Evaluating limits of processing agents: handling large datasets without heavy reliance on tools in CrewAI

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I am developing an agent using CrewAI as part of a research project aimed at evaluating the real capabilities of autonomous agents.

The goal is to design an agent that can:

  • receive a complete dataset (structured data, not just plain text)

  • process it end-to-end

  • generate a full structured output that can be consumed by another agent

So far, I have tested the agent with datasets of around 300 records, and it is able to process them successfully. However, I have not yet scaled beyond that, and I expect potential limitations to appear as the dataset size increases.

Current challenges:

  1. Uncertain scalability

    • The agent works with smaller datasets (~300 records), but it is unclear how far this can scale

    • There is a risk of truncation or information loss with larger inputs

  2. Handling large outputs

    • When generating full outputs, the agent may hit response size limits

    • This affects passing structured data to downstream agents

  3. FileWriterTool limitations

    • I tried using FileWriterTool to persist results

    • It does not seem well-suited for large structured datasets

    • Output is sometimes incomplete or inconsistent

  4. Framework design considerations

    • I understand CrewAI is designed for agents to delegate tasks to tools

    • However, the purpose of this experiment is to evaluate how much the agent can process independently before relying on external tools

Research context:

This work aims to explore:

  • the real processing limits of an agent

  • its ability to handle structured datasets without fragmentation

  • how performance degrades as data volume increases

Question to the community:

Is there any way within CrewAI to:

  • allow an agent to process larger datasets more efficiently without relying heavily on external tools?

  • prevent truncation when generating large outputs?

  • properly handle structured data outputs so they can be passed reliably to other agents?

Any recommendations on strategies, configurations, or design patterns would be highly appreciated.