Inspiration
Developers spend a lot of time doing repetitive tasks like reviewing code, running tests, and checking deployments. These are necessary but time-consuming. We wanted to build an autonomous assistant that saves developer time by automating these tasks with AI agent architecture.
What it does
Our Autonomous Developer Assistant continuously monitors code changes (loop agent).
When it detects a change, it triggers parallel agents:
- Code Reviewer Agent → flags issues such as TODOs or missing practices.
- Test Runner Agent → executes the code to check for errors.
Both run in parallel and return a final combined report for the developer.
How we built it
- Used Python to create a loop monitor that watches
main.py. - Implemented two lightweight agents (reviewer + tester).
- Used parallel threading to simulate multiple agents working together.
- Designed the architecture inspired by Google’s ADK (autonomy + lifecycle) and A2A protocol (agent-to-agent communication).
Challenges we ran into
- Limited time (only 2 hours) forced us to build a very lightweight MVP.
- Understanding how to represent ADK/A2A concepts in a short hackathon.
- Ensuring the demo worked reliably under time pressure.
Accomplishments that we're proud of
- Built a working autonomous system in under 2 hours.
- Successfully showcased continuous loop + parallel agent architecture.
- Created a simple but impactful demo that judges and developers can understand instantly.
What we learned
- The value of loop architecture for autonomy — no babysitting required.
- The importance of parallel agents for efficiency and modular design.
- How ADK and A2A can inspire scalable agent ecosystems.
What's next for Developer Assistant
- Add more agents: Bug Fixer, Deployment Validator, Documentation Writer.
- Integrate with real developer workflows (GitHub, CI/CD pipelines).
- Extend to enterprise use cases where dozens of agents can collaborate.
Built With
- a2a
- adk
- python
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