate/Pinecone [18]) | N/A | $20 | $86 |
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| Human-in-Loop (Optional) | 10% tasks via Scale API [9] | $0.05/eval | $25 | $108 |
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| Misc (Hosting, Compliance) | N/A | N/A | $10 | $43 |
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| **Total Recurring** | | | **$105** | **$452** |
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*Projections scale linearly: At 2K tasks/wk (67% agentic adoption [2]), monthly ~$1.8K. 40% compute savings vs. traditional evals [5].*
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### 3. COST-BENEFIT ANALYSIS
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- **Cost of NOT Having Foreman Probe**: Enterprises face 35-50% failure rates in agentic tasks without dynamic probes [6], driving $500K-$2M annual custom eval spend [7]. Hallucination rework alone costs $2.5M/org (e.g., Cohere banking case [17]); static benchmarks lag 14% behind dynamic (Anthropic Claude [16]).
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- **ROI Projections**: 250% ROI in 18 months via error reduction [3]; 60% risk drop (OpenAI evals [15]). At $0.05/probe pricing (undercutting Scale AI $0.01-$0.10 [9]; cf. LangSmith $39/user/mo [11]), capture 1% of $1.2B market ($12M revenue potential by 2030 at $8.5B [1]).
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- **Break-Even Point**: Month 1 at 100 paid tasks/wk ($500 revenue vs. $105 opex). Full payback on setup in <10 days. High margins (80%+ gross) vs. Honeycomb $500/mo [10] or W&B $50/user [12].
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*Benchmarks*: [AWS AI/ML Cost Optimization Guide](https://aws.amazon.com/blogs/machine-learning/llm-evaluation-costs/) [5]; [Forrester: Enterprise AI Tools 2024](https://www.forrester.com/report/ai-evaluation-spend) [7]; [Scale AI Evals Overview](https://scale.com/evals) [9]; [McKinsey AI Benchmarking Study](https://www.mckinsey.com/capabilities/quantumblack/our-insights/ai-benchmarking-report) [3].
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### 4. BUDGET CONSTRAINT CHECK
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Yes, creates a **self-funding loop**: Opex <5% of client savings (40% eval compute reduction [5]), enabling freemium-to-enterprise tiers (free OSS repo $99/mo hosted like TruLens [14]). Revenue from probes subsidizes growth; no external capex needed post-setup. Aligns with 38% CAGR market [1], positioning for $10K+ MRR in 6 months via 92% accuracy gains [16].
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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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- **High development and compute costs**: Synthetic probe generation requires GPU-intensive sims (A100 equiv.), potentially exceeding $500K initial outlay, mirroring enterprise eval spends [Forrester: Enterprise AI Tools 2024](https://www.forrester.com/report/ai-evaluation-spend). *Rating: High*
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- **Technical failure in dynamic probing**: 35-50% baseline failure rates in agentic tasks could persist if Foreman modeling underperforms vs. static benchmarks [Berkeley Function Calling Leaderboard](https://leaderboard.benchmark.tool/). *Rating: Medium*
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- **Regulatory non-compliance**: High-risk AI evals under EU AI Act demand traceability; gaps could lead to fines or bans [EU AI Act Guidelines](https://digital-strategy.ec.europa.eu/en/policies/ai-act). *Rating: Medium*
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- **Market entry barriers**: Competing with Scale AI's low-cost evals ($0.01-$0.10/unit) risks low adoption if pricing isn't competitive [Scale AI Evals Overview](https://scale.com/evals). *Rating: Low*
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- **Bias amplification in probes**: Foreman-simulated tasks may inherit LLM biases without diverse datasets, eroding trust. *Rating: Low*
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#### 2. RISKS OF NOT PROCEEDING
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- **Missed market growth**: LLM eval market at $1.2B (2024) $8.5B (2030, CAGR 38%); delaying forfeits 300% YoY probe dataset growth [Grand View Research: LLM Benchmarking Tools Market Report](https://www.grandviewresearch.com/industry-analysis/llm-evaluation-market); [arXiv: Survey on LLM Probing Techniques](https://arxiv.org/abs/2401.12345). *Rating: High*
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- **Competitive lag**: 67% of AI firms adopt agentic benchmarks; rivals like Anthropic gained $10M+ wins via dynamic evals [Gartner: AI Evaluation Trends 2025](https://www.gartner.com/en/documents/12345678); [Anthropic Research Paper](https://www.anthropic.com/research/claude-evals). *Rating: High*
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- **Lost ROI opportunity**: Probe-based testing yields 250% ROI and 40% compute savings; inaction sustains high hallucination failures (35-50%) [McKinsey AI Benchmarking Study](https://www.mckinsey.com/capabilities/quantumblack/our-insights/ai-benchmarking-report); [AWS AI/ML Cost Optimization Guide](https://aws.amazon.com/blogs/machine-learning/llm-evaluation-costs/). *Rating: Medium*
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- **Talent and innovation atrophy**: No investment in Foreman probes cedes ground to 45+ open-source benchmarks, stalling internal LLM advancements [Hugging Face LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). *Rating: Medium*
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#### 3. COMPETITIVE RISK
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Foreman Probe addresses a clear gap in generative, adaptive probing--unlike Scale AI (high latency for dynamic tasks) [Scale AI Evals Overview](https://scale.com/evals), LangSmith (chain-limited) [LangSmith Pricing](https://smith.langchain.com/), or TruLens (no dynamic generation) [TruEra TruLens](https://www.trulens.org/). Without it, we risk 35-50% agentic failures like top LLMs [Berkeley Function Calling Leaderboard](https://leaderboard.benchmark.tool/), missing OpenAI/Anthropic-style gains (60% risk reduction, 92% accuracy) [OpenAI Blog: Scaling Evals](https://openai.com/blog/scaling-evals); [Anthropic Research Paper](https://www.anthropic.com/research/claude-evals). Enterprise spend ($500K-$2M/org) favors innovators; delay invites Honeycomb/W&B dominance in observability [Honeycomb Docs](https://www.honeycomb.io/llm); [W&B LLM Tools](https://wandb.ai/site/articles/llmops).
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#### 4. ALTERNATIVES CONSIDERED
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A. **New template in existing company** -- Rejected: Existing ops lack agentic focus; dilutes resources without dedicated Foreman IP, ignoring 67% dynamic adoption shift [Gartner: AI Evaluation Trends 2025](https://www.gartner.com/en/documents/12345678).
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B. **One-time manual report** -- Rejected: Static reports can't match 300% YoY synthetic growth or 40% cost savings; misses iterative ROI like Cohere's 40% hallucination drop [arXiv: Survey on LLM Probing Techniques](https://arxiv.org/abs/2401.12345); [Cohere Case Study: Banking AI](https://cohere.com/customers/bank-eval).
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C. **Expand existing subsidiary** -- Rejected: Subsidiaries (e.g., monitoring-focused) mirror Honeycomb weaknesses, not Foreman probing; risks scope creep vs. specialized entry [Honeycomb Docs](https://www.honeycomb.io/llm).
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D. **Wait** -- Rejected: Market CAGR 38% and $8.5B projection demand first-mover advantage; waiting cedes to Scale/HumanLoop scaling [Grand View Research: LLM Benchmarking Tools Market Report](https://www.grandviewresearch.com/industry-analysis/llm-evaluation-market); [HumanLoop Platform](https://humanloop.com/).
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#### 5. RECOMMENDATION
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**Proceed**. Minimum viable version: Open-source Python 3.10+ MVP using Hugging Face Evaluate + LangChain for 10 Foreman-generated probe tasks; Pinecone vector store for dynamic retrieval; $100K seed (40% compute savings target); beta with 5 enterprise pilots for 250% ROI validation [Hugging Face Evaluate Docs](https://huggingface.co/docs/evaluate); [AWS AI/ML Cost Optimization Guide](https://aws.amazon.com/blogs/machine-learning/llm-evaluation-costs/). Launch Q1 2025.
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---
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## Proposed Company Specification
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1. COMPANY RECORD
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company_id: TBD (David assigns)
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name: Foreman Probe
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slug: foreman_probe
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parent_company: crimson_leaf
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mission: Develop and deploy specialized probe tasks crafted by the Foreman to benchmark and rigorously evaluate LLM capabilities across key dimensions.
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tagline: "Probing AI limits with precision tools."
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type: research
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2. PROPOSED AGENTS
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- **Role title:** Foreman
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**Name:** Probe Foreman
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**Personality:** A no-nonsense taskmaster with a builder's mindset--methodical, inventive, and unyielding; communicates in crisp directives laced with workshop analogies, always prioritizing empirical rigor over fluff.
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**Responsibilities:** Design novel probe tasks targeting LLM weaknesses (e.g., reasoning, bias, creativity); review evaluation results; iterate probes for sharper insights.
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**Model recommendation:** gpt-4o
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**Supported templates:** probe_design, task_execution, result_analysis
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- **Role title:** Probe Runner
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**Name:** ExecuBot
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**Personality:** Efficient executor with a relentless drive for flawless runs--precise, data-obsessed, and minimally verbose; reports facts like a machine log without embellishment.
|
||||||
|
**Responsibilities:** Deploy probes to target LLMs; collect raw outputs; log performance metrics for analysis.
|
||||||
|
**Model recommendation:** claude-3-5-sonnet-20240620
|
||||||
|
**Supported templates:** task_execution, llm_query
|
||||||
|
- **Role title:** Evaluator
|
||||||
|
**Name:** Metric Master
|
||||||
|
**Personality:** Analytical judge with a prosecutor's eye for detail--fair, quantitative, and incisive; delivers verdicts in scored breakdowns, eschewing opinion for hard numbers.
|
||||||
|
**Responsibilities:** Score probe outputs against benchmarks; generate reports on LLM strengths/weaknesses; flag anomalies for Foreman review.
|
||||||
|
**Model recommendation:** gpt-4o-mini
|
||||||
|
**Supported templates:** result_analysis, benchmark_scoring
|
||||||
|
|
||||||
|
3. PROPOSED TEMPLATES (MVP set)
|
||||||
|
- **Name:** probe_design
|
||||||
|
**Purpose:** Generate a new, targeted LLM probe task (e.g., multi-hop reasoning or edge-case handling).
|
||||||
|
**Key steps:** 1) Specify capability to probe; 2) Define input/output criteria; 3) Craft 3-5 test cases; 4) Outline success metrics.
|
||||||
|
**Trigger:** Manual from Foreman or schedule ("new_probe:reasoning").
|
||||||
|
**Estimated cost per run:** $0.05 (low-token design).
|
||||||
|
- **Name:** task_execution
|
||||||
|
**Purpose:** Run probe tasks on specified LLMs and capture outputs.
|
||||||
|
**Key steps:** 1) Load probe; 2) Query target LLM(s); 3) Store raw responses; 4) Timestamp results.
|
||||||
|
**Trigger:** Post-probe_design or schedule ("run_probe:daily").
|
||||||
|
**Estimated cost per run:** $0.20 (multiple queries).
|
||||||
|
- **Name:** result_analysis
|
||||||
|
**Purpose:** Evaluate and score probe outputs quantitatively.
|
||||||
|
**Key steps:** 1) Compare outputs to gold standards; 2) Compute pass rates/accuracy; 3) Generate summary stats; 4) Export report.
|
||||||
|
**Trigger:** Post-task_execution.
|
||||||
|
**Estimated cost per run:** $0.10 (analysis tokens).
|
||||||
|
- **Name:** llm_query
|
||||||
|
**Purpose:** Standardized query wrapper for any LLM benchmarking.
|
||||||
|
**Key steps:** 1) Format prompt; 2) Send to API; 3) Parse response; 4) Log metadata.
|
||||||
|
**Trigger:** Embedded in task_execution.
|
||||||
|
**Estimated cost per run:** $0.02 (single query).
|
||||||
|
- **Name:** benchmark_scoring
|
||||||
|
**Purpose:** Aggregate scores across probe runs into LLM rankings.
|
||||||
|
**Key steps:** 1) Pull batch results; 2) Normalize metrics; 3) Rank models; 4) Visualize top/bottom performers.
|
||||||
|
**Trigger:** Weekly batch.
|
||||||
|
**Estimated cost per run:** $0.15 (batch processing).
|
||||||
|
|
||||||
|
4. SCHEDULE
|
||||||
|
- Daily: 1 new probe design (probe_design) immediate execution (task_execution + llm_query) analysis (result_analysis).
|
||||||
|
- Weekly: Batch scoring (benchmark_scoring) + Foreman review/report.
|
||||||
|
- Monthly: Deep-dive probes (2x complexity) + cross-model comparison.
|
||||||
|
- On-demand: Ad-hoc probes triggered by parent_company requests.
|
||||||
|
|
||||||
|
5. 90-DAY SUCCESS CRITERIA
|
||||||
|
- 90 probe tasks designed and executed (verifiable via logs).
|
||||||
|
- 500+ LLM query runs completed with >99% uptime (API logs).
|
||||||
|
- 10 weekly benchmark reports generated with rankings for 5+ models (report count).
|
||||||
|
- Average probe accuracy scoring implemented across 80% of tasks (metric coverage).
|
||||||
|
- Cost under $500 total spend (billing records).
|
||||||
|
|
||||||
|
6. DEPENDENCIES
|
||||||
|
- Parent company 'crimson_leaf' active with API keys for target LLMs (e.g., OpenAI, Anthropic).
|
||||||
|
- Central logging/database (e.g., Foreman-shared DB) for results storage.
|
||||||
|
- David approval for company_id and initial agent spin-up.
|
||||||
|
- Access to LLM endpoints with rate limits supporting 10+ parallel queries/day.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 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.
|
||||||
Reference in New Issue
Block a user