Startup
Confident AI
AI quality platform to standardize evals and observability across the organization
Confident AI is an AI quality platform for enterprise teams to standardize evals and observability. It builds and maintains the open-source DeepEval evaluation framework and DeepTeam red-teaming framework. For agents it tests tool selection, arguments and full trajectories, and simulates multi-turn conversations. It blocks releases that fail evals or red-team checks and offers an MCP server and agent skills.
Neighbours in the directory
San Francisco, CA · Est. 2022
The de-facto standard toolkit for building LLM applications and agents.
LangChain makes the LangChain framework and LangGraph, the low-level orchestration standard for stateful, controllable agents, plus the LangSmith platform for tracing, evaluating and monitoring them in production.
~$75M raised
San Francisco, CA · Est. 2023
Enterprise-grade evals, data and AI gateway.
Braintrust turns prompts and agent traces into testable datasets and CI, plus an inference gateway — helping teams measure whether harness changes actually improve agent quality.
Berkeley, CA · Est. 2020
Observability and evaluation for AI — makers of OSS Phoenix.
Arize traces and evaluates LLM and agent systems in production; their open-source Phoenix tracer is a common instrumentation choice for agent harnesses that need to be debuggable.
Connections
Relations from the Harness Hub knowledge graph, as charted in the State of Harness reports.
Maintains · 1
- DeepEvalProject
Built on · 1
- Model Context ProtocolProject
Similar entries · 1
- Weights & Biases WeaveStartup
Sources
Verified Oct 29, 2026