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This article shares a prompt and practical guidelines for improving the token efficiency of LLM agent harnesses, based on lessons learned at Cursor, aiming to reduce costs without sacrificing task quality.
WebCMD provides memory for browser agents like Chrome, saving site paths to avoid repeating mistakes and reducing token waste. It was tested on Reddit and ranked as the most accurate and cheapest per task in BU Bench V1.
Introducing Claude Opus 5.5, the first model in the Claude 5.5 family, which performs at the level of Claude Fable 5.1 but costs 40% less to run with increased rate limits.
An analysis of 847 AI agent runs reveals that larger context windows cause performance drops due to attention cliffs, and Synap is presented as a tool to efficiently manage context and reduce token usage.
NVIDIA's SoL-Pi is an open-source agent harness that reduces token usage by 35-64% and API costs by 50-54% by automating the inspection and refactoring of AI workflows for efficiency.
The paper introduces SoL-Pi, an automated harness discovery method that reduces token usage by nearly half and cuts API costs by about a third while maintaining performance on benchmarks using models like GPT-5.6 Sol and Opus 5.
SoL-Pi introduces a method for recursively scaling auto-research loops in coding agents, achieving significant token and cost reductions while maintaining performance on benchmarks.
The post describes an evaluation of GPT-6 Astra across six agent harnesses on 29 agentic tasks, showing similar success rates but token usage varying by 3–5x on failures.
OdoBot is a novel web-agent architecture that uses application behavior modeling to reduce token consumption and improve task success rates, outperforming agents like Agent-E and WebVoyager on the Canvas LMS.
Agent RCA Bench evaluation found that GreptimeDB's SQL/PromQL interface produced 40% fewer wrong diagnoses and used ~48% fewer input tokens than Prometheus/Loki/Tempo wrappers across six AI models.
The article discusses whether internal documentation should be written for human readability or optimized for AI agent efficiency, exploring trade-offs and advocating for context-specific formatting.
The author open-sources a 'lean-thinking' skill for AI coding agents that reduces token usage by up to 35% while maintaining quality, based on personal testing with Claude and Codex models.
Eggshell is a local memory tool for AI agents that stores work results and evidence to reduce repeated investigation and save tokens, without requiring LLM calls for memory organization.
This article measures and compares the token efficiency of four web search APIs—Brave Search, Tavily, Exa, and Firecrawl—for AI agent contexts, concluding that Firecrawl is the most efficient for reducing context window bloat.
The article discusses the inefficiency of using subagents in AI models, suggesting that direct message exchange between sessions might be more effective, based on observations about GPT-6 Astra's token usage in Codex.
GPT-6-Astra takes first place on MathArena with a 90% expected performance and high token efficiency.
Google's update to Gemini 3.7 Flash introduces video token reduction by up to 88%, lowering costs significantly, though accuracy improvements are questionable.
Anthropic has released Fable 5.1, an updated Claude model that excels in coding, writing, and knowledge work, with improved speed, token efficiency, and business readiness.
Gemini now offers agentic video understanding, enabling iterative navigation and adaptive processing to reduce token usage and costs while boosting accuracy in video analysis.
Google DeepMind introduces agentic video understanding for Gemini models, reducing token consumption by up to 88% and improving accuracy in video analysis.