In this article, you will learn how to evaluate AI agents rigorously by examining their full execution process rather than only their final outputs.
Author Archive | Bala Priya C
AI Agent Tool Design: What Works and What Doesn’t
In this article, you will learn how tool design — not model capability — is the root cause of most AI agent failures, and what concrete design patterns you can apply to fix it.
Building Context-Aware Search in Python with LLM Embeddings + Metadata
In this article, you will learn how to build a context-aware semantic search engine in Python that combines embedding-based similarity with structured metadata filtering.
Building Vector Similarity Search in PostgreSQL with pgvector
In this article, you will learn how to implement vector similarity search in PostgreSQL using the pgvector extension, allowing you to find semantically similar results based on meaning rather than keyword matching.
Choosing the Right Agentic Design Pattern: A Decision-Tree Approach
In this article, you will learn how to apply a structured decision tree to choose the right agentic design pattern for any AI system you are building.
LLM Observability Tools for Reliable AI Applications
In this article, you will learn about seven leading LLM observability tools that help AI engineers monitor, evaluate, and debug large language model applications running in production.
The Roadmap to Mastering Tool Calling in AI Agents
In this article, you will learn how to design, scale, and secure tool calling in AI agents so that the layer connecting model reasoning to real-world action holds up in production.
Agentic RAG Explained in 3 Levels of Difficulty
In this article, you will learn what agentic RAG is, how it differs from traditional RAG, and when to use it.
Building AI Agents in Python with Pydantic AI
In this article, you will learn how to build production-ready AI agents in Python using Pydantic AI, with structured outputs, custom tools, and dependency injection.
Effective Context Engineering for AI Agents: A Developer’s Guide
In this article, you will learn what context engineering is and how to apply it systematically to keep AI agents reliable, cost-efficient, and accurate in production.