A persistent, inspectable coordination workspace that sits between you and your AI coding agent — so your intent survives every session, every context window, and every handoff.
The Workflow: In our interactive demo, you'll see how ctlsurf transforms AI coding workflows:
Example: An agent implementing "user authentication" documents: "Added JWT-based auth" (summary), "Used existing User model" (assumption), and "Skipped refresh token implementation" (simplified). No more mystery about what your AI actually did.
You give Claude Code or Cursor a clear task. It works beautifully for twenty minutes. Then it drifts. It forgets your architecture decisions. It rewrites files you told it not to touch. You restart the conversation and now you're re-explaining everything from scratch.
The problem isn't the agent. It's that there's no shared surface between you and it. No place where your decisions persist. No place where you can see what the agent is actually working from and correct it in real time.
You're collaborating with an intelligent system that has amnesia.
And when this happens right before a demo, a review, or a handoff, you're the one explaining behavior you didn't control.
Full explainability of AI internals is an unsolved research problem. ctlsurf doesn't try to solve it. Instead, it does something more practical — it gives you and your agent a shared artifact you can both see, reference, and build on.
Think of it like a blueprint between two engineers who speak different languages. You don't need to understand each other's internal reasoning. You need a document you both trust.
A persistent, inspectable coordination workspace where human intent and agent behavior stay aligned.
Your architecture decisions, coding standards, and project knowledge live in a shared workspace the agent reads every time. No more re-explaining.
See something wrong? Highlight it, annotate it, turn it into an instruction. The agent picks it up immediately. You're steering, not starting over.
ctlsurf connects directly to your coding agent through MCP integration. Changes you make are reflected instantly. No copy-paste. No workflow interruption.
You can always see exactly what instructions your agent is operating under. Not a black box. A legible, editable control surface that you own.
When an AI agent completes a task, it must document what it did, what it assumed, and what it skipped.
No more guessing what the AI did. Every completed task includes:
The "simplified or skipped" field is the most important - it catches when agents give up on parts of tasks without telling you.
When you spot something the agent simplified or skipped that shouldn't have been, you can reopen the task with feedback:
This creates an accountability loop - agents can't silently cut corners because you'll see exactly what they skipped and can push back.
Three steps to persistent AI context
Add ctlsurf to your MCP config. Works with Claude Code, Cursor, Windsurf, and any MCP-compatible tool.
Create pages for architecture, decisions, and tasks. Your agents will reference these automatically.
Every session, agents check for tasks, read your docs, and work with full project context.
MCP is an open standard created by Anthropic that allows AI assistants to connect to external tools and data sources. ctlsurf is built as an MCP server, meaning any MCP-compatible AI coding assistant can connect to it seamlessly.
Setup is simple: Add a few lines to your MCP configuration file, and your AI agent gains access to 50+ ctlsurf tools for managing pages, tasks, skills, and documentation.
No code changes required. Your existing AI coding workflow stays the same - ctlsurf just gives your agent a persistent memory and knowledge base to work with.
Works with your existing tools
From solo developers to engineering teams
Maintain shared context across sprints, agents, and tools. Everyone stays aligned on decisions and progress.
Understand why features shipped a certain way, with a traceable history of decisions and trade-offs.
Coordinate long-running tasks with evolving state instead of isolated prompts. Context persists across sessions.
Define workflows with guardrails that guide AI agents through complex tasks consistently.
Skills are structured workflow templates that guide AI agents through complex, multi-step tasks. Think of them as playbooks or runbooks that ensure consistency and quality across your team's AI-assisted work.
Each skill contains:
Example Use Cases: API debugging workflows, code review checklists, deployment procedures, security audit processes, feature implementation patterns.
Systematic approach to debugging
Browse and fork skills from the community marketplace. Find battle-tested workflows for common development tasks and customize them for your team's needs.
Start free, upgrade when you need more
For individual developers
For power users and teams
Introductory price
"AI agents reason differently than you do. We can't fully decode their internals yet, and maybe we never will. But we can build a shared workspace where human intent and agent behavior stay aligned. That's what ctlsurf is."
Built by an AI engineer with decades of experience building enterprise ML systems. ctlsurf came from the frustration of watching brilliant agents lose context every single session.
Get Started Free →