Continuous Claude: The AI-Powered CI/CD Pipeline That Lets Developers Sleep on Multi-Step Projects

In the high-stakes world of software development, tackling multi-step projects like migrating frameworks or boosting test coverage from 0% to 80% can feel like herding cats—especially when you're contractually bound to deliver results in weeks. Enter Continuous Claude, a new CLI tool that transforms Anthropic's Claude Code into an relentless, autonomous agent. By orchestrating it in a continuous loop, this tool handles everything from code changes to PR creation, CI checks, and merges, all while you catch some Z's. It's not just automation; it's a paradigm shift for how AI assists in dev workflows.

The Problem with One-Shot AI Coding Tools

Current AI coding assistants, including Claude Code, excel at isolated tasks but falter on larger projects. They complete a single job and stop, lacking mechanisms for self-criticism or iterative refinement. This 'one-shot' approach exhausts context windows quickly and makes scaling to complex tasks—like documenting an undocumented codebase or refactoring a monolith—nearly impossible without constant human intervention.

Continuous Claude addresses this by running Claude Code in a persistent loop, inspired by CI/CD practices and persistent agents. It breaks down overwhelming tasks into incremental steps, ensuring each iteration builds on the last. For developers and engineers weary of mundane technical debt, this means AI can now handle the grunt work autonomously, while humans stay in the loop via familiar GitHub PR reviews.

'This is part of a continuous development loop... you don't need to complete the entire goal in one iteration, just make meaningful progress on one thing, then leave clear notes for the next iteration.'

This prompt engineering sets expectations for Claude to focus on relay-race-style handoffs, preventing it from rushing or going rogue.

How Continuous Claude Works: A Symphony of Loops and Git

At its core, Continuous Claude is a Bash script that acts as a conductor for Claude Code. You provide a high-level prompt (e.g., 'add unit tests until all code is covered'), set a max number of iterations or cost budget, and specify your GitHub repo. The script then enters a while loop—potentially infinite—where each cycle:

1. Invokes Claude Code with the prompt, emphasizing incremental progress.
2. Handles Git Operations: Creates a new branch (e.g., continuous-claude/iteration-1/timestamp), commits changes, pushes to GitHub, and opens a PR using the GitHub CLI (gh).
3. Monitors and Merges: Polls for CI checks and required reviews. Once everything's green and approved, it merges (via squash, merge, or rebase) and cleans up the local branch.
4. Persists Context: Updates a SHARED_TASK_NOTES.md file with concise, action-oriented notes—like 'Note: I tried adding tests to X but they failed because of an edge case; need to handle null input in function Y.' This external memory prevents context drift and enables seamless handoffs between AI iterations or human reviewers.

If an iteration fails (e.g., tests don't pass), the PR is closed, the branch deleted, and the next loop learns from the failure via the notes. This wastefulness is offset by the system's ability to iterate smarter, leveraging GitHub's built-up ecosystem for code reviews and preview environments without extra setup.

For technical audiences, consider the implications: This isn't just scripting AI; it's embedding it into your existing DevOps pipeline. Developers can now offload refactoring tasks—like modernizing callbacks to async/await or updating to new style guidelines—over weekends, with 20+ PRs each passing full CI. It's Dependabot on steroids, capable of not only updating dependencies but fixing post-update bugs based on release notes.

Context Continuity: The Secret Sauce

Maintaining state across stateless AI invocations is tricky, but Continuous Claude's SHARED_TASK_NOTES.md is a clever MVP. Prompted to keep notes concise and focused (avoiding verbose logs or redundant info from GitHub), Claude faithfully records progress, insights, and pitfalls. In one real-world example, an iteration flagged a null input edge case, allowing the next to prioritize it—reducing circles of repetition that plague context-less AI.

Whether to commit this file to Git history is an open question. As an external scratchpad, it provides long-term memory beyond any single context window, but a more sophisticated system (e.g., multi-task tracking) could evolve it further. For now, the trade-off works: Claude generally adheres to prompting, minimizing drift.

Broader Implications for DevOps and Technical Debt

This tool shines for tasks too mundane for humans yet prone to breakage if rushed. Imagine automating monolith breakdowns, dependency updates, or even full framework migrations (e.g., Next.js to TanStack Start) in small, testable increments. By mirroring human dev practices—iterative refinement over big-bang changes—it democratizes AI for larger projects.

The human-in-the-loop aspect is key: While Claude handles coding and iteration, PR reviews ensure quality. You control the pace with flags like --max-runs, --max-cost, or --disable-commits for testing. It's flexible enough for infinite runs until stopped, or budget-capped execution.

As AI integrates deeper into software engineering, Continuous Claude exemplifies how simple loops + Git + persistence can unlock autonomous workflows. For teams battling technical debt, this could mean reclaiming weekends and focusing on innovation, not maintenance. Download it from GitHub and let AI do the heavy lifting—your codebase (and sanity) will thank you.

Source: Continuous Claude GitHub Repository by Anand Chowdhary, licensed under MIT.