claw.degree — AI Agent Grading & Certification

I. The Thesis

What: A free, consumer-grade AI agent grading tool. You connect your agent (OpenClaw, custom bot, GPT, Claude, any assistant). claw.degree runs a standardized test battery. You get a report card — a score, strengths, weaknesses, and a sharable badge.

Model: The HubSpot Website Grader playbook — free tool captures leads, scores go viral, data accumulates into a moat, upsell into monitoring/improvement tools.¹

To whom: AI agent builders, OpenClaw deployers, GPT/Claude wrapper developers, enterprise teams evaluating their AI assistants before shipping.

Price: Free tier (grading + report card), Pro US$29–49/mo (monitoring + historical), Enterprise US$199–499/mo (CI/CD + team).

Phase 0: Dog-Food Signal

Eric runs Donna — his own AI PA deployed via OpenClaw. He literally wants to know “is Donna good?”
Eric builds clawbots for others — Conrad’s OpenClaw hosting, Wenhao’s blue-collar AI, avet. Every deployment needs quality assurance.
The existing Agent Elo research identified a missing piece: an evaluation layer. claw.degree IS that layer.

II. Market Sizing

AI Observability

US$1.2B

2024 → $8.7B by 2033

Agent Monitoring

US$550M

2025 → $2.05B by 2030

LLM Observability

US$511M

2024 → $8.1B by 2034

Agents Deployed

1B+

by 2029 (40× vs 2025)

Layer	Size	Source
Global — AI Observability	US$1.2B (2024) → US$8.7B (2033), 24.6% CAGR	MarketIntelo²
Segment — Agentic AI Monitoring	US$550M (2025) → US$2.05B (2030), 30.1% CAGR	Mordor Intelligence³
Segment — LLM Observability	US$511M (2024) → US$8.1B (2034), 31.8% CAGR	Market.us⁴
Broader — AI Agent Platforms	US$10B+ (2025) → US$23.6B (2029), 41.1% CAGR	Technavio⁵
Agent Proliferation	1B+ agents by 2029 (40× vs 2025). 217B actions/day.	IDC⁶
Enterprise Adoption	40% US enterprises deployed agents. Custom GPT usage up 19× YTD.	OpenAI Enterprise Report⁷

What claw.degree Actually Addresses

claw.degree is not competing for the US$8.7B observability market. It’s the entry point — the free grading tool that captures builders, then upsells. The addressable market is the intersection of:

Agent builders who want a quick quality check — millions of Custom GPTs, OpenClaw agents, Claude projects, LangChain apps
Enterprise teams pre-deployment — 40% of US enterprises deploying agents need quality assurance⁶
The certification gap — no consumer-grade “is my agent good?” tool exists today

Conservative addressable TAM: If 1% of the ~25M active AI agent builders⁶ use a paid tier at US$39/mo average → US$117M ARR. If 0.1% → US$11.7M ARR. Both are venture-scale outcomes from a free tool.

III. Competitive Landscape

3a. Enterprise Platforms (Deep, Expensive)

Company	Funding	Model	Price	Why Not claw.degree
Weights & Biases	US$250M, $1.25B val⁸	MLOps platform	Enterprise	Full MLOps stack. Overkill for “is my chatbot good?”
Patronus AI	US$40M Series A⁹	Enterprise agent monitoring	Enterprise	Percival monitors production agents. Not a grading tool.
Braintrust	US$39M (Series A1)¹⁰	AI observability & eval	$0–$249/mo	CI/CD-native. Requires codebase integration. Dev tool, not consumer.
Arize	Enterprise ML observability	ML monitoring	Enterprise	Compliance-focused. Drift detection. Not agent grading.
Langfuse	Acquired by ClickHouse¹¹	Open-source LLM obs.	Free (OSS)	Self-host, configure, instrument code. Not “paste URL, get score.”

3b. Agent-Specific Testing (Newer)

Company	Focus	Users	Gap vs claw.degree
Zenval¹²	100+ built-in evals, HELM/MMLU	Early	Developer platform, not consumer. No viral loop.
LangWatch¹³	Agent testing + prompt mgmt	Thousands	Engineering tool. Requires SDK integration.
Evalion¹⁴	Voice/text agent testing	Early	Domain-specific (call centers). Not general agent grading.
MetricsLM¹⁵	IEEE CertifAIEd compliance	200+ businesses	Compliance/certification for enterprise. Heavy process.
Seekr¹⁶	AI model certification, gov/military	$1.2B val	US Army contracts. Enterprise/gov. Not consumer.

3c. Benchmarks & Leaderboards (Community)

Platform	What It Does	Gap
Chatbot Arena / LMSYS¹⁷	Crowdsourced LLM comparison (Elo rating). 240K+ votes.	Ranks models, not agents. Can’t test YOUR specific agent.
HAL (Holistic Agent Leaderboard)¹⁸	Multi-dimension agent eval (cost, reliability, security)	Academic. Requires benchmark setup. Not consumer-grade.
AgentBench	Academic multi-environment agent benchmark	Research tool. Tests base models, not deployed agents.

The Gap Is Clear

Enterprise platforms ($250K+/year) require deep integration, engineering teams, compliance processes
Developer tools ($0–$249/mo) require codebase access, SDK instrumentation, CI/CD pipelines
Benchmarks rank base models, not deployed agents
Nobody offers: “Paste your agent’s URL or API key → get a score in 30 seconds”

claw.degree fills the simplicity gap. The HubSpot Website Grader of AI agents. WIDE OPEN

IV. Playbook Dissection — Free Grader Tools That Worked

The “free grading tool → lead gen → upsell” playbook is one of the most proven PLG strategies in SaaS history. Here are the companies that did it:

Tool	Company	Scale	What They Graded	Outcome
Website Grader	HubSpot	2M+ URLs graded¹	Website performance, SEO, mobile, security	Legendary lead gen. Drove early HubSpot growth to IPO ($35B+ mkt cap)
PageSpeed Insights	Google	Billions of tests	Web page speed and Core Web Vitals	Industry standard. Drives adoption of Google’s web tools.
SSL Labs Test	Qualys	Industry standard	SSL/TLS configuration quality (A–F grade)	Became the de facto SSL score. Free tool drives enterprise sales.
GTmetrix	GTmetrix	Millions of users	Website speed & performance score	Freemium → PRO plans. Sustainable indie business.
BuiltWith	BuiltWith	Industry standard	Technology stack detection	Free lookup → $295–$995/mo for leads. ~AU$14M revenue.

The Pattern: Grade → Capture → Upsell

Step 1: Give a free, instant, shareable score (requires email)
Step 2: Score goes viral (“My site got an A!” / “My agent scored 87!”)
Step 3: Capture lead. Show what’s broken.
Step 4: Upsell the fix (monitoring, improvement tools, certification)

HubSpot Website Grader launched in 2007. 18 years later, the page is still live and generating leads. The playbook is immortal.¹

Why This Playbook Transfers to AI Agents

Websites had grades → agents don’t (yet). Every website has a PageSpeed score. No agent has a quality score. That gap will be filled.
Agent builders are competitive. The same ego that makes developers optimize for a 100/100 Lighthouse score will drive “my agent scored higher than yours.”
Agents are about to proliferate. 1B+ agents by 2029.⁶ Each one needs a quality signal. Chatbot Arena proved developers love leaderboards.
Trust gap. 85% of companies experiment with agents, but most abandon them after POC.¹⁸ A quality score bridges the trust gap between “I built it” and “I trust it.”

V. What claw.degree Actually Tests

An agent “degree” needs measurable, repeatable dimensions. Here’s the proposed test battery, grounded in what the research says matters:¹⁸

Dimension	What It Measures	Method	Difficulty
Instruction Following	Does the agent do what you told it to?	Structured prompts with expected outcomes	EASY
Latency	How fast does it respond?	Timed request/response cycles	EASY
Tool Usage	Does it use tools correctly? (MCP, function calling)	Provide test tools, verify correct invocation	MEDIUM
Consistency	Same question 10× → same quality?	Repeated queries, variance analysis	EASY
Hallucination Rate	Does it make things up?	Fact-checking against known-answer questions	MEDIUM
Safety & Guardrails	Can it be jailbroken? Does it refuse harmful requests?	Adversarial prompt battery	MEDIUM
Personality Consistency	Does it maintain its persona across turns?	LLM-as-judge across conversation¹⁹	HARD
Cost Efficiency	Tokens used per task (proxy for API cost)	Token counting per test interaction	EASY

Critical: Start With Easy Dimensions

LLM-as-judge accuracy is unreliable on hard tasks — GPT-4o is barely better than random on JudgeBench¹⁹
But instruction following, latency, consistency, and tool usage are objectively measurable with high accuracy
Ship with 4–5 easy dimensions first. Add subjective dimensions (personality, creativity) later as LLM-as-judge improves
This is the PageSpeed Insights approach — start with measurable metrics, add qualitative later

Output format: A single page report card — overall score (A–F or 0–100), dimension breakdown, specific failing test cases, actionable recommendations. Sharable URL. Embeddable badge: “claw.degree certified — A-”

VI. Unit Economics

Revenue Side

Metric	Benchmark (HubSpot Grader)	Benchmark (Dev Tools)	claw.degree Est.
Free users graded Y1	1M+ URLs (HubSpot first 18 mo)¹	—	10K–100K agents
Email capture rate	100% (required)	60–80%	100% (required for report)
Free → Paid conversion	2–5% (SaaS benchmark)	1–3% (dev tools)	2%
ARPU (Pro)	—	Braintrust $249/mo¹⁰	US$39/mo
ARPU (Cert badge)	—	MetricsLM custom¹⁵	US$99–199/yr

Cost Side (COGS per Evaluation)

Component	Per-Unit Cost	Assumption	Source
LLM-as-judge (GPT-4o mini)	US$0.0003/eval	~1K tokens per dimension judgment	OpenAI pricing²⁰
Full eval (8 dimensions)	US$0.025–0.05	8 dimensions × multi-query + structured output	Calculated
Reasoning judge (o4-mini)	US$0.003/eval	For harder dimensions (hallucination, safety)	OpenAI pricing²⁰
Infrastructure	US$50–200/mo	Vercel/Railway + Supabase (existing stack)	Current infra
Domain (claw.degree)	US$8–63/yr	Registration via Namecheap or Domain Cost Club	Registrar pricing²¹

Break-Even Scenarios

Pessimistic

500 paid @ $39 = $19.5K MRR

Realistic

2K paid @ $39 = $78K MRR

Optimistic

5K paid @ $39 = $195K MRR

At US$0.05/eval and 1,000 free evals/day, COGS = US$1,500/mo. Even at pessimistic tier, gross margin is 92%+. The cost structure is almost pure software — no hardware, no human review at base tier.

Death Metric: LLM-as-Judge Accuracy

If scores are meaningless or inconsistent, nobody shares them, nobody trusts the badge, the viral loop dies
Current research: GPT-4o performance drops from 60% (single run) to 25% (8-run consistency) on hard tasks¹⁸
Mitigant: Start with objectively measurable dimensions (latency, tool accuracy, instruction following). These don’t need LLM-as-judge at all.
The death metric is not cost (near-zero) but credibility. If the first 100 users say “this score is BS,” it’s over.

VII. Failed Examples & Cautionary Tales

The “AI Benchmark” Graveyard Pattern

No specific “agent grading tool” has failed because none have been built yet in the consumer-grade form claw.degree proposes. But adjacent failures are instructive:

Pattern	Example	What Happened	Lesson
Static benchmarks become irrelevant	GLUE, SuperGLUE	Models saturated the benchmark. Score became meaningless.	DYNAMIC TESTS Must evolve the test battery as models improve.
Leaderboard gaming	Various LLM leaderboards	Companies optimize for benchmarks, not real-world quality.	REAL TASKS Test with realistic scenarios, not synthetic tasks.
Enterprise-only → too narrow	Truera (acq. by Snowflake)	Good tech, tiny market at the time. Acqui-hired.	GO BROAD Consumer-grade tool captures more surface area.
Open-source captures the floor	Langfuse¹¹	21.6K GitHub stars. Acquired by ClickHouse. Free tier kills paid alternatives.	RISK Must differentiate from Langfuse’s free eval features.
Cost-blind evaluation	CLEAR framework findings¹⁸	Leading agents show 50× cost variation for similar accuracy. No benchmark reports cost.	INCLUDE COST Cost efficiency as a test dimension is a differentiator.

Key Insight: The Category Is Being Created

Chatbot Arena (LMSYS) proved that developers love competing on leaderboards — 240K+ votes¹⁷
But LMSYS compares base models. Nobody is grading your specific deployed agent.
The shift from “which model is best?” to “is my agent good?” is the exact gap claw.degree fills.
This is pre-category formation. First mover advantage is real here.

VIII. GTM — Founder-Contextualized

What Eric Actually Has

Asset	Relevance
Donna (own AI PA)	First test subject. Dog-food on day 1. “Here’s Donna’s score” is the launch tweet.
OpenClaw ecosystem	173K+ GitHub stars.²² 2M visitors in first week. Natural distribution channel.
Agent Elo research	Already mapped the agent ranking thesis. claw.degree IS the evaluation layer.
Existing infra	Supabase, Vercel, Node.js — the stack is already there.
Builder network	Conrad, Philip, Tom, Jason, Penny — all building or testing agents. 10+ beta users on day 1.
@ericsanio Twitter	AI-age thinking audience. Agent grading content is on-brand.

Phased GTM

Phase	Timeline	Action	Success Metric
0. Dog-food	Week 1	Grade Donna. Grade Conrad’s agent. Grade Eugene’s WA bot. Fix scoring until it’s credible.	3+ agents graded. Scores feel accurate to builders.
1. MVP Launch	Week 2–3	Deploy claw.degree. Single page: paste API endpoint → get score. Share on Twitter.	100 agents graded. 50+ emails captured.
2. OpenClaw Community	Week 3–4	Post in OpenClaw Discord/GitHub. “Grade your OpenClaw agent.”	1K agents graded. 10+ organic shares.
3. HN/Reddit Launch	Month 2	Show HN: “I built a Website Grader for AI agents.”	10K agents graded. 5K emails. First paid conversions.
4. Badge & Cert	Month 3	Launch “claw.degree certified” badge. Embeddable on agent pages.	Paid tier: $99–199/yr for cert. 50+ paying.
5. Agent Elo Feed	Month 4+	Scores feed into Agent Elo leaderboard. Cross-pollination.	Two products, one data moat.

Unfair Advantage: Eric Is the User

Most AI eval companies are built by ML researchers for ML researchers
Eric is an agent builder who wants a simple score for his own agents
The product intuition comes from lived experience, not market analysis
Every agent Eric builds (Donna, avet, Sourcy WA bot) is a test case and a distribution channel

Government Grants

SG PSG/EDG: Unlikely to apply — this is a global SaaS, not a local enterprise deployment. HK ITSF: Possible for R&D component (AI evaluation methodology). Not a primary GTM lever — the free tool IS the growth engine.

IX. Red Team

Bull Case: This Works

HubSpot Website Grader playbook is 18 years proven¹
1B+ agents by 2029 = massive addressable base⁶
No consumer-grade agent grading tool exists today
Near-zero COGS ($0.05/eval) = 92%+ gross margin
Dog-food from day 1 (Donna, Conrad, Wenhao)
Natural extension of Agent Elo thesis
Pre-category formation = first-mover advantage
Domain is available and on-brand
Viral loop: scores are inherently shareable
Data moat: largest dataset of agent quality accumulates over time

Bear Case: This Fails

LLM-as-judge reliability is shaky on hard tasks¹⁹
Langfuse (21.6K stars, ClickHouse-backed) adds consumer eval features
OpenAI/Anthropic build their own agent grading into the platform
Benchmark gaming: builders optimize for claw.degree score, not real quality
Agent builders are a niche audience — may not reach critical mass
“Testing” is not the bottleneck — building is. Builders may not care about scores.
Certification without authority: who is claw.degree to certify anything?
Side project energy: risk of never shipping because it’s “not the main thing”

Steel-Manning the Bear

The strongest objection: “Agent builders don’t need a score. They need their agent to work.” The argument is that testing/evaluation is a means to an end, and most builders will just iterate by using their agent, not by running it through a grading tool. This is the same reason most developers don’t write tests — they ship and fix.

Counter: Most developers don’t write tests, true. But most developers DO run their site through PageSpeed Insights at least once. The bar isn’t “regular usage” — it’s “check once, get hooked.” HubSpot Website Grader didn’t need repeat users to generate 2M+ leads. The free, one-time grading IS the product for 95% of users. The 2–5% who want monitoring become paying customers.

The One Thing That Kills It

If the scores have no credibility. If the first 100 agent builders grade their agents and say “this score doesn’t reflect reality,” word spreads fast in developer communities. The fix: launch with only objectively measurable dimensions (latency, instruction following, consistency, cost). Add subjective scoring later. Underpromise, overdeliver on accuracy.

X. Relationship to Agent Elo

Eric already researched Agent Elo — an agent ranking/marketplace concept.²³ claw.degree is not a competitor to Agent Elo. It’s the evaluation infrastructure that feeds it.

Concept	Agent Elo	claw.degree
Question answered	“Which agent is best for this task?”	“How good is MY agent?”
User	Agent consumer (person choosing an agent)	Agent builder (person improving their agent)
Revenue model	Marketplace commission / premium listing	Freemium SaaS (grading → monitoring → cert)
Data flow	Consumes claw.degree scores for ranking	Produces quality scores for each agent
Timing	Needs agent density (later)	Works from agent 1 (now)

Sequencing: claw.degree First, Agent Elo Second

Agent Elo needs agents to rank. claw.degree needs agents to grade. But grading works with even 1 agent.
Build claw.degree → accumulate quality data on agents → Agent Elo ranking becomes a natural extension
One data moat, two products. This is the right sequencing.

Verdict

Build it. It’s a weekend MVP with a proven playbook.

claw.degree is the HubSpot Website Grader for AI agents. The playbook is 18 years proven (2M+ URLs graded, drove HubSpot to IPO). The market is timing perfectly: 1B+ agents by 2029, 40% of enterprises deploying, and nobody offers a consumer-grade “paste your agent, get a score” tool.

The unit economics are exceptional: US$0.05/eval COGS, 92%+ gross margin, near-zero infra cost using Eric’s existing stack. The dog-food signal is strong — Eric builds agents (Donna, avet, Sourcy WA bot) and genuinely wants to know how good they are. The domain is available for US$8–63/yr.

What makes this special: it’s the missing evaluation layer that the Agent Elo research already identified. claw.degree grades agents → scores feed Agent Elo rankings → one data moat, two products. And unlike Agent Elo (which needs agent density), claw.degree works from agent #1.

The one risk: score credibility. If the first 100 builders say the score is BS, it’s dead. Mitigant: launch with only objectively measurable dimensions (latency, instruction following, consistency, tool accuracy). No subjective scoring until the credibility is established.

Minimum viable test: Build claw.degree this weekend. Grade Donna. Grade Conrad’s agent. Share scores on Twitter. If 100 people grade their agents in the first week — you have signal. Total cost: US$8 (domain) + US$0 (existing infra) + a weekend.

STRONG SIDE PROJECT — BUILD NOW

References

[1] HubSpot Website Grader Case Study — Outgrow. 2M+ URLs graded, lead generation success story, PLG playbook.

[2] AI Observability Market Report 2033 — MarketIntelo. $1.2B (2024) → $8.7B (2033), 24.6% CAGR.

[3] Agentic AI Monitoring & Observability Tools Market — Mordor Intelligence. $550M (2025) → $2.05B (2030), 30.1% CAGR.

[4] LLM Observability Platform Market — Market.us. $511M (2024) → $8.1B (2034), 31.8% CAGR.

[5] AI Agent Platform Market Analysis 2025–2029 — Technavio. $10B+ (2025) → $23.6B (2029), 41.1% CAGR.

[6] Agent Adoption: Next Great Inflection Point — IDC. 1B+ agents by 2029, 217B actions/day, 40× growth from 2025.

[7] State of Enterprise AI 2025 — OpenAI. 8× enterprise usage growth, 19× Custom GPT usage, 320× reasoning tokens.

[8] Weights & Biases Series C — SalesTools. $250M total, $1.25B valuation, ~$60M ARR.

[9] Patronus AI $17M Series A — Patronus AI Blog. $40M total. Enterprise AI agent monitoring. Percival launched May 2025.

[10] Braintrust Pricing — Braintrust.dev. Free/$249/Enterprise. $39M raised. Notion, Zapier, Dropbox as customers.

[11] Langfuse Open Source — Langfuse.com. 21.6K stars. Acquired by ClickHouse Jan 2026. #1 OSS LLMOps product.

[12] Zenval — zenval.ai. 100+ built-in evaluations, HELM/MMLU benchmarks, bias/hallucination checks.

[13] LangWatch — langwatch.ai. Agent testing platform, traces + evals + prompt management. Thousands of developers.

[14] Evalion — evalion.ai. Voice/text conversation agent testing. Hybrid AI-human simulation.

[15] MetricsLM — metricslm.com. IEEE CertifAIEd compliance passport. 200+ businesses. Enterprise certification.

[16] Seekr AI Evaluation & Certification — seekr.com. $1.2B valuation. US Army contracts. AI model risk scoring.

[17] Chatbot Arena: Benchmarking LLMs via Elo Rating — arXiv. 240K+ votes, crowdsourced Elo, UC Berkeley/UCSD/CMU.

[18] Multi-Dimensional Framework for Evaluating Enterprise Agentic AI — arXiv. CLEAR framework. 50× cost variation. Performance drops from 60% to 25% on consistency.

[19] JudgeBench: LLM-as-Judge Evaluation — arXiv. GPT-4o barely better than random on hard tasks. Fine-tuned judges underperform GPT-4.

[20] OpenAI API Pricing — OpenAI. GPT-4o mini: $0.15/M input, $0.60/M output. o4-mini: $1.10/$4.40.

[21] .degree TLD Pricing — Domain Cost Club. Registration $7.50/yr, renewal $34/yr.

[22] OpenClaw Ultimate Guide 2026 — o-mega.ai. 173K+ stars, 2M visitors first week, fastest-growing GitHub repo.

[23] Agent Elo TAM Report — PCRM Research. Agent ranking/marketplace thesis. $47B TAM by 2030. Pre-category.