AI native development patterns

From: aidotengineer

AI native development signifies a shift in how software engineering workflows are approached, moving beyond merely sprinkling AI on existing processes to fundamentally redefine practices [00:00:44]. The rapid advancement from simple Large Language Models (LLMs) to RAG (Retrieval-Augmented Generation), code indexing, and the emergence of agents and teams of agents has driven this evolution [00:00:16]. This new paradigm impacts tasks by either replacing, enhancing, or introducing new responsibilities for developers [00:01:17].

Four key patterns define this shift in AI native development [00:01:30]:

1. From Producer to Manager

Historically, developers have been the primary producers of code [00:02:16]. With AI agents now capable of producing code, the developer’s role is evolving into managing these agents [00:02:21].

Key aspects of this shift include:

• Increased Review Time & Cognitive Load: While coding time decreases due to AI generation, the time spent reviewing code increases, elevating the cognitive load on developers [00:02:36].
• New Review Methods: To mitigate cognitive load, new review methods are emerging, such as:
  • Summary views that strip down code to essential elements for quick “yes/no” decisions [00:03:12].
  • Step-by-step reviews for multiple files, breaking down the process into a clear flow [00:03:37].
  • Visual reviews, like generating diagrams of code changes, which are easier to spot errors in compared to raw text [00:03:54].
• Moldable Development Environments: Integrated Development Environments (IDEs) are expected to adapt to specific code review needs, domains, and specifics, rather than presenting endless streams of text [00:04:10].
• Automated Commit Acceptance: Some approaches propose auto-committing AI-generated code based on heuristics, allowing for post-commit review if issues arise [00:04:38].
• Checkpoints for Longer-Running Agents: For extended AI agent operations, checkpoints allow developers to intervene or regenerate from specific points, avoiding repeated full reviews of the entire thought process [00:05:05].
• Setting Constraints and Permissions: Developers, much like managers, define rules for AI agents, such as locking files or specifying permissions to control what the AI can and cannot do [00:05:27].
• Cost Monitoring: As AI operations become longer-running, monitoring the cost of AI agent activities (e.g., per prompt or task) becomes an important management consideration [00:05:49].

2. From Implementation to Intent

This pattern involves a shift from developers focusing on the detailed implementation of code to primarily specifying the desired intent to AI agents [00:06:28].

Key aspects of this shift include:

• Specification Files: Simple markdown files or similar documents can serve as explicit specification files, defining functional or technical requirements that the AI uses to generate code, reducing repetitive prompting [00:06:39].
• AI-Generated Plans: AI tools can translate developer intent into step-by-step execution plans [00:07:07].
• Intent-Based Coding: The focus moves from chat-based interactions or text completion to defining tasks and allowing the AI to build a plan and generate the code [00:07:21].
• Specification-Centric Workflows: Entire development workflows can become specification-centric, where functional, technical, and security requirements are primarily defined through specifications, and the code generation process is largely abstracted [00:07:39].
• Program Manager Role: Developers may evolve into a role akin to a program manager, overseeing the process rather than deeply engaging with the code itself [00:08:05].

3. From Delivery to Discovery

This pattern emphasizes discovering the right problems to solve and ideas to build, rather than solely focusing on delivering code to production [00:08:31].

Key aspects of this shift include:

• Accelerated Prototyping: AI can rapidly design and create prototypes, enabling faster exploration of ideas and requirements [00:08:52].
• Multiple Iterations and Suggestions: AI can generate multiple versions and iterations of designs or features, allowing developers to choose the best option and suggesting ideas they might not have considered [00:09:16].
• Refined Discovery Process: The entire design-to-code process becomes an iterative loop focused on exploration and refinement [00:09:34].
• Customer Vibe Coding: Customers could potentially use AI to “vibe code” or directly interact with interfaces, allowing them to adapt products to their needs, similar to an A/B testing approach on steroids [00:09:56]. This aligns with AI accelerated need finding.

4. From Data to Knowledge

This pattern focuses on transforming insights gained throughout the development process and beyond into valuable, accessible knowledge [00:10:31].

Key aspects of this shift include:

• Learning from Production Issues: Knowledge can be extracted from production incidents, informing future code changes [00:10:49].
• Incident Response Lessons: AI can help derive lessons from incident responses, identifying failures, guiding new guidelines, or highlighting technologies for improvement [00:11:00].
• Code as Lessons: Code itself can be transformed into lessons, reducing onboarding time for new team members or capturing knowledge from departing personnel [00:11:14]. This relates to AI integration in documentation teams.
• Feature Memory: Maintaining a “feature memory” tracks past feature attempts and decisions, preventing re-exploration of dismissed ideas and preserving architectural choices [00:11:39].
• In-Flow Knowledge Capture: AI agents can suggest saving important information as knowledge during real-time chat and coding interactions, creating a beneficial learning loop for both humans and AI [00:12:05].
• Knowledge Application: Captured knowledge can be used to answer questions from others and improve future code solutions [00:12:26].

Conclusion

These four patterns — Producer to Manager, Implementation to Intent, Delivery to Discovery, and Data to Knowledge — illustrate how AI reshapes the developer’s role [00:13:37]. Developers move into areas traditionally associated with operations (managing), quality assurance/architecture (specifying intent), product ownership (discovery), and data engineering (knowledge capture) [00:12:41]. Ultimately, AI enables developers to operate more like highly experienced senior developers, extending beyond mere “faster typing” to encompass broader strategic and intellectual contributions [00:13:05].

For further exploration of AI native development tools and concepts, resources like nativedev.io curate a landscape of hundreds of tools [00:13:23].