Hey folks, been experimenting with AgentGPT for automating some internal tooling workflows. Overall, it's pretty slick for chaining tasks, but I've hit a snag that feels like a classic infinite loop or deadlock scenario.
My agent is supposed to `analyze customer feedback from the last week`. It fetches the data successfully, but then it gets stuck in a cycle where it keeps repeating analysis steps without progressing. In the logs, I see it alternating between "Analyzing sentiment" and "Categorizing feedback topics" over and over, never calling the next task (which is `generate summary report`). It's like it's stuck in a `while(1)` with no exit condition 😅.
My setup is pretty standard:
```yaml
Task: analyze_customer_feedback
Sub-tasks:
- fetch_feedback_from_api
- perform_sentiment_analysis
- extract_key_topics
- compile_summary
```
Has anyone else run into this? I'm trying to think about it from a systems perspective:
* Is the agent failing to recognize a "task completion" state because the output format of one step is malformed?
* Could it be a token/context window issue where the intermediate results are too verbose, causing it to lose track of the original goal?
* I'm wondering if there's a way to inject a "timeout" or a max-iteration guardrail, similar to a `SIGALRM` in a process.
I'd love to hear if anyone has debugged similar loops. Specifically:
* What logging or tracing did you find most useful in AgentGPT to see the agent's "thought" process?
* Did you solve it by tweaking the prompt, breaking the task down differently, or using a specific agent model?
* Are there any EBPF-inspired analogies here? Monitoring the "system calls" (API calls) and state transitions of the agent?
System calls per second matter.
Seen this a dozen times. It's almost never the agent's logic, it's your data.
> failing to recognize a "task completion" state
Bingo. Your sub-tasks are too vague. "Extract key topics" with no defined list or confidence threshold? The agent will keep trying to 'perfect' its extraction, looping forever. It needs a concrete success metric.
Define a clear output schema for each step. Force sentiment_analysis to return a structured JSON with fields like "sentiment_score" and "processed_count". The loop stops when it meets the spec. Without that, it's just wandering in a semantic desert.
CRM is a necessary evil
You're hitting a classic agent completion criteria problem. User156 is correct about the schema, but your YAML snippet shows the deeper issue: your sub-tasks lack explicit, machine-evaluable success conditions. "perform_sentiment_analysis" and "extract_key_topics" are ambiguous verbs to the agent. Without a defined output structure and a validation step, the agent will recursively attempt to satisfy an undefined goal.
I'd instrument it with validation gates. For example, modify your task chain so each step must produce a JSON matching a schema you predefine, and include a subsequent "validate_[step]_output" task. The loop often breaks because the agent's internal evaluation of whether "extract_key_topics" is complete is fuzzy; force it to be binary by checking against a schema. Also, check the actual token count of the data being passed between steps. If the fetched feedback is massive, the agent might be truncating context and thus never receiving a "complete" signal from its own prior step, causing it to restart the analysis.
Trust but verify.