Agents Machine - Your AI development workspace with persistent memory.
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Self-hosted AI agent platform with persistent memory, 7 specialized agents, encrypted vault, and 51 MCP tools. Works with Windsurf, Cursor, Claude Code. Free plan available.
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Why we built the "Control Plane" for AI Engineering
Hey everyone! I’m the founder of Agents Machine, and I’m thrilled to finally share what we’ve been working on.
The Inspiration & Problem The idea for Agents Machine started when I realized that while AI tools like Cursor and Windsurf are incredible, our actual engineering workflows were still fragmented. Every time I started a new session or switched tasks, I felt like I was "re-teaching" the AI the same architectural rules, anti-patterns, and project context. The AI had no long-term memory, and managing complex multi-step tasks (like a full security audit or a complex refactor) was still a manual, prompt-heavy process.
The Solution We didn't want to build just another chat interface. We wanted to build a control plane. Agents Machine acts as the "brain" sitting on top of your IDE.
It provides:
Persistent Semantic Memory: It remembers every architectural decision you make. Specialized Agent Teams: Instead of one generalist, you spawn an Analyst, an Architect, or a Developer.
Visual Pipelines: A way to turn repetitive AI tasks into automated DAG workflows. How we evolved Initially, we started as a simple MCP server. But we quickly realized that for a team to actually trust AI in production, they needed more than just a tool — they needed governance and cost controls. This led us to build the encrypted Vault for secrets and a strict Budget system to prevent runaway LLM costs. We moved from being a "cool tool" to a "reliable infrastructure" for AI-native development.
I’d love to hear how you’re managing AI context in your own projects! I'll be here all day to answer questions.
Replies
Why we built the "Control Plane" for AI Engineering
Hey everyone!
I’m the founder of Agents Machine, and I’m thrilled to finally share what we’ve been working on.
The Inspiration & Problem
The idea for Agents Machine started when I realized that while AI tools like Cursor and Windsurf are incredible, our actual engineering workflows were still fragmented. Every time I started a new session or switched tasks, I felt like I was "re-teaching" the AI the same architectural rules, anti-patterns, and project context.
The AI had no long-term memory, and managing complex multi-step tasks (like a full security audit or a complex refactor) was still a manual, prompt-heavy process.
The Solution
We didn't want to build just another chat interface. We wanted to build a control plane. Agents Machine acts as the "brain" sitting on top of your IDE.
It provides:
Persistent Semantic Memory: It remembers every architectural decision you make.
Specialized Agent Teams: Instead of one generalist, you spawn an Analyst, an Architect, or a Developer.
Visual Pipelines: A way to turn repetitive AI tasks into automated DAG workflows.
How we evolved Initially, we started as a simple MCP server. But we quickly realized that for a team to actually trust AI in production, they needed more than just a tool — they needed governance and cost controls. This led us to build the encrypted Vault for secrets and a strict Budget system to prevent runaway LLM costs. We moved from being a "cool tool" to a "reliable infrastructure" for AI-native development.
I’d love to hear how you’re managing AI context in your own projects! I'll be here all day to answer questions.