AI Launch Profile
AgentX Agent Evaluation Framework
AgentX launched an AI-agent evaluation workflow for building test suites, tracing failures, comparing models on quality, cost, and latency, and suggesting fixes before production deployment.
At a glance
Launch Snapshot
- Company
- AgentX
- Launch date
- June 22, 2026
- Launch type
- Not classified
- Category
- AI Agents, AI Developer Tools
- Audience
- AI product teams, agent developers, engineers, and platform teams that need repeatable pre-deployment evaluation, tracing, comparison, and multi-agent orchestration workflows.
- Pricing
- AgentX lists a $0 platform tier with 200 one-time credits. Paid builder access starts at $49/month or $490/year with 5,000 monthly credits; additional credits are $10 per 1,000, and full Enterprise evaluation is custom-priced.
- Free plan
- Yes
- API
- Not publicly confirmed
- Open weights/source
- Not publicly confirmed
Launch Context
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Verification & Sources
- Status
- Verified
- Source links
- 4
- Freshness
- Verified July 16, 2026
- Last verified
- July 16, 2026
- Last updated
- July 9, 2026
Key source checks
Suggest a correction
Kingy Launch Score
7.2 / 10 · Solid
One earned credibility score, computed from cited evidence — not a placeholder. How the Kingy Launch Score works
Why this score
- Source & verification 7.5/10 — An official launch article, site, and pricing pages plus a public Python SDK repository — dated, single-vendor sourcing. agentx.so
- Product evidence 8.0/10 — A public Python SDK repository plus pricing and product pages make the surface inspectable in code. github.com
- Significance & novelty 6.0/10 — Test suites, tracing, and cross-model comparison packaged like CI/CD for agents — useful in an active evaluation space. agentx.so
- Traction signals not scored — insufficient sourced evidence
- Offer clarity 7.0/10 — An official pricing page documents plans; credit economics still warrant validation. agentx.so
Evidence checked 2026-07-10
Kingy AI Take
AgentX launched an agent-evaluation framework that builds test suites, traces failures, compares models on quality, cost, and latency, and suggests fixes before deployment, shipping with a public Python SDK (github.com/AgentX-ai/agentx-python). AI Product Teams and Agent Developers who want CI/CD-style checks for agents are the fit. Because it acts as an LLM judge, teams should validate judge reliability, benchmark design, and credit economics against known cases before trusting its verdicts.
Who it is for
AI product teams, agent developers, engineers, and platform teams that need repeatable pre-deployment evaluation, tracing, comparison, and multi-agent orchestration workflows.
Source-backed record