惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

F
Fortinet All Blogs
有赞技术团队
有赞技术团队
量子位
N
Netflix TechBlog - Medium
博客园 - 叶小钗
博客园 - 三生石上(FineUI控件)
Google DeepMind News
Google DeepMind News
aimingoo的专栏
aimingoo的专栏
GbyAI
GbyAI
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Blog — PlanetScale
Blog — PlanetScale
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
月光博客
月光博客
Martin Fowler
Martin Fowler
Y
Y Combinator Blog
宝玉的分享
宝玉的分享
博客园 - 司徒正美
云风的 BLOG
云风的 BLOG
V
Visual Studio Blog
V
V2EX
IT之家
IT之家
L
LangChain Blog
大猫的无限游戏
大猫的无限游戏
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More

MarkTechPost

A Coding Implementation of End-to-End Brain Decoding from MEG Signals Using NeuralSet and Deep Learning for Predicting Linguistic Features Meta Introduces Autodata: An Agentic Framework That Turns AI Models into Autonomous Data Scientists for High-Quality Training Data Creation Qwen AI Releases Qwen-Scope: An Open-Source Sparse AutoEncoders (SAE) Suite That Turns LLM Internal Features into Practical Development Tools A Coding Deep Dive into Agentic UI, Generative UI, State Synchronization, and Interrupt-Driven Approval Flows Moonshot AI Open-Sources FlashKDA: CUTLASS Kernels for Kimi Delta Attention with Variable-Length Batching and H20 Benchmarks Microsoft Research’s World-R1 Uses Flow-GRPO and 3D-Aware Rewards to Inject Geometric Consistency Into Wan 2.1 Without Architectural Changes A Coding Implementation on Pyright Type Checking Covering Generics, Protocols, Strict Mode, Type Narrowing, and Modern Python Typing IBM Releases Two Granite Speech 4.1 2B Models: Autoregressive ASR with Translation and Non-Autoregressive Editing for Fast Inference Top 10 KV Cache Compression Techniques for LLM Inference: Reducing Memory Overhead Across Eviction, Quantization, and Low-Rank Methods Qwen Team Releases FlashQLA: a High-Performance Linear Attention Kernel Library That Achieves Up to 3× Speedup on NVIDIA Hopper GPUs Step by Step Guide to Build a Complete PII Detection and Redaction Pipeline with OpenAI Privacy Filter Meta FAIR Releases NeuralSet: A Python Package for Neuro-AI That Supports fMRI, M/EEG, Spikes, and HuggingFace Embeddings smol-audio: A Colab-Friendly Notebook Collection for Fine-Tuning Whisper, Parakeet, Voxtral, Granite Speech, and Audio Flamingo 3 A Coding Implementation on Document Parsing Benchmarking with LlamaIndex ParseBench Using Python, Hugging Face, and Evaluation Metrics Poolside AI Introduces Laguna XS.2 and M.1: Agentic Coding Models Reaching 68.2% and 72.5% on SWE-bench Verified How to Build Traceable and Evaluated LLM Workflows Using Promptflow, Prompty, and OpenAI OpenAI Releases Privacy Filter: A 1.5B-Parameter Open-Source PII Redaction Model with 50M Active Parameters Top 10 Physical AI Models Powering Real-World Robots in 2026 How to Build a Lightweight Vision-Language-Action-Inspired Embodied Agent with Latent World Modeling and Model Predictive Control Meet Talkie-1930: A 13B Open-Weight LLM Trained on Pre-1931 English Text for Historical Reasoning and Generalization Research Build a Reinforcement Learning Powered Agent that Learns to Retrieve Relevant Long-Term Memories for Accurate LLM Question Answering OpenMOSS Releases MOSS-Audio: An Open-Source Foundation Model for Speech, Sound, Music, and Time-Aware Audio Reasoning Meta AI Releases Sapiens2: A High-Resolution Human-Centric Vision Model for Pose, Segmentation, Normals, Pointmap, and Albedo The LoRA Assumption That Breaks in Production How to Build a Fully Searchable AI Knowledge Base with OpenKB, OpenRouter, and Llama How to Build Smarter Multilingual Text Wrapping with BudouX Through Parsing, HTML Rendering, Model Introspection, and Toy Training Top 7 Benchmarks That Actually Matter for Agentic Reasoning in Large Language Models RAG Without Vectors: How PageIndex Retrieves by Reasoning A Coding Tutorial on Datashader on Rendering Massive Datasets with High-Performance Python Visual Analytics xAI Launches grok-voice-think-fast-1.0: Topping τ-voice Bench at 67.3%, Outperforming Gemini, GPT Realtime, and More
xAI Launches /goal in Grok Build, Adding Long-Running Aut...
Michal Sutter · 2026-06-23 · via MarkTechPost

xAI shipped a new mode called /goal inside Grok Build, its terminal coding agent. The feature targets long-running, autonomous task execution. You hand the agent a larger implementation task, then step back.

Most coding sessions require back-and-forth execution and verification. You prompt, the agent acts, and you verify each step. /goal changes that loop. The agent keeps working until a task is completed and verified. Verification can mean reviewing code, inspecting webpages, or executing scripts.

The mode packages that autonomy behind one command and a visible progress checklist.

TL;DR

  • /goal runs long, autonomous tasks inside Grok Build’s terminal agent.
  • It plans an approach, builds a checklist, executes, and verifies until complete.
  • Verification can review code, inspect webpages, or execute scripts.
  • status, pause, resume, and clear steer the live run.
  • Access needs a SuperGrok or X Premium Plus subscription.

What is /goal

/goal is a mode within Grok Build, not a separate product. Grok Build is xAI’s coding agent and CLI for software engineering. A CLI is a command-line tool that runs in your terminal. It works against your local codebase, reading files and running commands.

Grok Build already ships several agent features. It has a plan mode that blocks edits until you approve a plan. It delegates larger work to specialized subagents that run in parallel. It reads your AGENTS.md, plugins, hooks, skills, and MCP servers out of the box. MCP is a protocol that connects agents to external tools and data.

Access requires a SuperGrok or X Premium Plus subscription. You install the CLI with one command:

curl -fsSL https://x.ai/cli/install.sh | bash

You then sign in with your account and invoke /goal. The mode is built for handoff. You describe one objective, and the agent owns the multi-step work.

Why Long-Running Execution Matters

An agent is given a goal, not just a single prompt. It plans steps, executes them, observes results, and replans when reality diverges. That is the observe–plan–act loop. The trade-off is observability: long runs produce many actions you must monitor. /goal answers that with a progress checklist and steering commands.

How /goal Works

You set a goal in a single line. You type it inside the Grok Build session, not your system shell. For example:

/goal Migrate the auth module to the new API

Grok Build plans an approach for that objective. It breaks the work into a progress checklist. It then starts executing the checklist items one by one.

You are not locked out during the run. You can keep sharing additional instructions as it works. When the goal is finished, the panel flips to Complete. Every checklist item then shows as checked.

The detail worth noting is verification. /goal does not stop at writing code. It continues until the task is completed and verified.

Verification, per xAI, can take three forms. The agent may review the code it produced. It may inspect webpages to confirm behavior. It may execute scripts to test the result.

This matters for autonomous runs. An agent that only edits files may report success before the change works. Building verification into the run means the agent tests its own output before it finishes.

Interactive Demo

Use Cases With Examples

Module migration is the main example. /goal Migrate the auth module to the new API covers a multi-file change. The task is long, mechanical, and testable.

Other practical handoffs fit the same shape:

  • Refactor a service, then run its test suite to confirm a green build.
  • Add an endpoint, then inspect the rendered page for runtime errors.
  • Upgrade a dependency, execute the build, and fix the breakages.
  • Port a config across files, then run a script to validate it.

Each task suits an autonomous goal better than a quick one-line edit. The payoff comes when the work spans many steps you would otherwise supervise.

Steering a Long-Running Goal

/goal adds commands to monitor and steer the run. You type them inside the Grok Build session:

/goal status     # see the live progress panel
/goal pause      # stop work, keep the goal
/goal resume     # pick back up
/goal clear      # drop the goal entirely

status surfaces the live progress panel. pause halts work but keeps the goal intact. resume continues from where it stopped. clear drops the goal entirely and starts fresh.

These controls are important for unattended runs. You can pause work or drop the goal at any point. That helps when an agent edits files and runs scripts on its own.

How /goal Compares

/goal sits among terminal coding agents that already run multi-step work. What it adds is an explicit, steerable goal object. The named lifecycle commands and the built-in verification step are the new parts.

DimensionGrok Build /goalClaude CodeOpenAI Codex CLICursor Agent Mode
Handoff modelOne-line goal objectConversational + task listPrompt with approval modeChat instruction in IDE
Progress viewAuto checklist + live panelInternal to-do listStep-by-step logInline edit stream
Steering controlsstatus / pause / resume / clearInterrupt and repromptApprove / reject stepsAccept / reject edits
VerificationRuns until completed and verifiedRuns tests when instructedRuns tests when instructedRuns tests when instructed
Where it runsTerminal (TUI, headless, ACP)TerminalTerminalIDE-integrated
AccessSuperGrok / X Premium PlusSubscription or APISubscription or APISubscription

Key Takeaways

  • /goal runs long, autonomous tasks inside Grok Build’s terminal agent.
  • It plans an approach, builds a checklist, executes, and verifies until complete.
  • Verification can review code, inspect webpages, or execute scripts.
  • status, pause, resume, and clear steer the live run.
  • Access needs a SuperGrok or X Premium Plus subscription.

Check out the Technical detailsAlso, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.

Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us

Michal Sutter

Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.