Agency AI offers AgentOps.ai, a platform designed for the development of AI agents in a controlled environment. This solution addresses the challenges of reliability and scalability in AI agent deployment, enabling developers to create robust applications efficiently.
Funding
$2.6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Developing and deploying AI agents presents challenges in ensuring reliability, safety, and scalability, often leading to unexpected failures and hindering efficient application development. Existing tools lack comprehensive observability, making it difficult to debug and monitor agent behavior in production environments.
Solution
AgentOps.ai by Agency AI provides a platform for testing, debugging, and deploying AI agents and Large Language Model (LLM) applications. The platform offers observability and monitoring capabilities, allowing developers to visualize agent behavior and identify limitations. By integrating with various agent frameworks and LLMs, AgentOps enables users to track traces, manage sessions, and gain insights into agent performance through a user-friendly dashboard. This helps teams accelerate their development cycles and build more robust and reliable AI agents.
Target Audience
The primary target audience includes AI developers, machine learning engineers, and enterprises building and deploying AI agents and LLM applications.
Features
- Integration with popular AI agent frameworks such as OpenAI Agents SDK, CrewAI, AG2, AutoGen, LangChain, and LiteLLM
- Real-time observability and monitoring of AI agent behavior
- Comprehensive dashboard for visualizing sessions, traces, and LLM calls
- Custom trace creation using the `@trace` decorator
- Manual trace management for advanced use cases
- Session drilldown with detailed debugging information, including SDK versions and execution times
- Session waterfall view displaying LLM calls, action events, tool calls, and errors
- Support for Claude Computer Use from Anthropic
- Model-agnostic design, compatible with various AI agent frameworks and tools