Dev Works

The next generation of building software will transform software engineers into engineering managers. Dev Works enables that future.

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Inspiration

The world of software development is at an inflection point. With the rise of powerful AI agents, the role of the software engineer is poised for a dramatic evolution. We were inspired by this shift to ask: What if we could move from being builders of software to being managers and conductors of intelligent agents who build the software for us? We envision a future where a single software engineer can manage a team of AI agents, multiplying their productivity and creativity by an order of magnitude. This project is our first step towards making that future a reality.

What It Does

This project is a web-based platform for managing the entire lifecycle of agent-driven software development. From the project's SpecBuilder, an engineer can define a high-level software specification. Our system helps translate that specification into a concrete set of tasks that can be assigned to different AI agents via the TaskManager. The engineer can then monitor the progress of these agents, review their work, and communicate with them through a Chatbot interface, providing feedback and guidance. It’s a command center for the future of software engineering.

What's Next for the Project

This is just the beginning. Our roadmap for this platform is ambitious and exciting. Here’s what’s next:

  • Agent Marketplace: We envision a marketplace where developers can find, train, and share specialized agents for a wide variety of software development tasks.
  • Multi-Agent Collaboration: We plan to enable multiple agents to collaborate on the same project, allowing for specialization (e.g., "frontend agent," "database agent") and more complex problem-solving.
  • Deeper IDE Integration: We want to build plugins for popular IDEs to bring this agent management experience directly into the developer's primary workflow.
  • Self-Improving Agents: Our ultimate goal is to create a system where the agents can learn from their mistakes and successes, continuously improving their performance over time.

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