Fixing token latency in sequential agent loops and This "Parallel Tool Calling fix" worked well.
Summary
A fix for token latency in sequential agent loops is presented, where parallel tool calling improves performance in LLM agent systems.
Similar Articles
Towards Direct Latent-Space Synthesis for Parallel Branches in LLM-Agent Workflows
Introduces Parallel-Synthesis, a framework that enables direct consumption of KV caches from parallel worker agents, reducing time-to-first-token by 2.5x–11x while maintaining or improving performance on agentic tasks.
Stateful Inference for Low-Latency Multi-Agent Tool Calling
This paper presents a stateful inference architecture for multi-agent tool calling that reuses KV cache across turns and employs speculative decoding, achieving 2.1x-4.2x speedup over vLLM and SGLang on agentic workflows.
Subagents Account for Most Token Costs in Long Agent Runs: Fixes That Cut Usage 70 to 90 Percent in Practice
The article analyzes a 2026 paper by Bai et al. showing that subagents and context bloat cause token costs in long agent runs to be ~1000x higher than chat, and presents three practical fixes (PLAN.md, read budget, out-of-band notes) that reduce token usage by 70-90%.
New AI Agent Architecture to fix LLM deviations and token costs
BotCircuits Agent is an open-source framework that introduces a Workflow-native Agent Loop architecture, splitting deterministic state-machine navigation from targeted LLM execution to reduce deviations and token costs.
Never waste a token (15 minute read)
A technical blog post explaining how to avoid wasting LLM tokens by placing a durable buffer between the agent and the provider, enabling recovery from process crashes without re-fetching already-generated tokens.