@Saboo_Shubham_: This is the WAY...Speculative Programmatic Tool Calling for Agent Harness. While the LLM is streaming tokens, it specul…

X AI KOLs Timeline Tools

Summary

Describes a speculative programmatic tool calling method for agent harnesses, where LLMs queue up tool calls during token streaming to act as futures in code execution.

This is the WAY...Speculative Programmatic Tool Calling for Agent Harness. While the LLM is streaming tokens, it speculates and queues up tool calls in the partially-generated code that act as futures when the actual code is executed. https://t.co/sFUEVo2sFH
Original Article
View Cached Full Text

Cached at: 08/26/26, 11:26 PM

This is the WAY…Speculative Programmatic Tool Calling for Agent Harness.

While the LLM is streaming tokens, it speculates and queues up tool calls in the partially-generated code that act as futures when the actual code is executed. https://t.co/sFUEVo2sFH

Similar Articles

Speculative Programmatic Tool Calling (12 minute read)

TLDR AI

The article proposes speculative programmatic tool calling (sPTC), a technique to optimize tool calls in AI harnesses by overlapping execution with token generation, reducing latency inspired by speculative execution in CPUs and LLMs.

@janehu07: https://x.com/janehu07/status/2058359677843599494

X AI KOLs Timeline

This learning note introduces the concept of an agent harness as the infrastructure layer around an LLM, proposing the ETCLOVG taxonomy (Execution, Tooling, Context, Lifecycle, Observability, Verification, Governance) and demonstrating its application through a coding agent case study.

Speculative Macro Commit for Faster Tool-Using Agents

arXiv cs.AI

This paper introduces Speculative Macro Commit (SMC), a two-tier agent system that reduces latency in tool-using LLM agents by speculatively executing future action chains and committing them when matched, achieving speedups over sequential execution.

Harnessing LLM Agents with Skill Programs

Hugging Face Daily Papers

HASP is a framework that upgrades agent skills into executable program functions acting as guardrails, enabling direct intervention in LLM agent loops and improving performance on complex tasks like web-search, math reasoning, and coding.