Agentic AI and tool-calling debates are heating up. Real experiments are surfacing progress toward autonomy, but practical autonomy still fights with tool-calling hiccups and thinking pitfalls [1].
What’s delivering autonomy in practice:
• Spine AI’s Spine Canvas: a visual workspace to think across 300+ AI models and agents, with branches and implicit context passing between connected blocks and easy model swaps [2].
• The Browser Arena: run and compare multiple autonomous browser-agents side-by-side, with metrics like cost and speed [3].
Tooling and code execution offer sharper autonomy rather than brute-force prompts:
• Model Context Protocol (MCP) — a code-based tool approach from Anthropic that treats tools as files in a sandbox, cutting token load and data shuttling [4].
• The rise of MCP-enabled tooling points to more efficient workflows, where agents write and execute code to manage tools and data rather than bloating prompts [4].
Tool-calling hiccups and thinking patterns show where autonomy stumbles:
• Kimi K2 thinking highlights that tool calling within thinking can derail answers—interleaved thinking is a common pain point and still requiring fixes [5].
Case studies show mixed but meaningful gains in perception of autonomy:
• Qwen3-VL shines with the Zoom-in Tool, boosting image-recognition accuracy when tools help zoom into details [6].
Closing thought: autonomy is advancing, but robust tool design and debugging remain essential before we trust agents to govern themselves end-to-end.
References
Ask HN: What are most up-to-date LLM Benchmarks for Agentic Coding
Hacker News user seeks current benchmarks comparing LLMs on speed, quality, cost, for coding and tool use
View sourceVisual workspace unites 300+ models; blocks and branching enable cross-model thinking, context sharing, and multi-LLM collaboration for founders and teams.
View sourceShow HN: Run and Compare multiple autonomous browser-agents side-by-side
A tool to run and compare several browser-based agents side-by-side, with metrics like cost and speed to judge model performance.
View sourceCode execution with MCP: Building more efficient agents - while saving on tokens
Discusses MCP code execution for LLMs, reducing token use and data transfer by treating tools as code APIs in sandbox
View sourcePSA Kimi K2 Thinking seems to currently be broken for most agents because of tool calling within it's thinking tags
Discusses K2 Thinking tool calling bug; interleaved thinking concerns; compares to other models; opinions on fixes.
View sourceQwen3-VL works really good with Zoom-in Tool
Discusses Qwen3-VL's zoom_in tool improving image recognition; compares models; endorses tool usage; notes limitations and policy constraints.
View source