This article explores the emerging category of AI-powered operations agents, comparing AI DevOps engineers and AI SRE agents, how cloud providers are responding, and what engineers should consider when evaluating these tools.
Late last year, startup Platform Engineering Labs made waves in the world of Infrastructure as Code (IaC) by introducing a new IaC platform, called Formae, available initially on Amazon Web Services. This week, Platform Engineering Labs‘ platform gets (beta) support from additional cloud platforms, including Google Cloud Platform, Microsoft Azure, Oracle Cloud Infrastructure, and OVHcloud. The company has also released new AI-enhanced software for managing infrastructure tooling, called the Platform for Infrastructure Builders.
The awesome collection of OpenClaw Skills. Formerly known as Moltbot, originally Clawdbot.
A single developer built a powerful search and monitoring tool for the web using a simple SQLite database and a clever bot, highlighting the potential of individual creators to tackle complex problems.
>When deployed strategically, agents can empower SREs to offload low-risk, toilsome tasks so they can focus on the most critical matters.
Agents in practice include:
* **Contextual Information:** Providing SREs with details from previously resolved incidents involving the same service, including responder notes.
* **Root Cause Analysis:** Suggesting potential origins of an issue and identifying recent configuration changes that might be responsible.
* **Automated Remediation:** Handling low-risk, well-defined issues without human intervention, with SRE review of after-action reports.
* **Diagnostic Suggestions:** Nudging SREs towards running specific diagnostics for partially understood incidents and supplying them automatically.
* **Runbook Generation:** Automatically creating and updating runbooks based on successful remediation steps, preventing recurring issues.
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Agents in practice include:
* **Contextual Information:** Providing SREs with details from previously resolved incidents involving the same service, including responder notes.
* **Root Cause Analysis:** Suggesting potential origins of an issue and identifying recent configuration changes that might be responsible.
* **Automated Remediation:** Handling low-risk, well-defined issues without human intervention, with SRE review of after-action reports.
* **Diagnostic Suggestions:** Nudging SREs towards running specific diagnostics for partially understood incidents and supplying them automatically.
* **Runbook Generation:** Automatically creating and updating runbooks based on successful remediation steps, preventing recurring issues.
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A guide to supercharging Claude Code with Skills and the Model Context Protocol (MCP), including running Claude Code in an IDE like Cursor or VS Code. It covers setting up Skills, connecting to MCP servers, and combining both for powerful workflows.
Moltbot is a self-hosted AI assistant that runs on your machines, connects to messaging platforms, performs actions, and maintains persistent memory. It was renamed from Clawdbot due to trademark concerns.
* **What it is:** Moltbot is an AI assistant designed to run locally on your machines (macOS, Windows, Linux) offering privacy and customization. It differs from cloud-based services.
* **How it works:** It connects to various messaging platforms (WhatsApp, Telegram, Slack, etc.) allowing interaction via chat.
* **Capabilities:** Moltbot can perform actions beyond answering questions – automating tasks, running scripts, scheduling jobs, browsing the web, and integrating with other services via plugins.
* **Key Feature: Persistent Memory:** Unlike many bots, Moltbot remembers past interactions, providing a tailored and consistent experience.
* **Name Change:** The project was renamed from Clawdbot to Moltbot due to trademark concerns with Anthropic’s Claude.
* **What it is:** Moltbot is an AI assistant designed to run locally on your machines (macOS, Windows, Linux) offering privacy and customization. It differs from cloud-based services.
* **How it works:** It connects to various messaging platforms (WhatsApp, Telegram, Slack, etc.) allowing interaction via chat.
* **Capabilities:** Moltbot can perform actions beyond answering questions – automating tasks, running scripts, scheduling jobs, browsing the web, and integrating with other services via plugins.
* **Key Feature: Persistent Memory:** Unlike many bots, Moltbot remembers past interactions, providing a tailored and consistent experience.
* **Name Change:** The project was renamed from Clawdbot to Moltbot due to trademark concerns with Anthropic’s Claude.
An in-depth look at Clawdbot, an open-source AI agent that runs locally and integrates with messaging apps, offering a highly customizable and powerful personal assistant experience. The article details the author's experience setting up and using Clawdbot, its capabilities, and the potential implications for the future of apps and digital assistants.
Automated digest of news in my topics of interest.
A list of Python libraries that helped the author improve their automation scripts and turn duct-tape code into something trustworthy.
* **pathlib:** Simplifies file path manipulation, making it cross-platform compatible and more readable.
* **tenacity:** Provides a decorator for automatically retrying failed operations (like API calls) with configurable settings.
* **rich:** Enhances logging with features like progress bars, colored output, and detailed tracebacks for better observability.
* **schedule:** A more readable alternative to cron for scheduling tasks in Python.
* **pydantic:** Enforces data validation, ensuring inputs conform to expected types and structures.
* **python-dotenv:** Manages environment variables, keeping sensitive information (like API keys) separate from code.
* **loguru:** A streamlined logging library that requires minimal configuration.
* **watchdog:** Monitors filesystem changes and triggers actions based on those changes (event-driven automation).
* **typer:** Creates command-line interfaces (CLIs) for Python scripts, making them more user-friendly as tools.
* **pathlib:** Simplifies file path manipulation, making it cross-platform compatible and more readable.
* **tenacity:** Provides a decorator for automatically retrying failed operations (like API calls) with configurable settings.
* **rich:** Enhances logging with features like progress bars, colored output, and detailed tracebacks for better observability.
* **schedule:** A more readable alternative to cron for scheduling tasks in Python.
* **pydantic:** Enforces data validation, ensuring inputs conform to expected types and structures.
* **python-dotenv:** Manages environment variables, keeping sensitive information (like API keys) separate from code.
* **loguru:** A streamlined logging library that requires minimal configuration.
* **watchdog:** Monitors filesystem changes and triggers actions based on those changes (event-driven automation).
* **typer:** Creates command-line interfaces (CLIs) for Python scripts, making them more user-friendly as tools.
