All Blogs
What is an AI Agent Loop?
The AI Agent Loop is the code that runs an AI Agent. It asks the LLM what to do, runs that action, feeds back the result, and repeats until the task is done.
What is AI Agent Observability? Traces, Spans, and Metrics Explained
AI Agent Observability means recording every step an AI Agent takes, like each thought and tool call, so that we can see why it behaved the way it did.
How AI Agents Communicate
AI agents communicate by sending messages to each other to share information, ask for help, and work together. We will learn the main ways and protocols.
What are AI SubAgents? Why We Need Them and How They Work
An AI SubAgent is a smaller, specialized agent that works under a main agent to handle one part of a big task, like a team member under a manager.
What is LLM Evaluation? Metrics, Benchmarks, and Methods Explained
LLM Evaluation is the process of measuring how well a Large Language Model performs on the tasks we expect it to do, using metrics, benchmarks, and more.
How to Evaluate AI Agents? Metrics, Methods, and Best Practices
AI Agent Evaluation is the process of measuring how well an AI Agent performs a task, by checking the final result, the steps it took, and the tools it used.
What is AI Orchestration? How It Works and the Common Patterns
AI Orchestration is the process of coordinating many AI parts, like LLMs, tools, data sources, and agents, so that they work together to finish a complex task.
What is LLM as a Judge? How to Use an LLM to Evaluate LLM Outputs
LLM as a Judge is a technique where we use one LLM to evaluate the output of another LLM against our criteria, and give a score or a verdict with a reason.
What is Contrastive Learning? How It Works Step by Step
Contrastive Learning teaches a model by comparing things. It pulls similar things close together and pushes different things far apart. Let's see how it works.