Jan 17
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This video explains the progression from Generative AI → AI Agents → Agentic AI, and why building real autonomous systems requires multi-agent architectures. We break down the limitations of a single-agent setup and why it fails as tasks grow more complex: • Instruction Overload: When prompts, rules, or workflows become too long or too detailed, a single agent gets bogged down and outputs worse results. • Tool Overload: A single agent can’t reliably manage too many tools—most systems peak at 5–10 tools before performance drops. • Context Saturation: Long interactions quickly fill the LLM’s context window, causing the agent to forget, repeat, or break. These constraints make single-agent systems unsuitable for complex workflows like travel planning, onboarding flows, research assistants, or multi-step business automations. That’s where multi-agent systems come in. In a multi-agent architecture: - A manager agent interprets the goal. - It delegates tasks to specialized sub-agents (e.g., planning, retrieval, communication, coding, scheduling). - Each agent operates within a smaller, cleaner context, improving accuracy, reliability, and control. This structure enables: - Specialisation — Agents excel in specific domains. - Modularity — Easier building, debugging, and scaling. - Control & Transparency — Clear routing, state management, and communication between agents. We also cover common system designs, including the supervisor/manager-agent architecture, and why most production-grade agent systems rely on custom cognitive frameworks rather than generic “single-agent” prompting. Watch this video to learn the architectural foundations needed to build real, autonomous Agentic AI systems capable of handling real-world complexity.