AI Programming Power User Workflow: A Practical Guide to Automated Loops and Multi-Model Collaboration

From Beginner to Expert: The Path to Advanced AI Programming

In the world of AI-assisted programming, there's a massive efficiency gap between beginners and experts. A beginner's workflow looks like this: prompt → wait → review → prompt again, endlessly repeating the cycle. True experts, on the other hand, have learned to automate the entire workflow, keeping AI agents running in the background—even fixing bugs and updating documentation while you sleep.

This article systematically covers the core strategies used by top-tier AI programming engineers, including tool selection, rules configuration, skills systems, automation and loops, cloud-based agents, and multi-model collaboration.

AI Coding Tool Selection: Finding the Right Coding Agent for You

An AI coding agent is an autonomous programming system built on large language models (LLMs). It doesn't just generate code snippets—it understands project context, executes terminal commands, reads and writes to the file system, and even interacts with Git. Unlike earlier code completion tools (such as GitHub Copilot's tab completion), coding agents are "agentic"—they can autonomously plan tasks, break them into steps, execute actions, and iteratively self-correct based on feedback. This capability relies on the ReAct (Reasoning + Acting) architecture, where the model reasons and then acts at each step, forming a think-act-observe loop.

The current mainstream AI coding tools each have their strengths and weaknesses. Cursor is one of the most recommended choices, with its core advantage being support for models from multiple AI companies (OpenAI, Anthropic, and even Cursor's own proprietary models), and it was among the first to support cloud-based agents.

Codex is known for its elegant design and streamlined interaction experience—after executing a command, it provides just one or two sentences of summary without outputting lengthy explanations. For developers who don't want to read through walls of text, this concise style is very appealing.

Additionally, tools like Cloud Code, Devin, and Factory each have their own merits. The key is to try them yourself and find the one that best fits your workflow.

Rules and agents.md: Defining Your AI's Code of Conduct

What Is agents.md?

agents.md (or the corresponding rules file) is the core configuration file where you tell your AI tool "how you want it to work." This concept originates from best practices in System Prompt Engineering. In the LLM architecture, the system prompt sits at the very beginning of the conversation context and exerts a persistent influence on all subsequent model outputs. agents.md essentially persists these system-level instructions as a project file, making them version-controllable, shareable across teams, and iteratively optimizable. This aligns with the philosophy behind .editorconfig or .eslintrc files in traditional software engineering—using declarative configuration to unify team behavior. Different tools use different names: Cursor uses .cursor/rules, Windsurf uses .windsurfrules, and Claude Code uses CLAUDE.md.

You can define things like:

• The model's response style and persona
• Commit message formatting
• Coding preferences and project conventions
• Deployment processes and workflows

In Cursor, go to Preferences → Rules/Skills/Sub-agents to configure these. For example, you can set rules like "keep responses brief," "reply only in plain English without showing code snippets," or "avoid writing one-off scripts."

Recommendation: If you're not using agents.md yet, start now. Begin by defining the model's "personality," then gradually add workflow specifications.

Skills System: Reuse Every Repeated Operation

Skills are one of the most underrated yet most important features in AI programming. The core principle is: anything you've done more than twice should become a skill.

From a technical perspective, the skills system is essentially a combination of a "prompt template library" and a "tool-calling protocol." Each skill contains: trigger conditions (when to activate), context injection (which files or environment variables to read), execution steps (the specific sequence of operations), and validation conditions (how to determine success). During runtime, the agent automatically discovers relevant skills through semantic matching, relying on RAG (Retrieval-Augmented Generation) technology—all skill descriptions are vectorized and stored, and when user input or task context exceeds a similarity threshold with a skill's semantics, that skill is automatically injected into the current context.

Four Core Uses of Skills

1. Repetitive task automation: After creating a skill, simply type /skill-name to invoke it—no need to repeatedly paste the same prompts
2. Domain-specific rules: Company writing styles, GitHub Issue templates, internal information—all can be encapsulated as skills
3. Tool instructions: Define how to run tests, how to call specific APIs or CLIs, so the agent doesn't need to be told the API endpoint and expected response every time
4. Quality gates: For example, "run all tests before opening a PR, ensure 100% pass rate, and auto-fix any failures"

Notably, agents can automatically discover and decide which skills to use at runtime—you don't even need to invoke them manually.

There's currently an open-source project on GitHub called "agent-skills" with 61,000 stars, covering the complete development lifecycle from requirements refinement, PRD writing, and code implementation to testing and deployment. Installation is extremely simple—copy the URL and type "install this skill" in your tool.

Automation and Loops: The Expert's Core Weapons

Automations

Automations allow you to automatically send prompts to the model based on a trigger. In Cursor, for example, click Automations in the top-left corner → Create New Automation, then set the trigger and instructions.

A practical example: when a PR is opened, Greptile automatically performs a code review and leaves comments. Through automation, you can have another agent wait for Greptile's comments to appear, address each comment one by one, then push the fixed code back to the PR. The entire process requires zero human intervention.

Loops

Loops consist of three elements: a trigger condition, a repeatedly executed action, and a termination goal. Here are three highly practical loop examples:

1. Nightly Documentation Scan Loop: Automatically reviews the entire codebase every night, ensures all documentation reflects the day's latest changes, then opens a PR to submit updates.

2. 50ms Page Load Optimization Loop: The agent traverses every page, modal, and sidebar of the application. If any element takes longer than 50 milliseconds to load, it optimizes queries and code until everything loads within 50ms. This loop may run for hours, but when it's done, the application will be blazing fast.

3. Production Error Auto-Fix Loop: Every night, it checks production logs for errors, analyzes root causes, writes fix code, and submits a PR. When you wake up in the morning, the fix is already waiting for you.

The Three Pillars: The Flywheel Effect of Testing, Documentation, and Logging

The core philosophy of expert-level AI programming is building a self-improving flywheel:

• 100% test coverage: Automatically check coverage and supplement tests when insufficient
• Never-outdated documentation: Automatically sync code changes to documentation daily
• Comprehensive logging: Log everything—the cost is minimal (you can set a 7-day or 30-day window), but it enables agents to automatically fix any errors that arise

These three elements reinforce each other: comprehensive tests let agents safely modify code, comprehensive documentation lets agents understand context, and comprehensive logs let agents discover and fix issues.

Cloud Agents vs. Local Agents: How to Choose

Advantages of Cloud Agents

• Unlimited parallelism: Not constrained by local CPU/RAM, you can run 10, 20, or 30 agents simultaneously
• Access from anywhere: Manage agents from your mobile device
• Fully isolated environments: Multiple agents writing to the same repo won't conflict
• Visual feedback: Cursor's cloud agents automatically generate videos and screenshots of changes

Advantages of Local Agents

• Faster startup (no waiting for environment initialization)
• Greater sense of control
• Usually first to receive the latest features

Practical advice: when you need to run a large number of agents in parallel (12–20), your local machine will become extremely slow—cloud agents are the better choice in this scenario.

Work Trees: The Key Technology for Parallel Development

Work Tree (git worktree) is a native Git feature introduced in Git 2.5 that allows you to create multiple working directories from the same repository, each checking out a different branch. Unlike cloning the repo multiple times, worktrees share the same .git directory (object database and references), making creation extremely fast and disk-space efficient. In multi-agent scenarios, each agent operating on an independent worktree means they each have their own working area and staging area, allowing them to simultaneously modify different files—or even the same file—without file lock conflicts. They commit independently through their respective branches, and code conflicts are handled during the merge phase.

In Cursor and Codex, creating a work tree is as simple as clicking the "New Work Tree" button.

The only scenario where work trees aren't needed is when multiple agents are working on completely separate areas of the codebase.

Multi-Model Collaboration Strategy: Optimal Balance of Speed and Cost

You don't always need to use the most powerful model. Here's an example of an efficient multi-model workflow:

1. Planning phase: Use the strongest model (e.g., Fable) to analyze the entire codebase and create a detailed plan
2. Execution phase: Use a model that excels at writing code (e.g., Composer) to implement features according to the plan
3. Review phase: Use yet another model (e.g., GPT-5.5) to provide code review from a different perspective

This strategy reduces token costs, speeds up task completion, and provides multi-angle quality assurance.

The Unsolved Challenge: Multi-Agent Merging and Deployment

When a dozen agents are working in parallel, merging code into the main branch becomes a nightmare—after each agent merges, all other agents need to rebase, re-test, and attempt to merge again, creating a chain of waiting. This problem is essentially the "eventual consistency" challenge in distributed systems. In traditional Git workflows, the main branch is linear, and each merge is a serial operation. When N agents complete work simultaneously and need to merge, it theoretically requires O(N²) conflict detection and resolution passes. This is similar to the "write-write conflict" problem in the database domain.

The current workaround is to batch PRs and have a single agent handle all merging and deployment. Interestingly, Cursor has announced it's building a Git alternative specifically designed for agent-scale deployment. This solution may adopt an approach similar to CRDTs (Conflict-free Replicated Data Types), or introduce semantic-level merging (understanding code intent rather than just text diffs), elevating merging from a text operation to a semantic operation. This problem is expected to be solved in the near future.

Conclusion

The core of AI programming isn't about writing better prompts—it's about building an automated development system. By mastering agents.md configuration, the skills system, automated loops, cloud agents, and multi-model collaboration, you can evolve from "manual prompting" to "systematic operations," turning AI into your 24/7 development team.

Key Takeaways