Mastering Vibe Coding: 6 Suggestions for Successful Agentic Development

For those of you who have been reading my LinkedIn posts, you'll know that I have been spending a lot of my spare time "Vibe Coding". Vibe coding refers to the use of Agentic AI in software development ( see here ). Vibe coding has become particularly popular in the last few months, thanks to tools like VSCode Agentic Mode ( see here ) and advanced large language models (LLMs), such as Claude 4, which excel at development tasks ( see here ).

The transformation has been remarkable. I've had professional developers quote me 20 days or more to undertake tasks that I've completed using Vibe Coding in less than a day. However, vibe coding is not without its challenges or pitfalls. Through practical experience, I've learned several key lessons that can make the difference between success and frustration when it comes to working with agentic AI development systems.

The following six strategies learnt from my practical experience should help developers and citizen developers alike harness the full potential of these powerful tools.

Background: The Rise of Agentic AI in 2025

Before diving into the practical lessons, it's worth understanding the landscape in which we're operating. 2025 has been dubbed "the year of Agentic AI," with Gartner naming it the top tech trend for 2025 ( see: here ). Agentic AI refers to systems that are capable of autonomously performing tasks on behalf of users by designing workflows and using available tools, having "agency" to make decisions, take actions, solve complex problems and interact with external systems ( see: here ).

GitHub recently introduced Claude Sonnet 4 as the model powering the new coding agent in GitHub Copilot, specifically highlighting its improvements in agentic-based scenarios ( see: here ). In addition, VSCode's Agent Mode has been rolled out to all users, acting as an autonomous pair programmer that performs multi-step coding tasks, including analysing codebases, proposing file edits, and running terminal commands ( see: here ).

But here's the thing about these powerful tools – they require skill and knowledge to be used effectively. After months of experimenting with these systems, I've identified six critical strategies that separate successful vibe coding from frustrating dead ends.

Vibe Coding Learning # 1: Be Specific in Terms of "What" You Ask the Agent to Build and "How" You Want It Built

This is where your technical architecture knowledge becomes invaluable. If your prompt to the agent lacks specificity, the agent may take unusual and unexpected paths when attempting to address your request. The challenge lies in IT itself – there are countless ways to accomplish any given task, and that flexibility can become a liability when working with AI agents.

Consider the difference between these two prompts:

Vague: "Build me a user authentication system"

Specific: "Build a JWT-based user authentication system using Node.js with Express, implementing bcrypt for password hashing, PostgreSQL for user storage, and following REST API principles. Include middleware for token validation and refresh token functionality."

The specific prompt gives the agent clear guardrails, preferred technologies, and architectural patterns to follow. Without this guidance, you might end up with a solution that technically works but doesn't align with your technology stack or coding standards.

Many agentic development tools now support the ability to point to websites or documentation as part of the system prompt to identify development standards. Whether in your system prompt or user prompt, you want to clearly specify "how" the agent should build code. This includes preferred development languages, methodologies, libraries, infrastructure choices, and coding conventions.

Vibe Coding Learning # 2: Have the Agent Maintain an "Agent Design File"

One of the most effective strategies I've discovered is having the agent maintain a comprehensive design document in Markdown format for its own reference. This document should include a detailed description of your solution design, architectural decisions, and most importantly, a design change log. Include this document in the agent's context whenever you're giving directions, and ask the agent to update it frequently with your learnings as it goes.

LLMs suffer from what's called "loss of attention," which means that even with high input token limits, they can lose track of their position when handling complex development tasks ( see: here ). The design file serves as a persistent memory system, helping the agent maintain context across sessions and complex operations.

Your design file should include:

• Overall system architecture

• Technology stack decisions and rationale

• Database schemas and relationships

• API endpoint specifications

• Security considerations

• Performance requirements

• Change log with timestamps and reasoning

This document becomes particularly valuable when you return to a project after a break or when you need to onboard a new agent to an existing codebase.

Vibe Coding Learning #3: Have the Agent Maintain Its Own "Agent Task List"

Similar to maintaining a design file, keeping an active task list in markdown format is crucial for complex projects. You should ask the agent to regularly update this list with completed tasks and remaining work items. Include this task list in the agent's context when providing directions.

The task list addresses the same "loss of attention" issue, helping the agent remember its position in complex processes. Without this organisational structure, agents can sometimes forget completed work or get confused about priorities, leading to duplicated efforts or incomplete implementations.

Your task list should be structured with:

• High-level project phases

• Detailed subtasks with priority levels

• Completion status and timestamps

• Dependencies between tasks

• Notes about blockers or special considerations

This approach transforms what could be a chaotic development process into a structured, trackable workflow that both you and the agent can follow.

Vibe Coding Learning #4: "Break Development Tasks Down" into Smaller Sequential Steps

This might be the most important lesson I've learned. Rather than giving the agent massive, complex instructions all at once, break your project into smaller, sequential tasks with detailed specifications for each step.

For example, instead of prompting: "Refactor this application from Python to Node.js, move it to AWS, and add OAuth authentication," try this approach:

Phase 1: "Refactor the user management module from Python to Node.js, maintaining the same API endpoints and database schema. Use Express.js and follow the existing error handling patterns."

Phase 2: "Create AWS deployment configuration using Docker containers, setting up ECS with proper networking and security groups based on the existing infrastructure patterns."

Phase 3: "Implement OAuth 2.0 authentication using the authorisation code flow, integrating with the existing user management system while maintaining backward compatibility."

This sequential approach, giving the agent one set of tasks at a time, prevents the agent from becoming overwhelmed by competing goals and reduces the likelihood of the agent making conflicting decisions across different aspects of the project.

Vibe Coding Learning #5: Backup Often!

This cannot be overstated: ensure you back up your work frequently or utilise a robust code management solution, such as Azure DevOps or GitHub. Agents, like humans, can make mistakes, but their mistake patterns differ and sometimes exhibit a more systematic nature.

I've observed that agents typically start with conservative, targeted solutions to problems. However, when these initial approaches fail to achieve the desired results, they tend to progressively escalate the scope of changes. This escalation can spiral into systematic changes that have unintended consequences elsewhere in the application, potentially creating multiple interconnected issues.

The result is often a situation where you're working with the agent to fix multiple problems simultaneously, some of which were created by the agent's previous attempts at solutions. Regular backups ensure you can always roll back to a known good state rather than trying to untangle a complex web of cascading changes.

Best practices for backup include:

• Commit to version control after each successful task completion

• Create feature branches for experimental changes

• Tag stable releases

• Document the rationale for significant changes

• Keep notes about what worked and what didn't

Vibe Coding Learning #6: Use GenAI to Write Your Agentic Prompts

One of the most powerful meta-strategies I've discovered is using generative AI to help craft the prompts you'll give to your coding agents. This might sound recursive, but it's incredibly effective for ensuring your prompts are comprehensive and well-structured.

The challenge with agent prompts is that they need to be both detailed enough to provide clear direction and structured enough to be easily parsed by the AI system. Writing effective prompts requires you to think through all the technical specifications, constraints, and context that a human developer would need, but in a format optimised for AI consumption.

Here's an example of how I'd approach prompt generation:

Step 1: Start with a high-level description of what you want to accomplish. For example: "I need to create a REST API for a task management system with user authentication."

Step 2: Ask an LLM (such as Claude or ChatGPT) to assist you in creating a comprehensive prompt. Your request might look like this:

"Help me write a detailed prompt for a coding agent to build a REST API for a task management system. The prompt should include technical specifications, architectural decisions, security considerations, and implementation details. Make sure it covers all aspects a professional developer would need to know."

Step 3: Review and refine the generated prompt, incorporating your specific requirements, technology preferences, and any unique project constraints.

The benefits of this approach are significant:

• Completeness: LLMs excel at identifying requirements that you might have overlooked, such as error handling patterns, logging strategies, or security considerations.

• Structure: Generated prompts tend to be well-organised with clear sections and hierarchical information that agents can easily parse.

• Technical Detail: LLMs can help you specify technical details you might not have considered, like database indexing strategies or API versioning approaches.

• Best Practices: The generated prompts often incorporate industry best practices and common patterns that improve code quality.

For example, when I ask an LLM to help write a prompt for database migration, it might remind me to include rollback procedures, data validation steps, performance considerations, and backup strategies – details I might have forgotten to specify explicitly.

This meta-approach also helps with consistency. When you're working on a large project with multiple coding tasks, having LLM-generated prompts ensures that each task prompt maintains the same level of detail and follows similar structural patterns.

The key is to treat the LLM-generated prompt as a starting point, not a final product. Constantly review, customise, and refine based on your specific project needs and technical preferences.

The Broader Context: Why This Matters Now

The investment in learning good agentic development practices is worthwhile because agentic AI is rapidly becoming mainstream. Microsoft has recently announced that we've entered "the era of AI agents" with groundbreaking advancements in reasoning and memory ( see: here ), while Google is developing software AI agents intended to help engineers navigate every stage of the software development process ( see: here ).

Current agentic AI systems go beyond simple code assistance – they can run, debug, and execute code to obtain results, representing a fundamental shift in how we approach software development ( see: here ).

The question is not whether these tools will become standard practice, but how quickly you can master them to stay competitive.

Conclusions

There's no doubt that generative AI and agentic solutions represent a game-changer for application development and cloud infrastructure deployment ( see: here ). However, success requires more than just access to powerful tools – it demands a strategic approach that effectively leverages both human architectural expertise and AI capabilities.

The six lessons I've shared – being specific about requirements, maintaining design documentation, keeping organised task lists, breaking down complex work, backing up frequently, and using AI to craft better prompts can form the foundation of successful vibe coding. These practices transform what could be a chaotic, unpredictable process into a structured, reliable methodology for rapid development.

As we move deeper into 2025 and beyond, the developers who increase their architectural skills and master these collaborative patterns with AI agents will find themselves capable of accomplishing tasks that previously required entire teams. The future of software development isn't about humans versus AI – it's about humans and AI working together more effectively than either could alone.

The vibe coding revolution is just beginning, and those who learn to ride the wave now will be the ones shaping the future of how software gets built.

If you have questions about this topic or want further information or assistance, please contact me at david@ai-savvy.com.au . You can also read more about GenAI at www.ai-savvy.com.au

Useful References:

1. Microsoft Build 2025: The age of AI agents and building the open agentic web - https://blogs.microsoft.com/blog/2025/05/19/microsoft-build-2025-the-age-of-ai-agents-and-building-the-open-agentic-web/

2. 2025 Informed: the year of Agentic AI - https://techinformed.com/2025-informed-the-year-of-agentic-ai/

3. AI Agents in 2025: Expectations vs. Reality | IBM - https://www.ibm.com/think/insights/ai-agents-2025-expectations-vs-reality

4. Microsoft announces over 50 AI tools to build the 'agentic web' at Build 2025 | VentureBeat - https://venturebeat.com/ai/microsoft-announces-over-50-ai-tools-to-build-the-agentic-web-at-build-2025/

5. Top Agentic AI Platforms in 2025: The Ultimate Guide for Businesses | Olive Technologies - https://olive.app/blog/top-agentic-ai-platforms-in-2025-the-ultimate-guide-for-businesses/

6. Agentic AI: 4 reasons why it's the next big thing in AI research | IBM - https://www.ibm.com/think/insights/agentic-ai

7. Agentic AI Named Top Tech Trend for 2025 -- Campus Technology - https://campustechnology.com/articles/2024/10/23/agentic-ai-named-top-tech-trend-for-2025.aspx

8. Agentic AI: 8 Use Cases and Real life Examples in 2025 - https://research.aimultiple.com/agentic-ai/

9. What Is Agentic AI, and How Will It Change Work? - https://hbr.org/2024/12/what-is-agentic-ai-and-how-will-it-change-work

10. Google is developing software AI agent ahead of annual conference, The Information reports | Reuters - https://www.reuters.com/business/google-is-developing-software-ai-agent-ahead-annual-conference-information-2025-05-12/

11. Introducing Claude 4 | Anthropic - https://www.anthropic.com/news/claude-4

12. Agent mode: available to all users and supports MCP - https://code.visualstudio.com/blogs/2025/04/07/agentMode

13. Claude Code: Best practices for agentic coding - https://www.anthropic.com/engineering/claude-code-best-practices

14. Introducing GitHub Copilot agent mode (preview) - https://code.visualstudio.com/blogs/2025/02/24/introducing-copilot-agent-mode

15. GitHub - anthropics/claude-code - https://github.com/anthropics/claude-code

16. Claude Code overview - Anthropic - https://docs.anthropic.com/en/docs/claude-code/overview

17. Anthropic Claude Sonnet 4 and Claude Opus 4 are now in public preview in GitHub Copilot - https://github.blog/changelog/2025-05-22-anthropic-claude-sonnet-4-and-claude-opus-4-are-now-in-public-preview-in-github-copilot/