From: aidotengineer
This article explores the four emerging patterns of AI native development, detailing how AI has transformed the software development workflow since the rise of generative AI [00:00:01]. Technology has evolved from simple Large Language Models (LLMs) to Retrieval Augmented Generation (RAG), code indexing, advanced functions, and even teams of agents [00:00:16]. Similar to the cloud-native explosion, AI is ushering in a new way of working [00:00:36].
The integration of AI changes our tasks: some are replaced, others are enhanced, and new ones emerge [00:01:14]. The core of this shift is the developer, moving into different areas of expertise [00:01:36].
Speaker Background
The speaker is an independent industry advisor known for work in DevOps and DevSecOps, focusing on automation, which aligns well with generative AI integration in software engineering [00:01:47]. They also contribute to the AI native dev community by curating content [00:02:02].
Four Patterns of AI Native Development
1. From Producer to Manager
Traditionally, developers produce code. With AI agents producing code, the developer’s role shifts to managing these agents [00:02:13].
- Shift in Workload
- Less time spent coding [00:02:36].
- Increased time and cognitive load in reviewing AI-generated code [00:02:39]. The “thinking work” now occurs during the review phase [00:02:45].
- Reducing Cognitive Load in Review
- New review methods are emerging beyond clunky diff views or verbose chat views [00:03:01].
- Summarized Reviews: Reviews can be stripped down to what matters, allowing for quick “yes/no” decisions [00:03:12].
- Step-by-Step Review: For multiple files, breaking down the review into a structured flow helps manage complexity [00:03:34].
- Visual Reviews: Reviewing changes through diagrams rather than just text makes it easier to spot errors and impacts [00:03:51].
- Moldable Development Environments: Future IDEs are expected to adapt to specific code review needs and domains, moving away from endless text streams [00:04:08].
- Automating Commits and Agent Management
- Auto-Commit: The concept of auto-committing AI-generated code, with developers reviewing only if they don’t like it, shifts the responsibility [00:04:36]. This relies on heuristics to accept low-risk or low-impact changes [00:04:51].
- Checkpoints for Longer-Running Agents: For extended agent operations, checkpoints allow developers to jump in at specific points and regenerate code, avoiding full re-reviews of the entire thought process [00:05:05].
- Setting Constraints: Developers, acting as managers, can set rules for AI, such as locking files or defining permissions for agents, establishing guardrails for their actions [00:05:27].
- Cost Monitoring: As agents run for longer durations, monitoring the cost of their operations becomes important [00:05:48].
2. From Implementation to Intent
The focus shifts from directly implementing code to specifying the desired outcome or intent to AI agents [00:06:25].
- Specification-Driven Development
- Simple Markdown Specifications: Early forms involved adding markdown files as simplified specification files to prompts, reducing repetitive re-writing in prompts [00:06:39]. This helps build shared functional or technical specifications [00:06:55].
- AI-Generated Plans: AI can assist in building step-by-step plans from desired outcomes, moving towards intent-based coding rather than just chat or text completion [00:07:07].
- Specification-Centric Workflows: Entire workflows can become specification-centric, where functional, technical, and security requirements are paramount, and the underlying code is less visible [00:07:39].
- Program Manager Role: Developers increasingly manage the process of defining intent, akin to a program manager [00:08:05].
3. From Delivery to Discovery
With robust pipelines handling delivery, the emphasis shifts to discovering new ideas and problems to solve [00:08:37].
- Accelerated Prototyping and Design
- AI enables faster design and creation of prototypes, allowing for quicker exploration of “how it needs to be” [00:08:52].
- Multiple Iterations: AI can generate multiple versions or iterations of a design, allowing developers to choose the best one and suggesting ideas they might not have considered [00:09:16].
- Design-to-Code Loop: The iterative process of designing, coding, redesigning, and modifying code is refined to facilitate discovery and exploration [00:09:34].
- Customer Involvement: The concept of “vibe coding” (rapid prototyping) can extend to customers, allowing them to design their desired interfaces on top of a product, similar to A/B testing on steroids [00:09:56]. This addresses friction points in achieving the “perfect product” [00:10:07].
4. From Data to Knowledge
Whatever is learned throughout the development process, including content produced, is transformed into reusable knowledge [00:10:31].
- Capturing and Leveraging Knowledge
- Learning from Production Issues: Insights gained from production issues can be fed back into code and development practices [00:10:49].
- Incident Response: Lessons from incidents, such as what failed or new guidelines, can be codified as knowledge [00:10:58].
- Onboarding and Retention: Turning code into lessons reduces onboarding time for new team members and helps retain knowledge when someone leaves [00:11:14].
- Feature Memory: AI can track the history of features, including discarded attempts, creating a “feature memory” to prevent re-doing work and track decisions that are often lost in tickets or diagrams [00:11:34].
- In-Flow Knowledge Capture: As developers chat and code, agents can proactively suggest saving important information as knowledge, creating a beneficial loop for both human and AI learning [00:12:05].
- Knowledge Application: This captured knowledge can then be used to answer questions and improve the coding of solutions [00:12:24].
Summary of Patterns and Roles
The four patterns of AI native development represent a shift in the developer’s role [00:13:02]:
- From Producer to Manager: Moving towards operations, overseeing AI-generated code.
- From Implementation to Intent: Shifting towards QA and architecture, defining what needs to be built.
- From Delivery to Discovery: Embracing a product owner mindset, exploring and finding new ideas.
- From Data to Knowledge: Acting more like a data engineer, capturing and leveraging insights.
These shifts highlight that AI’s impact extends far beyond merely helping with faster typing [00:13:14]; it enables developers to perform tasks traditionally associated with senior roles [00:13:05].
Resources
To explore the landscape of AI native development tools, you can visit ainativedev.io, which curates over 300 tools [00:13:21]. A newsletter also focuses on the intersection of coding, software engineering, and AI [00:13:35].