Langfuse and Helicone work well for traditional LLM operations, but AI agents are different. We discovered that AI agents require fundamentally different tooling, here are some examples.
First, while LLMs simply respond to prompts, agents often get stuck in behavioral loops where they repeat the same actions; to address this, we built a graph visualization that automatically detects when an agent reaches the same state multiple times and groups these occurrences together, making loops immediately visible.
Second, our evaluations are much more tailored for AI Agents. LLM ops evaluations usually occur at a per prompt level (i.e hallucination, qa-correctness) which makes sense for those use cases, but agent evaluations are usually per session or run. What this means is that usually a single prompt in isolation didn’t cause an issue but some downstream memory issue or previous action caused this current tool to fail. So, we spent a lot of time creating a way for you to create a rubric. Then, to evaluate the rubric (so that there isn’t context overload) we created an agentic pipeline which has tools like viewing rubric examples, ability to zoom “in and out” of a session (to prevent context overload), referencing previous examples, etc.
Third, time traveling and clustering of similar responses. LLM debugging is straightforward because prompts are stateless and are independent from one another, but agents maintain complex state through tools, context, and memory management; we solved this by creating “time travel” functionality that captures the complete agent state at any point, allowing developers to modify variables like context or tool availability and replay from that exact moment and then simulate that 20-30 times and group together similar responses (with our clustering alg).
Fourth, agents exhibit far more non-deterministic behavior than LLMs because a single tool call can completely change their trajectory; to handle this complexity, we developed workflow trajectory clustering that groups similar execution paths together, helping developers identify patterns and edge cases that would be impossible to spot in traditional LLM systems.
Langfuse and Helicone work well for traditional LLM operations, but AI agents are different. We discovered that AI agents require fundamentally different tooling, here are some examples.
First, while LLMs simply respond to prompts, agents often get stuck in behavioral loops where they repeat the same actions; to address this, we built a graph visualization that automatically detects when an agent reaches the same state multiple times and groups these occurrences together, making loops immediately visible.
Second, our evaluations are much more tailored for AI Agents. LLM ops evaluations usually occur at a per prompt level (i.e hallucination, qa-correctness) which makes sense for those use cases, but agent evaluations are usually per session or run. What this means is that usually a single prompt in isolation didn’t cause an issue but some downstream memory issue or previous action caused this current tool to fail. So, we spent a lot of time creating a way for you to create a rubric. Then, to evaluate the rubric (so that there isn’t context overload) we created an agentic pipeline which has tools like viewing rubric examples, ability to zoom “in and out” of a session (to prevent context overload), referencing previous examples, etc.
Third, time traveling and clustering of similar responses. LLM debugging is straightforward because prompts are stateless and are independent from one another, but agents maintain complex state through tools, context, and memory management; we solved this by creating “time travel” functionality that captures the complete agent state at any point, allowing developers to modify variables like context or tool availability and replay from that exact moment and then simulate that 20-30 times and group together similar responses (with our clustering alg).
Fourth, agents exhibit far more non-deterministic behavior than LLMs because a single tool call can completely change their trajectory; to handle this complexity, we developed workflow trajectory clustering that groups similar execution paths together, helping developers identify patterns and edge cases that would be impossible to spot in traditional LLM systems.
This makes sense. We'll look into this some more, will be making a decision next couple days :)
Good luck!