Category: AI Agents
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Human-in-the-loop (HITL) in LangGraph is a mechanism that allows pausing graph execution to request human intervention, enabling validation, approval, or adjustment of decisions before the flow continues. Persistence in LangGraph allows saving the graph state throughout execution using checkpoints stored in a persistent storage. This enables the workflow to be interrupted and later resumed from…
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LangGraph is a framework designed for building complex graph-based workflows, enabling the explicit modeling of systems with multiple steps and decision points. Unlike traditional linear flows, it uses nodes (execution units) and edges (transitions between nodes) to define system behavior. This approach provides a high level of control, allowing for conditional branching, parallel node execution,…
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This post aims to demonstrate how to integrate the Model Context Protocol (MCP) into the example from the previous article (click here), where I used the Agent2Agent Protocol (A2A) to implement a CRUD system. Therefore, I highly recommend reading the previous post first, as it provides the foundation to better understand this content. For better…
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Most agent systems operate in isolation, restricted to their own infrastructure. In practice, this means that an agent built within a company or hosted on a cloud server cannot easily communicate with another agent created in a different environment. The Agent2Agent Protocol (A2A) was designed to solve exactly this problem. It enables agents to connect…
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FastMCP is a framework that simplifies the creation of servers compatible with the MCP protocol. If you want to learn more about MCP, click here to read my post on the topic. In this post, I’ll cover: Use Case: Automatic Report Sending via Email How to Create a Server Using FastMCP Server Integration with LangChain…
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The Model Context Protocol (or simply MCP) is a protocol developed by Anthropic that aims to standardize access to external tools used by AI applications. Imagine you are developing a system of AI Agents that need to access information from the internet. With the MCP protocol, there’s no need to create or adapt how these…
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In this post, I will cover: What Flows are Use Case: Video Transcription for Course Offer Creation Practice: Flow Structure Practice: Exploring the Code Results: Messages Generated for the Course Offer If you’d like to dive deeper into using AI Agents with CrewAI, click here to check out other posts. What Flows are Flows are…
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In this post, I will cover: Training and Testing a Multi AI-Agent System using CrewAI Practice: Training and Testing a Sequential Process Practice: Training and Testing a Hierarchical Process If you want to learn more about AI Agents using CrewAI, click here to see other content. Training and Testing a Multi AI-Agent System using CrewAI…
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In this post, I will cover: What is a Hierarchical Process? Using a Custom Manager Agent Adding Planning to the Hierarchical Process Using ChatOpenAI as the Hierarchical Manager To see other posts related to AI Agents with CrewAI, click here — you’ll find details that won’t be covered in this post. What is a Hierarchical…
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In this post, I’ll cover: What are Tools? How to Use Tools Provided by CrewAI? How to Create Your Own Tool? Practical: Sending a Recruitment Email to a Candidate. If you’d like to learn more about AI Agents, click here to check out the post where I go into more detail. What are Tools? Tools…
