Fundamentals of AI Agents | Rakesh Gohel

Want to build AI Agents that scale?? Join me for my new cohort next year: https://maven.com/rakeshgohel/ai-agent-engineering-react-rag-multi-agent?utm_campaign=NjQ4MjYx&utm_medium=clp_share_link&utm_source=instructor&promoCode=FAST25 If you're serious about AI Agents, understanding these fundamentals isn't optional; it's essential. 📌 Let me break down the 7 key concepts: 1\. Agents Under the Hood AI Agents are not about just models and frameworks. They are more about: - Agent Auth: Secure identity and access control - Memory: Short-term and long-term context retention - Tools: External APIs and function calling capabilities - Frameworks: ReACT, Reflexion, LangGraph orchestration - Model Routers: Dynamic model selection based on task - Models: Foundation LLMs powering agent reasoning - Infrastructure: Scalable deployment and execution environment 2\. SPAR Framework (Understanding an AI Agent) - Sense: Understanding and processing inputs - Plan: Strategic thinking and building execution plans - Act: Generating responses and taking actions - Reflect: Self-correction and learning from errors Learn more here: https://www.linkedin.com/posts/rakeshgohel01_ai-agents-fail-when-you-skip-the-steps-that-activity-7379493573202804736-TIl5?utm_source=share&utm_medium=member_desktop&rcm=ACoAADbIlqoBXsMJDd5Rmp0n6hdlqpbGlM-mkP4 3\ 3M's of Governance - Map: Document agent capabilities and workflows - Measure: Track performance, accuracy, and compliance - Manage: Continuous monitoring and optimization 4\ 5P's of Guardrails - Protocols: Standardized operating procedures - Pre-Check: Input validation before processing - Permissions: Access control and authorization - Post-Check: Output validation and safety checks - Protection: Security layers and risk mitigation Learn more here: https://www.linkedin.com/posts/rakeshgohel01_your-ai-agents-are-only-as-strong-as-the-activity-7391475341669666816-AVbT?utm_source=share&utm_medium=member_desktop&rcm=ACoAADbIlqoBXsMJDd5Rmp0n6hdlq