AI Coding Agents Stall at Deployment: Cowork User Hits Sandbox, Permission, and Context Loss Issues

A Reddit user on r/ClaudeAI describes a recurring frustration with AI coding agents: they handle the coding phase well but stop short of deployment, forcing manual intervention. Using Cowork to build and deploy a Next.js app with complex map functionality, the user found the coding part (commits, functionality) went well. However, deployment hit a wall.
Key issues encountered:
- Sandbox restrictions: Cowork couldn't push to GitHub due to its sandbox environment; it effectively told the user to do it locally.
- Manual git commands: The user had to open a terminal, run git commands, and troubleshoot SSH key/authentication issues — completely context-switching out of the platform.
- Session context loss: Later in the session, the agent required rephrasing earlier clarifications, despite the transcript existing.
The user points out a broader pattern: AI tooling gets you 85% of the way but hands back the keyboard at the critical deployment step. They describe it as less an autonomous developer and more a "talented intern trapped behind bulletproof glass." Specific pain points include sandbox restrictions breaking deployment pipelines, permission weirdness, unreliable long-session memory, and automation stopping at the exact moment it matters most.
Who it's for: Developers using or evaluating AI coding agents for full-stack app development and deployment.
📖 Read the full source: r/ClaudeAI
👀 See Also

The Versatile Applications of OpenClaw: Insights from the Clawdbot Community
Discover the innovative ways users have leveraged OpenClaw, from personal projects to ambitious automated systems, as shared by the r/clawdbot community.

Building a Full App with Lovable + Claude + Gemini: A Case Study
A software engineer built Earnest, a bank bonus tracker, using Lovable for UI, Claude for intent understanding, and Gemini as a second opinion. The app now has 100+ users tracking $9,700+ in bonuses.

AI Agents Running a Real E-commerce Business: Practical Insights from an Implementation
An AI agent system operates an actual e-commerce store, handling design, coding, marketing, and customer operations without human task execution. The implementation reveals that judgment calls like design rejection thresholds and incident prioritization present harder challenges than technical agent coordination.

How Letting OpenClaw Improve Its Own Environment Creates Sustainable Workspaces
An experienced OpenClaw user found the biggest productivity gain came from allowing the agent to update its own internal docs, edit operating files, refine prompts, build custom tools, write scripts, and document lessons. Their workspace structure includes key markdown files like SOUL.md for behavioral style, AGENTS.md for operational conventions, and MEMORY.md as a lightweight index.