Inside an Agent Harness: Architecture, Memory, MCP & Jailbreaking AI Agents

𝗔𝗴𝗲𝗻𝘁 𝗛𝗮𝗿𝗻𝗲𝘀𝘀 — 𝗨𝗻𝗱𝗲𝗿 𝘁𝗵𝗲 𝗛𝗼𝗼𝗱 Today, I want to take you inside an Agent Harness. We’ll start with a real-world use case, watch the agent in action, and then try to 𝗷𝗮𝗶𝗹𝗯𝗿𝗲𝗮𝗸 𝗶𝘁. Then we’ll deep dive into the architecture — 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, 𝗠𝗖𝗣, 𝗛𝗜𝗧𝗟, and short-term persistent memory. More importantly, we’ll explore the problems that actually matter when building agents for the real world: • 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗥𝗼𝘁 & 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗗𝗶𝗹𝘂𝘁𝗶𝗼𝗻 • 𝗗𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗕𝗶𝗮𝘀 • 𝗟𝗲𝘅𝗶𝗰𝗮𝗹 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗕𝗶𝗮𝘀 • 𝗘𝗥𝗣 𝗗𝗮𝘁𝗮 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗮𝗺𝗶𝗻𝗮𝘁𝗶𝗼𝗻 • Design principles behind 𝗖𝗼𝗧, 𝗥𝗲𝗔𝗰𝘁 & effective agents As always, I strongly encourage you to 𝗯𝘂𝗶𝗹𝗱. If you need help, consultation, or hands-on guidance, feel free to reach out. It would be my pleasure to help you grow. 𝗔 𝗿𝗲𝗾𝘂𝗲𝘀𝘁 𝘁𝗼 𝗺𝘆 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆: If you’re a Hiring Manager — or know someone solving these complex problems and looking for a seasoned Data Scientist to help with 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝗠𝗟 & 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝗮𝘁 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗦𝗰𝗮𝗹𝗲 — I’d really appreciate an introduction. Please 𝗟𝗶𝗸𝗲 & 𝗦𝗵𝗮𝗿𝗲 this post. It helps me reach the right teams. And leave me a comment. It helps me stay honest. 🙂