"The agent runs a loop: THINK → ACT → OBSERVE → REPEAT until I have enough to answer."
The ReAct loop in your EvalAgent is intriguing, but isn't there a risk of it getting stuck in an infinite loop if it continually finds data that doesn't fully resolve the issue? How do you cap the number of iterations to prevent it from spiraling out of control? It seems like that could be a potential snag, especially when working with ambiguous or partially complete data. Having run into similar issues, I know that setting a sensible upper limit can save a lot of headache.
AI Systems Engineer. Multi-agent orchestration, RAG, LLM evals.
Contributor to Mastra AI (22k★, YC-backed). Building TraceMind —
open-source LLM observability.
Good question — max_iterations is the primary guard.
The loop has a hard ceiling of 8 iterations. After 8 tool calls with no ANSWER:, the agent returns "Analysis incomplete after 8 steps" and saves whatever it found so far. The investigation doesn't spiral — it terminates and reports partial findings.
The more interesting failure mode you're pointing at is getting stuck in a reasoning rut — the agent keeps calling the same tool with slightly different inputs because each result gives enough signal to continue but not enough to conclude.
I handle this with two mechanisms:
Context accumulation — every tool result is appended to the working context. The LLM can see its own prior calls, which prevents pure repetition (calling search_similar_failures twice
with identical inputs gives identical output — the model learns this after 1-2 tries).
Tool diversity pressure — the system prompt instructs the agent to use different tools to gather diverse signal rather than repeating the same one. In practice, 8 iterations is more than
enough for any investigation I've run — the average is 4-5 tool calls to reach a specific root cause.
What I'd do for production at scale: add a tool-call deduplication check (if tool+input_hash was called before, skip it) and a confidence threshold (if analyze_failure_pattern returns high confidence, exit early). Neither is implemented yet — worth adding.
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"The agent runs a loop: THINK → ACT → OBSERVE → REPEAT until I have enough to answer."
The ReAct loop in your EvalAgent is intriguing, but isn't there a risk of it getting stuck in an infinite loop if it continually finds data that doesn't fully resolve the issue? How do you cap the number of iterations to prevent it from spiraling out of control? It seems like that could be a potential snag, especially when working with ambiguous or partially complete data. Having run into similar issues, I know that setting a sensible upper limit can save a lot of headache.
Good question — max_iterations is the primary guard.
The loop has a hard ceiling of 8 iterations. After 8 tool calls with no ANSWER:, the agent returns "Analysis incomplete after 8 steps" and saves whatever it found so far. The investigation doesn't spiral — it terminates and reports partial findings.
The more interesting failure mode you're pointing at is getting stuck in a reasoning rut — the agent keeps calling the same tool with slightly different inputs because each result gives enough signal to continue but not enough to conclude.
I handle this with two mechanisms:
Context accumulation — every tool result is appended to the working context. The LLM can see its own prior calls, which prevents pure repetition (calling search_similar_failures twice
with identical inputs gives identical output — the model learns this after 1-2 tries).
Tool diversity pressure — the system prompt instructs the agent to use different tools to gather diverse signal rather than repeating the same one. In practice, 8 iterations is more than
enough for any investigation I've run — the average is 4-5 tool calls to reach a specific root cause.
What I'd do for production at scale: add a tool-call deduplication check (if tool+input_hash was called before, skip it) and a confidence threshold (if analyze_failure_pattern returns high confidence, exit early). Neither is implemented yet — worth adding.