NASA’s new dark energy space telescope can also detect killer asteroids
technologyreview.com·2h ago
AI Research
arXiv CS.AI·May 26·5 min read
Toward Reliable Design of LLM-Enabled Agentic Workflows: Optimizing Latency-Reliability-Cost Tradeoffs
TL;DR
AI systems face challenges in balancing latency (delay), reliability (consistency), and cost in workflows that use large language models (LLMs). A new performance model was developed to optimize these tradeoffs, introducing a water-filling token allocation policy.
✦ Why It Matters
Engineers can apply these models to design more efficient and reliable AI workflows that balance performance and cost.
Key Takeaways
- Utilize performance models to assess tradeoffs in AI workflows.
- Implement water-filling token allocation for resource optimization.
- Characterize workflow reliability using shadow prices.
- Balance latency, reliability, and cost in LLM-enabled systems.
Full Summary
Modern AI systems often integrate multiple agents, including those powered by large language models (LLMs) and traditional computational modules. This research addresses the critical tradeoffs between latency, reliability, and cost in workflows that utilize LLMs.
The authors developed performance models that quantify the relationship between computational effort and output quality, particularly for LLM agents, using a parametric exponential reliability function. They introduced a water-filling token allocation policy to optimize resource distribution under latency and cost constraints.
Key findings include characterizing optimal workflow reliability through shadow prices, which can guide engineers in making informed design decisions. This work provides a structured approach to enhance the reliability of LLM-enabled workflows while managing costs and delays.