TechPrufer
Get started
David Ondrej
David Ondrej logoDavid Ondrej · Feature trail

Agentic Engineering Workflows

14 videos · Aug 2026 – Oct 2026

What this trail covers

This post continues the discussion on agentic engineering workflows, emphasizing the practical application and potential of AI agents in automating and enhancing the software development lifecycle. It reinforces the idea that developers can shift their focus from direct code interaction to managing and orchestrating AI agents for tasks like code comprehension, modification, and potentially entire development processes. The scalability of running numerous agents and the implications for developer productivity and the future of software development are key themes. The post predicts a significant shift in how engineering will be conducted by 2027, moving towards more abstract, agent-driven development paradigms.

All David Ondrej trails
  1. Agentic Engineering in 2027 will look completely differentThis post predicts that agentic engineering in 2027 will be fundamentally different from today, characterized by 'Software Factories,' 'Agentic Loops,' and 'Cloud Agents.' It suggests a move away from direct coding towards managing and orchestrating AI agents for complex development tasks, implying a significant evolution in developer workflows and the overall software development lifecycle.
  2. Vercel CEO reveals his Agentic Engineering WorkflowThis post introduces Guillermo Rauch's personal agentic engineering workflow, highlighting his use of AI agents for development tasks and his creation of the 'fx' coding harness. It delves into the practical application of agentic systems for developers, discussing how AI can impact cybersecurity and the rationale behind building Cloudroom. The conversation emphasizes the shift from direct coding to agent orchestration and the potential for AI to revolutionize the software development lifecycle.
  3. You're betting against AI if you're doing this...The post argues that if developers are still performing tasks that AI agents can now handle, such as writing boilerplate code, debugging, or even complex code refactoring, they are effectively betting against the advancement and adoption of AI in software development. It implies that embracing AI agents for these tasks is crucial for staying relevant and efficient in modern engineering. The core technical contribution is the framing of current development practices against the backdrop of rapidly advancing AI capabilities, suggesting a paradigm shift where developers' roles evolve towards higher-level orchestration and strategic decision-making rather than direct, low-level coding.
  4. Why every Agent needs it's own computerThis post argues that each AI agent should have its own dedicated computing environment (a "computer") to manage complexity, isolation, and resource allocation. This approach is presented as a solution to the challenges of scaling and managing numerous agents, suggesting that providing each agent with its own "machine" enhances reliability, simplifies debugging, and allows for better resource control. The core technical claim is that this dedicated environment strategy is crucial for robust agentic systems.
  5. Ex-Apple dev reveals his Agentic Engineering WorkflowThis post introduces the concept of an 'Agentic Engineering Workflow' as revealed by an ex-Apple developer, Daniel Miessler. The core idea is to leverage AI agents to automate and enhance the software development lifecycle, shifting developer focus from direct coding to orchestrating AI agents for tasks like code comprehension and modification. The workflow aims to accelerate the 'inner loop' of development, enabling faster shipping of software. The post highlights the potential for AI agents to significantly improve developer productivity and streamline the development process.
  7. The Best Agentic Engineering GUIThis post introduces Cloudroom.dev as a potentially superior GUI for agentic engineering workflows. While the post itself is brief and promotional, it implies that the GUI offers significant improvements in user experience for interacting with and managing AI agents in an engineering context. The core technical contribution is the presentation of this GUI as a solution to enhance the usability of agentic engineering tools.
  8. Watch this 100x developer use GPT-6 Astra… it’s insaneThis post contributes to the understanding of agentic engineering by showcasing a specific setup (Magnus's) that leverages AI agents for development tasks. It raises questions about the scalability of agent systems (1000 agents) and posits a future where human code reading is obsolete due to agent capabilities. The mention of 'GPT-6 Astra' suggests advancements in agent intelligence and functionality.
  9. Pi Agent dev reveals his Agentic Engineering WorkflowThis post provides a concrete example of an 'Agentic Engineering Workflow' through the lens of a "Pi Agent dev." It highlights the practical implementation and potential benefits of using AI agents in the software development lifecycle, specifically mentioning Armin Ronacher (creator of Flask) as a reference point for the caliber of discussion. The content reinforces the ongoing evolution of developer tooling and processes driven by AI, suggesting a future where developers may interact with code less directly and more through agentic systems.
  10. Fireworks CTO: Why AI Is About To Get 1000x BetterDmytro from Fireworks AI discusses his Agentic Engineering Workflow, highlighting the future of open-source models and how AI is poised for significant improvement. The post implicitly suggests advancements in AI model capabilities and their integration into development workflows, aligning with the broader theme of agentic engineering.
  11. My Agentic Engineering Workflow (after 6,775 sessions)David Ondrej details his personal agentic engineering workflow, sharing his 'Skills' repository and insights gained from over 6,775 sessions. The post highlights the practical application of AI agents in the development process, suggesting a shift in developer focus towards orchestration and management of these agents. It serves as a case study for implementing and refining agentic workflows for enhanced productivity.
  12. $75M founder reveals his Agentic Engineering setupThis post features a podcast episode with Alex Lieberman and Dan Zakon discussing their 'Agentic Engineering setup.' While framed as an overview of their approach, the core technical contribution lies in the practical insights shared about leveraging AI agents for software development. The discussion touches upon tools like Cursor and the broader concept of agentic workflows, implying a shift in how engineers interact with code and development processes. The mention of 'DeepAPI' and 'Topview' suggests an ecosystem of tools supporting this agentic paradigm. The emphasis is on the practical implementation and adoption of AI agents in the engineering workflow.
  13. Ex-Uber dev explains his Multi-Agent WorkflowThis post introduces the concept of providing agents with a "ultimate API" (deepapi.co) for enhanced capabilities, and promotes a Skool community for learning AI coding. It also highlights the role of AI agents in the inner-loop development process, suggesting that developers can leverage these agents for tasks like code migration and modification, thereby changing how they work. The discussion implies a shift towards agentic workflows for software development.
  14. Ex-NASA dev reveals his Agentic Engineering WorkflowThis talk introduces the concept of an 'Agentic Engineering Workflow' as demonstrated by the 'Dexter Software Factory'. It highlights how AI agents can be leveraged to automate various aspects of the software development lifecycle, from code generation and modification to testing and deployment. The discussion emphasizes the shift in developer focus from direct coding to orchestrating and managing AI agents. Specific tools and platforms like Dexter, HumanLayer, and Cursor are mentioned as enablers of this workflow. The talk also touches upon the potential for AI agents to assist in tasks like migrating existing systems, suggesting a future where AI plays a more central role in software engineering.
  15. Let agents write your testsThis post introduces the concept of using AI agents to automatically write tests for software. It highlights how agents can analyze code and generate relevant test cases, thereby improving developer productivity and code quality. The contribution focuses on the practical application of agentic workflows within the inner loop of software development, specifically for test generation.