Agentic AI Explained | Lecture 8 – Multi-Agent Systems, Evaluation Optimizer & Prompting скачать в хорошем качестве

Agentic AI Explained | Lecture 8 – Multi-Agent Systems, Evaluation Optimizer & Prompting

Agentic AI Course – Lecture 8 | Multi-Agent Systems Deep Dive & Advanced Prompting** In this lecture, we go *deep into Multi-Agent Systems**, the architecture behind **scalable, production-grade Agentic AI solutions* used in real companies. You will learn that modern AI systems are not built using a single prompt or a single agent. Instead, they rely on **multiple coordinated agents**, each with a defined responsibility, working together under structured patterns. This lecture explains *four fundamental multi-agent patterns* used across AI research and industry, followed by an **in-depth breakdown of prompting strategies**, including **proactive and reactive prompting**, which determine how intelligent and reliable an agent behaves. --- 🧠 Multi-Agent Systems Explained (In Depth) What is a Multi-Agent System? A multi-agent system is an architecture where: Multiple AI agents work together Each agent performs a specific task Coordination is managed through defined control patterns Instead of one overloaded agent, intelligence is **distributed**. --- 🔹 Four Core Types of Multi-Agent Systems --- 1️⃣ Prompt Chaining *What it is:* Prompt chaining breaks a complex task into **sequential steps**, where the output of one agent becomes the input for the next. *How it works:* 1. Agent A analyzes the problem 2. Agent B plans the solution 3. Agent C executes the task 4. Agent D validates the result *Why it matters:* Improves reasoning quality Reduces hallucinations Makes workflows debuggable *Real-World Use Cases:* Content generation pipelines Business process automation Data cleaning and transformation --- 2️⃣ Routing *What it is:* Routing decides *which agent should handle a task* based on intent, context, or conditions. *How it works:* A router agent classifies the request Routes it to the correct specialized agent *Example:* Support query → Support agent Sales query → Sales agent Technical query → Technical agent *Why it matters:* Efficiency Clear separation of responsibilities Better scalability --- 3️⃣ Parallelization *What it is:* Parallelization allows *multiple agents to work at the same time* on different parts of a task. *How it works:* One request is split into sub-tasks Multiple agents process simultaneously Results are merged *Why it matters:* Faster execution Better coverage Redundancy and reliability *Example Use Cases:* Research from multiple sources Multi-criteria analysis Data validation --- 4️⃣ Evaluation Optimizer *What it is:* An evaluation optimizer is a feedback-driven system where one agent *evaluates**, **scores**, and **improves* the output of another agent. *How it works:* 1. Generator agent produces output 2. Evaluator agent reviews quality 3. Optimizer refines prompt or logic 4. Loop continues until quality threshold is met *Why it matters:* High-quality outputs Continuous improvement Production-grade reliability --- 🧠 Prompting in Depth Prompting is not just “writing good instructions.” It defines **how an agent thinks and behaves**. --- 🔹 Proactive Prompting *Definition:* The agent *anticipates future needs* and takes action before being explicitly asked. *Characteristics:* Goal-driven Context-aware Initiative-based *Example:* An AI receptionist: Remembers user preferences Suggests next steps Follows up automatically *Why it matters:* Feels intelligent Reduces user effort Enables autonomous systems --- 🔹 Reactive Prompting *Definition:* The agent *responds only when triggered* by user input or an event. *Characteristics:* Event-driven Safer and controlled Easier to debug *Example:* Chatbot replies only when asked Workflow triggers on webhook or message *Why it matters:* Predictability Compliance Lower risk --- ⚖️ Proactive vs Reactive Prompting | Aspect | Proactive | Reactive | | -------- | ---------------------- | ------------------- | | Control | Lower | Higher | | Autonomy | High | Limited | | Risk | Higher | Lower | | Use Case | Intelligent assistants | Business automation | Production systems often use **both together**. --- 🎯 Who This Lecture Is For Advanced Agentic AI students AI automation engineers Developers building multi-agent systems Freelancers working on complex AI solutions Anyone moving from demos to *real AI architectures* --- 📌 Course Context This is *Lecture 8* of the **Agentic AI & Workflow Automation Course**, focusing on **advanced agent architectures and intelligent prompting**, preparing students for **enterprise-level AI systems**.