Rust Runtime to Give AI Agents Real DevOps Tools

A developer has built tooler, an open-source runtime written in Rust that provides AI agents and DevOps workflows with structured tools for tasks like HTTP requests, database operations, filesystem access, and deployments. The runtime exposes its capabilities through the Model Context Protocol (MCP), enabling AI agents to execute operations deterministically rather than relying on arbitrary shell commands.

I’ve been building tooler , an open-source runtime written in Rust designed to give developers, DevOps workflows, and AI agents a consistent set of real-world tools. Instead of letting an agent rely on arbitrary shell commands, tooler provides structured capabilities for HTTP requests, databases, filesystems, processes, logs, Git/GitHub, deployments, health checks, systemd, cron, remote hosts, and reusable playbooks. It also exposes its capabilities through MCP , making them directly accessible to AI agents. The idea is simple: LLM → reasoning and planning → tooler → deterministic execution This creates a clean separation between what the agent decides to do and how those operations are safely and consistently executed. tooler is built in Rust , distributed as a standalone binary, and designed for both humans and AI agents. In this post, I’ll introduce the project, its architecture, why I built it, and how I’m exploring tooler as an execution layer for increasingly complex agentic workflows.