EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Aug 31·5 min read
The End of Software Engineering
TL;DR
AI agents are transforming software engineering by shifting the role of code from static decision logic to dynamic, ephemeral tools driven by large language models (LLMs). This change marks a fundamental restructuring of the software paradigm, leading to the emergence of Agentic Engineering.
✦ Why It Matters
Start exploring Agentic Engineering to adapt your software development practices for the future.
Key Takeaways
- Embrace the shift to Agentic Engineering in your projects.
- Focus on articulating clear intents for AI agents.
- Understand the coordination of multiple AI agents in workflows.
- Develop frameworks for evaluating AI-generated outcomes.
How It Works
Agentic systems utilize large language models (LLMs) as reasoning engines that dynamically generate code based on user intent. Unlike traditional software, where decision logic is pre-coded, these systems create and discard code as needed, allowing for more flexible and efficient problem-solving.
This approach decouples the complexity of software development from human cognitive limits, enabling agents to handle tasks that would be infeasible for human engineers alone.