Assessing Your Agentic AI Investments

As businesses increasingly integrate large language models (LLMs) into their operations, particularly through agentic AI applications, the need for robust evaluation tools has become paramount. These tools help organizations understand how these complex systems behave, identify potential failure points, and ensure they align with business objectives.

The market for AI evaluation platforms is rapidly evolving, with vendors offering solutions that span performance tracking (sometimes called “AgentOps” or “Observability”), trust and safety controls, and comprehensive testing frameworks. Many companies are expanding across these categories as the market matures.

Top Evaluation Tools to Consider:

Here’s an overview of 13 leading AI evaluation tools that enterprises should consider:

  1. Braintrust: Designed for large-scale dataflows, Braintrust traces all interactions to pinpoint errors and provides dashboards for identifying latency, cost, or quality issues.

    • Pricing: Free plan with $10 credits; Pro plan starts at $250
    • Standout feature: Loop agent tracks behavior through multiple iterations for deeper debugging

  2. Confident AI: A cloud-based platform that extends the capabilities of open-source tools like DeepEval, offering a collaborative environment for testing and archiving results.

    • Pricing: Forever free plan with limited features; paid plans start at $200
    • Standout feature: Automated red-teaming and on-demand pen-testing

  3. DeepEval: A Pytest-native framework that enables developers to create unit tests for LLMs, checking for issues like hallucinations, drift, and knowledge retention.

    • Pricing: Open-source with Apache 2.0 license
    • Standout feature: Comprehensive suite of test modules covering various failure modes

  4. LangSmith: From the creators of LangChain, this tool tracks all steps in an agent’s workflow, providing deep debugging capabilities for complex interactions.

  5. PromptLayer: Offers prompt tracking, versioning, and evaluation features to help teams manage and optimize their LLM applications.

  6. Arthur AI: Provides observability and monitoring solutions specifically designed for generative AI applications, including drift detection and performance analysis.

  7. Weights & Biases: A popular MLOps platform that can be used to track and evaluate LLMs across training and deployment.

  8. Arize AI: Offers comprehensive model observability and monitoring capabilities for AI systems, including generative models.

  9. SuperAgent: Focuses on evaluating the safety and reliability of autonomous agents through red-teaming and adversarial testing.

  10. Helicone: Provides detailed logs and tracing for LLM applications, enabling developers to debug issues and understand agent behavior.

  11. Vellum: A prompt engineering platform with built-in evaluation features to track performance and identify areas for improvement.

  12. Manual Evaluation Platforms: Services like Scale AI or Labelbox offer human-in-the-loop evaluation capabilities for more nuanced assessments.

  13. Custom Solutions: Many enterprises build their own evaluation frameworks using open-source tools and integrating with existing monitoring infrastructure.

Key Considerations When Choosing a Tool:

  • Integration: How well does the tool integrate with your current development workflow and infrastructure?
  • Scalability: Can it handle the volume of data generated by your AI applications?
  • Features: Does it offer the specific evaluation capabilities you need (e.g., red-teaming, drift detection)?
  • Collaboration: How well does it support team workflows for testing and debugging?
  • Cost: What’s the total cost of ownership, including setup, maintenance, and usage fees?

As agentic AI continues to evolve, these evaluation tools will become increasingly essential for ensuring responsible adoption and maximizing business value.