>"Google knows asking agents to navigate GUIs designed for humans is ridiculous. Microsoft might not."
The article argues that the command line interface (CLI) is experiencing a resurgence due to the limitations of graphical user interfaces (GUIs) for autonomous agents. GUIs, once lauded for reducing cognitive load, have become cluttered and inconsistent, hindering agent efficiency. Agents struggle with GUIs, requiring repetitive image analysis and complex actions. CLIs provide a universal and efficient interface for agents to interact with software. Google's release of gws, a CLI for Google Workspace, exemplifies this trend. The author predicts a "SaaSpocalypse" where software providers scramble to develop CLIs to remain competitive.
The article argues that the command line interface (CLI) is experiencing a resurgence due to the limitations of graphical user interfaces (GUIs) for autonomous agents. GUIs, once lauded for reducing cognitive load, have become cluttered and inconsistent, hindering agent efficiency. Agents struggle with GUIs, requiring repetitive image analysis and complex actions. CLIs provide a universal and efficient interface for agents to interact with software. Google's release of gws, a CLI for Google Workspace, exemplifies this trend. The author predicts a "SaaSpocalypse" where software providers scramble to develop CLIs to remain competitive.
Three vendors – Cohesity, ServiceNow, and Datadog – have partnered to create a recoverability service designed to address the risks associated with agentic AI (AIOps). The service aims to restore systems to a "trusted state" by identifying and recovering files and data corrupted by AI errors or malicious attacks.
The companies anticipate increased adoption of agentic AI for system operation but recognize the potential for errors and vulnerabilities. Their solution focuses on preserving immutable snapshots of AI environments, enabling point-in-time recovery of agents, data, and infrastructure components, including vector stores and agent memory.
ServiceNow and Datadog provide control and observability platforms to detect anomalies, triggering API-driven restorations when problems are identified. This offering competes with Rubrik's similar tool and native rollback capabilities from vendors like Cisco. Gartner predicts a significant increase in the integration of task-specific agents in enterprise applications, while Forrester emphasizes the need for guardrails and strong oversight in agentic AI development.
The companies anticipate increased adoption of agentic AI for system operation but recognize the potential for errors and vulnerabilities. Their solution focuses on preserving immutable snapshots of AI environments, enabling point-in-time recovery of agents, data, and infrastructure components, including vector stores and agent memory.
ServiceNow and Datadog provide control and observability platforms to detect anomalies, triggering API-driven restorations when problems are identified. This offering competes with Rubrik's similar tool and native rollback capabilities from vendors like Cisco. Gartner predicts a significant increase in the integration of task-specific agents in enterprise applications, while Forrester emphasizes the need for guardrails and strong oversight in agentic AI development.
Amazon outages linked to rapid AI integration were discussed in a recent internal meeting. AI glitches in algorithms managing infrastructure caused disruptions (e.g., issues viewing product details, Freevee streaming). While Amazon is aggressively using AI, sources say the speed is creating instability. The company is focused on reliability amidst growing AI competition. Amazon declined to comment specifically but affirmed commitment to customer experience
This essay argues that the economics of context engineering expose a gap in the Brynjolfsson-Hitzig framework that changes its practical implications: for how enterprises build with AI, which firms centralize successfully, and whether the AI economy will be as centralized as their framework suggests. It explores how the cost and effort required to make knowledge usable by AI—context engineering—creates a bottleneck that prevents complete centralization, preserving the importance of local knowledge and human judgment. The article discusses the implications for SaaS companies, knowledge workers, and the future of work in an AI-driven economy, predicting that those who invest in context engineering capabilities will see the highest ROI.
GitHub Agentic Workflows are built with isolation, constrained outputs, and comprehensive logging. Learn how our threat model and security architecture help teams run agents safely in GitHub Actions.
This post explains how we built Agentic Workflows with security in mind from day one, starting with the threat model and the security architecture that it needs. It details the defense in depth approach using substrate, configuration, and planning layers, emphasizing zero-secret agents through isolation and careful exposure of host resources. It also highlights the staging and vetting of all writes using safe outputs, and comprehensive logging for observability and future information-flow controls.
This post explains how we built Agentic Workflows with security in mind from day one, starting with the threat model and the security architecture that it needs. It details the defense in depth approach using substrate, configuration, and planning layers, emphasizing zero-secret agents through isolation and careful exposure of host resources. It also highlights the staging and vetting of all writes using safe outputs, and comprehensive logging for observability and future information-flow controls.
LLM coding assistance is moving beyond traditional IDE plugins to powerful, terminal-native agents. These agents, like the new open-source **OPENDEV**, operate directly within a developer's workflow – managing code, builds, and deployments with increased autonomy.
OPENDEV tackles key challenges of autonomous AI, like safety and context management, with a unique architecture featuring specialized AI models, separated planning & execution, and efficient memory. It intelligently manages information by prioritizing relevant context and learning from past sessions, preventing errors and "instruction fade."
OPENDEV provides a secure and adaptable foundation for terminal-first system, paving the way for robust and autonomous software engineering.
OPENDEV tackles key challenges of autonomous AI, like safety and context management, with a unique architecture featuring specialized AI models, separated planning & execution, and efficient memory. It intelligently manages information by prioritizing relevant context and learning from past sessions, preventing errors and "instruction fade."
OPENDEV provides a secure and adaptable foundation for terminal-first system, paving the way for robust and autonomous software engineering.
Researchers have identified a neural network associated with adaptive mentalization – the ability to adjust how we infer others’ intentions and beliefs based on their behavior. Using computational modeling and fMRI, they found activity and connectivity within brain regions (including the temporoparietal junction) tracked participants’ ability to update beliefs about opponents' strategic sophistication in a game setting. This neural signature could potentially be used to assess mentalization capabilities in both healthy individuals and those with brain disorders.
An account of how a developer, Alexey Grigorev, accidentally deleted 2.5 years of data from his AI Shipping Labs and DataTalks.Club websites using Claude Code and Terraform. Grigorev intended to migrate his website to AWS, but a missing state file and subsequent actions by Claude Code led to a complete wipe of the production setup, including the database and snapshots. The data was ultimately restored with help from Amazon Business support. The article highlights the importance of backups, careful permissions management, and manual review of potentially destructive actions performed by AI agents.
The article details “autoresearch,” a project by Karpathy where an AI agent autonomously experiments with training a small language model (nanochat) to improve its performance. The agent modifies the `train.py` file, trains for a fixed 5-minute period, and evaluates the results, repeating this process to iteratively refine the model. The project aims to demonstrate autonomous AI research, focusing on a simplified, single-GPU setup with a clear metric (validation bits per byte).
* **Autonomous Research:** The core concept of AI-driven experimentation.
* **nanochat:** The small language model used for training.
* **Fixed Time Budget:** Each experiment runs for exactly 5 minutes.
* **program.md:** The file containing instructions for the AI agent.
* **Single-File Modification:** The agent only edits `train.py`.
* **Autonomous Research:** The core concept of AI-driven experimentation.
* **nanochat:** The small language model used for training.
* **Fixed Time Budget:** Each experiment runs for exactly 5 minutes.
* **program.md:** The file containing instructions for the AI agent.
* **Single-File Modification:** The agent only edits `train.py`.
Google has released a new command-line interface for Google Workspace apps, designed to make it easier for AI agents like OpenClaw to interface with Google apps like Docs, Drive, and Gmail. The tool offers over 100 Agent Skills to simplify agent actions and supports integrations with other AI agents beyond OpenClaw. While published by Google, it's not an officially supported product, so use it at your own risk.
