Ai-agent
Design a Real-Time Voice AI Agent
In this blog, we will learn about how to design a Real-Time Voice AI Agent, a system that listens to a person speaking, understands what they said, thinks about it, takes actions if needed, and talks back in a natural human-like voice, all within a fraction of a second. We will also see why voice is much harder than a text chatbot, the two big ways to build it (the cascaded pipeline of Speech-to-Text, LLM, and Text-to-Speech versus the end-to-end Speech-to-Speech model), how the agent knows when the user has stopped talking, how we handle interruptions, how tools and memory fit in, how we scale it to thousands of calls, the edge cases that break a voice agent in production, the pros and cons of every approach, and when to use which one.
What is Graph Engineering?
In this blog, we will learn about Graph Engineering, the practice of building an AI system as a graph of small steps connected by clear paths instead of one giant prompt or one endless loop. We will also see why we need it, what nodes and edges actually mean, how the state travels through the graph, how conditional edges take decisions, how cycles let the system do the work again, how parallel branches save time, how checkpoints let us pause and resume, and where it works well and where it fails.
What is Loop Engineering?
In this blog, we will learn about Loop Engineering, the practice of designing the repeating cycle that an AI agent runs again and again until a task is actually finished. We will also see why we need it, what one turn of the loop looks like, the parts of the loop that we must control, how it is different from prompt engineering and context engineering, the common ways a loop breaks, the techniques that fix those breaks, and where it works well and where it fails.
What are Agent Skills?
In this blog, we will learn about Agent Skills. We will also see why we need them, what is inside a Skill, how the description makes an agent pick up the right Skill by itself, how progressive disclosure keeps the context window free, how Agent Skills differ from MCP, and how we can create our own Skill.
What is MCP (Model Context Protocol)?
In this blog, we will learn about MCP (Model Context Protocol). We will also see the problem it solves, the pieces it is made of, how a request travels from the AI model all the way to a tool and back, and what we must be careful about while using it.
What is OKF (Open Knowledge Format)?
In this blog, we will learn about OKF (Open Knowledge Format). We will also see why the knowledge about our data stays scattered across many places, how OKF writes that knowledge down as a folder of plain markdown files which any AI agent or any tool can read, and where it fits along with MCP and Agent Skills in the real world.
How does LangGraph work?
In this blog, we will learn about how LangGraph works. We will also see why we need it, what graphs, state, nodes, and edges are, how tools work and who actually calls them, how memory and human-in-the-loop fit in through a complete example, and when to use it in the real world.
How does LangChain work?
In this blog, we will learn about how LangChain works. We will also see why we need it, what chains, prompts, memory, and output parsers are, how retrieval and agents fit in, and how the full flow works together in the real world.
How do Computer-Use Agents work?
In this blog, we will learn about how computer-use agents work.
What is Sakana Fugu? The Technical Report Explained
In this blog, we are going to learn about Sakana Fugu, a family of AI models that work like a conductor for a team of other AI models.
How does Function Calling work in LLMs?
In this blog, we will learn about how Function Calling works in LLMs. We will see what it is, why we need it, the key insight behind it, and how it powers AI agents and assistants step by step.
What is an AI Agent Loop?
In this blog, we will learn about the AI Agent Loop - what it is, why an AI Agent needs it, the think-act-observe cycle that powers it, how the loop knows when to stop, and the common ways the loop fails.
What is AI Agent Observability? Traces, Spans, and Metrics Explained
In this blog, we will learn about AI Agent Observability. We will also see why we need it, how it is different from normal software monitoring, what we must observe inside an agent, the key concepts like traces and spans, the metrics we must track, the tools we can use, and the best practices to follow.
How AI Agents Communicate
In this blog, we will learn about how AI agents communicate. We will understand why agents need to communicate, the main ways they talk to each other, the message format, and the protocols that make agents work together to finish complex tasks.
What are AI SubAgents? Why We Need Them and How They Work
In this blog, we will learn about AI SubAgents. We will understand what they are, why we need them, how they work, and how to use them to build AI systems that can handle big and complex tasks.
How to Evaluate AI Agents? Metrics, Methods, and Best Practices
In this blog, we will learn about AI Agent Evaluation. We will also see why it is different from LLM Evaluation, the types of evaluation we can do, the key metrics we must track, the methods we can use, and the best practices to follow.
What is AI Orchestration? How It Works and the Common Patterns
In this blog, we will learn about AI Orchestration. We will understand what it is, why we need it, how it is different from AI Agents, and the common patterns we use to coordinate multiple LLMs, tools, and steps together to build real AI products.
What is Context Engineering?
In this blog, we will learn about Context Engineering - what it is, why it has become the most important skill for building reliable AI applications, how it differs from Prompt Engineering, the components that make up the context, common patterns like RAG, few-shot examples, tools, and memory, and the best practices and common mistakes to keep in mind.
What is a Reflection Agent? How It Generates, Critiques, and Revises
In this blog, we will learn about the Reflection Agent - what it is, how it is built, its anatomy, how it generates, critiques, and revises its own work, and how to handle its common failure modes.
What is GraphRAG? How Knowledge Graphs Improve RAG
In this blog, we will learn about GraphRAG and how it improves retrieval by using a knowledge graph along with vector search.
What is a Plan-and-Execute Agent and How Does It Work?
In this blog, we will learn about the Plan-and-Execute Agent - what it is, its anatomy, how it plans and runs the steps, how it differs from a ReAct Agent, and how to handle its common failure modes.
What is Agentic RAG? How It Works and When to Use It
In this blog, we will learn about Agentic RAG - what it is, why standard RAG falls short, the agentic RAG loop, the three building blocks, the common patterns, when to use it, and the limitations to keep in mind.
What is a ReAct Agent? How It Thinks and Acts, Explained
In this blog, we will learn about the ReAct Agent - what it is, how it is built, its anatomy, how it thinks and acts, and how to handle its common failure modes.
What are Multi-Agent Systems and When Should We Use Them?
In this blog, we will learn about Multi-Agent Systems - what they are, the three pillars that hold them together, the common agent roles, how agents communicate and coordinate, the trade-offs, and when to use them.
How Does AI Agent Memory Work? The Memory Stack Explained
In this blog, we will learn about AI Agent Memory - why agents need it, the memory stack, the four core operations (write, read, update, forget), how memory flows at runtime, and the common mistakes.
What is an AI Agent? How It Works
In this blog, we will learn about the AI Agent - what it is, how it is different from a plain LLM, its five core parts, how it works end to end, the main types, and when to use one.
What is Harness Engineering?
In this blog, we will learn about Harness Engineering in AI.