- AI Engineering
- Posts
- AI Agents vs Agentic AI
In today’s newsletter:
Voxel 51 introduced Verified Auto Labeling
AI Agents vs Agentic AI clearly explained
DocETL: Python library for Agentic LLM-powered data processing
Reading time: 3 minutes.
TOGETHER WITH VOXEL 51
Better DATA = Better MODELS!
But Manual labeling is one of the biggest bottlenecks in deploying computer vision. It's expensive, time-consuming, and hard to scale.
Verified Auto Labeling solves this.
The new research report from the Voxel51 ML team shows that foundation models like Grounding DINO and YOLO-World can reach up to 95% of human-level performance on object detection tasks — with zero manual labels.
Even more interesting: cleaner labels aren’t always better. The research found that moderate confidence thresholds (0.2–0.5) outperformed high ones (0.8–0.9), which often reduced recall and hurt downstream accuracy.
With the right setup, auto-labeling can:
Reduces labeling costs by up to 100,000×
Speeds up annotation by 5,000×
Delivers high-quality, consistent training data
This paper summarizes the key distinctions between AI Agents and Agentic AI, covering their architecture, reasoning mechanisms, memory usage, and level of autonomy.
Here are the key differences:
AI Agents:
AI Agents are single systems powered by LLMs and tools, built for task-specific automation. They are reactive, goal-driven, and operate within fixed scopes like retrieval, summarization, or scheduling.
Examples include agents that search and summarize documents, prioritize emails, or help with customer queries using tool-augmented LLMs.
Agentic AI:
Agentic AI involves multiple agents working together. These agents coordinate, share memory, decompose goals, and adapt to changing tasks. The systems are designed for collaborative decision-making and dynamic workflows.
Examples include multi-agent research systems where one agent retrieves papers, another summarizes them, and a third writes a draft; or robotic teams where drones detect crop issues and ground robots respond with targeted actions.
Python library for Agentic LLM-powered data processing!
DocETL is a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks.
Here’s why this is a game changer:
Build and refine prompt workflows faster with an interactive UI playground built for iterative development
Deploy pipelines at scale using a Python package that runs from the command line or within your codebase
It's 100% Open Source
That’s a Wrap
That’s all for today. Thank you for reading today’s edition. See you in the next issue with more AI Engineering insights.
PS: We curate this AI Engineering content for free, and your support means everything. If you find value in what you read, consider sharing it with a friend or two.
Your feedback is valuable: If there’s a topic you’re stuck on or curious about, reply to this email. We’re building this for you, and your feedback helps shape what we send.
WORK WITH US
Looking to promote your company, product, or service to 120K+ AI developers? Get in touch today by replying to this email.