AgentProbe
Catch the bugs your AI agents hide before your users find them
● The Problem
Two thirds of organizations are experimenting with AI agents, but fewer than one in four have scaled them to production. The #1 barrier is quality: 32% of teams cite it as the top blocker. Traditional testing frameworks test deterministic software. AI agents are non-deterministic, multi-step, and tool-calling. A customer support agent that routes correctly 95% of the time still fails on every 20th ticket. Debugging these failures requires replaying full conversation arcs, not checking individual outputs.
● The Solution
A testing and monitoring platform built specifically for AI agents. Define test scenarios in natural language, simulate synthetic users that interact with your agent end-to-end, and evaluate full conversation sessions with LLM-based judges. Mock external tool calls so tests run without hitting real APIs. Monitor production agents for quality drift and alert when pass rates drop below thresholds.
Key Signals
MRR Potential
$20K-100K
Competition
Medium
Build Time
1-3 Months
Search Trend
rising
Market Timing
Cekura (YC F24) launched on Hacker News this week with 89 points and strong endorsement. TestSprite 2.1 hit 316 upvotes on Product Hunt. Anthropic published "Demystifying evals for AI agents" in January 2026. LangChain reports 89% of agent teams use observability but only 52% use evals. The gap between monitoring and testing is where the opportunity sits.
MVP Feature List
- 1Natural language test scenario builder
- 2Synthetic user simulator for multi-turn conversations
- 3Full-session LLM-based evaluation (not turn-by-turn)
- 4Mock tool platform for external API calls
- 5CI/CD integration via GitHub Actions
- 6Production quality monitoring with drift alerts
- 7Test report dashboard with pass rates by scenario
Suggested Tech Stack
Go-to-Market Strategy
Free tier with 50 test runs/month to get individual developers building agents. $29/month starter plan matches Cekura pricing. Target AI agent framework communities (LangChain, CrewAI, Autogen) with integration guides. Write the definitive "how to test AI agents" tutorial series. Sponsor AI agent Discord servers and hackathons. Land voice AI companies first since voice agent testing is the most painful variant.
Target Audience
Monetization
Tiered PlansCompetitive Landscape
Cekura (YC F24, $30/month) focuses on voice and chat agents with scenario generation and mock tooling. TestSprite 2.1 targets AI-generated code testing with GitHub PR integration. DeepEval is open-source and pytest-compatible but requires significant engineering effort to configure. Braintrust and Maxim offer LLM eval platforms but focus on model evaluation, not agent workflow testing. No one offers a simple, affordable platform that combines synthetic user simulation with production monitoring for small teams.
Why Now?
79% of organizations deployed AI agents in 2025. Gartner predicts 40% of enterprise software will embed agents by end of 2026. But evals adoption lags observability (52% vs 89% per LangChain data). Anthropic published its agent eval guide in January 2026, signaling that even model providers see testing as an unsolved problem. The tooling gap between "agents can do things" and "we can verify agents do things correctly" is the biggest unaddressed risk in AI infrastructure.
Tools & Resources to Get Started
Frequently Asked Questions
What problem does AgentProbe solve?
Two thirds of organizations are experimenting with AI agents, but fewer than one in four have scaled them to production. The #1 barrier is quality: 32% of teams cite it as the top blocker. Traditional testing frameworks test deterministic software. AI agents are non-deterministic, multi-step, and tool-calling. A customer support agent that routes correctly 95% of the time still fails on every 20th ticket. Debugging these failures requires replaying full conversation arcs, not checking individual outputs.
How much MRR can AgentProbe generate?
AgentProbe has $20K-100K MRR potential with a Tiered Plans model. The estimated build time is 1-3 Months with Medium competition in the market.
What are the MVP features for AgentProbe?
Natural language test scenario builder. Synthetic user simulator for multi-turn conversations. Full-session LLM-based evaluation (not turn-by-turn). Mock tool platform for external API calls. CI/CD integration via GitHub Actions. Production quality monitoring with drift alerts. Test report dashboard with pass rates by scenario.
What is the go-to-market strategy for AgentProbe?
Free tier with 50 test runs/month to get individual developers building agents. $29/month starter plan matches Cekura pricing. Target AI agent framework communities (LangChain, CrewAI, Autogen) with integration guides. Write the definitive "how to test AI agents" tutorial series. Sponsor AI agent Discord servers and hackathons. Land voice AI companies first since voice agent testing is the most painful variant.
Who is the target audience for AgentProbe?
The primary target audience includes AI Engineering Teams at Startups, Solo Developers Shipping AI Agents, Voice AI Companies, Customer Support AI Teams. 79% of organizations deployed AI agents in 2025. Gartner predicts 40% of enterprise software will embed agents by end of 2026. But evals adoption lags observability (52% vs 89% per LangChain data). Anthropic published its agent eval guide in January 2026, signaling that even model providers see testing as an unsolved problem. The tooling gap between "agents can do things" and "we can verify agents do things correctly" is the biggest unaddressed risk in AI infrastructure.
Similar Ideas
Related Market Trends
Agentic AI market at $10.9B in 2026, projected $57.4B by 2031. Funding surged 143% YoY in Q1 2026. Gartner: 40% of enterprise apps to embed agents by year-end.
Big 5 committed $660-690B capex for 2026 (nearly double 2025). 75% of spend directly on AI infrastructure.
MCP is the universal AI connectivity standard. 2026 roadmap: OAuth 2.1 enterprise auth, horizontal scaling, governance maturation.
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