EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Aug 10·5 min read
The Promise None of Them Kept
TL;DR
Mark Burgess's promise theory, which underpins autonomous agents in infrastructure management, has been revitalized by the introduction of large language models (LLMs) that enhance reasoning capabilities. This advancement allows agents to observe and act on live systems more effectively than previous tools like Chef and Puppet.
✦ Why It Matters
Explore integrating LLMs into your infrastructure tools to enhance decision-making capabilities.
Key Takeaways
- Evaluate Swamp for real-time infrastructure management capabilities.
- Transition from static state files to dynamic observation models.
- Leverage versioned data for better decision-making in infrastructure.
- Adopt local reasoning to enhance agent autonomy in operations.
How It Works
Swamp operates by allowing agents to directly observe live systems and capture their current state. It stores this information as versioned, schema-validated data, enabling agents to compare the current state against previous observations.
This process allows for local reasoning, where agents can decide whether to take action based on real-time data rather than relying on pre-defined instructions.