Most Retrieval-Augmented Generation (RAG) and AI agent
errors are not Large Language Model (LLM) model failures — they are
architecture and workload failures.
This editorial short report summarizes the most common
pitfalls and outlines practical mitigation strategies based on two years of
developing ☸️SAIMSARA, a Systematic, AI-powered Medical Scientific
Article Review Agent (saimsara.com).
Common Architectural Pitfalls
- Prompt Overload: Prompts that combine many rules, constraints, and strict
formatting requirements consume a disproportionate share of the model’s
attention, leaving insufficient capacity for reliable data processing.
- Excessive Batch Size: Large batches amplify error probability through
cumulative effects, including skipped items, cross-item interference, and
degradation toward the end of long sequences.
- Oversized Input Items: Long text passages, dense token sequences, or
high-resolution images increase per-item processing cost and reduce
overall system stability.
- Speed-Optimized Model Selection: LLM optimized primarily for throughput often
lack the step-by-step discipline required for multi-stage reasoning tasks,
leading to skipped reasoning steps and structural output errors.
Practical Mitigation Strategies
Error rates can be significantly reduced by aligning
system workload with model capacity:
- Use a stronger or more deliberative LLM for complex, multi-step tasks
- Simplify prompts by reducing rules, exceptions, and implicit
assumptions
- Reduce batch size to limit cumulative cognitive LLM load
- Decrease item size (characters, tokens, pixels) to improve per-item
reliability
Editorial Conclusion
These interventions do not eliminate errors entirely,
but they move agentic AI systems into a stable operating regime. The key
insight is that robustness in RAG and agentic AI is primarily a systems
engineering challenge, not a model selection problem.
As these systems scale, architectural discipline -rather
than marginal gains in model performance- will determine reliability and
reproducibility in real-world applications.
No Conflict of Interest