Introduction: Single agents hit limits—they can’t be experts at everything, they struggle with complex multi-step tasks, and they lack the ability to parallelize work. Multi-agent systems solve these problems by coordinating multiple specialized agents, each with distinct capabilities and roles. This guide covers practical multi-agent patterns: orchestrator agents that delegate and coordinate, specialist agents with […]
Read more →Category: Technology Engineering
Technology Engineering
Agentic Workflow Patterns: Building Autonomous AI Systems That Plan, Act, and Learn
Introduction: Agentic workflows represent a paradigm shift from simple prompt-response patterns to autonomous, goal-directed AI systems. Unlike traditional LLM applications where the model responds once and stops, agentic systems can plan multi-step solutions, execute actions, observe results, and iterate until the goal is achieved. This guide covers the core patterns that make agentic systems work: […]
Read more →The Python Renaissance: Why 2025 Is the Year Everything Changed for Data Engineers
🎓 AUTHORITY NOTE This analysis draws from 20+ years of Python experience in enterprise data engineering, covering production deployments at scale across multiple Fortune 500 companies. Executive Summary Something remarkable happened in the Python ecosystem over the past year. After decades of incremental improvements, we’ve witnessed a fundamental shift in how data engineers approach their […]
Read more →DevSecOps: Integrating Security into DevOps – Part 2
Continuing from my previous blog, let’s dive deeper into the implementation of DevSecOps. Integrating Security into DevOps To implement DevSecOps, it is essential to integrate security into every phase of the DevOps lifecycle. The following are the key phases in DevOps and how to integrate security into each phase: DevSecOps Best Practices Here are some […]
Read more →Embedding Model Selection: Choosing the Right Model for Your RAG System
Introduction: Choosing the right embedding model is critical for RAG systems, semantic search, and similarity applications. The wrong choice leads to poor retrieval quality, high costs, or unacceptable latency. OpenAI’s text-embedding-3-small is cheap and fast but may miss nuanced similarities. Cohere’s embed-v3 excels at multilingual content. Open-source models like BGE and E5 offer privacy and […]
Read more →Retrieval Augmented Generation Patterns: Building RAG Systems That Actually Work
Introduction: Retrieval Augmented Generation (RAG) grounds LLM responses in your actual data, reducing hallucinations and enabling knowledge that wasn’t in the training set. But naive RAG—embed documents, retrieve top-k, stuff into prompt—often disappoints. Retrieval misses relevant documents, context windows overflow, and the model ignores important information buried in long contexts. This guide covers advanced RAG […]
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