Back

4 powerful loops that power Agentic AI. Here’s the easiest explanation of how each one works. 𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗟𝗢𝗢𝗣𝗦 Agentic Loops explain how AI agents think, act, learn, coordinate, and improve over time using structured cycles. 1. 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗟𝗼𝗼𝗽 The Collaboration Loop allows multiple agents to communicate, divide tasks, and work together smoothly. 1. Define the shared goal all agents must achieve. 2. Share context or information each agent already knows. 3. Assign roles so each agent handles a specific part of the task. 4. Create a plan describing who does what and in what order. 5. Agents perform their individual tasks. 6. Combine all outputs into one final result. 7. Resolve conflicts when agents produce mismatched results. 8. Review team performance to improve coordination next time. _____________________________ 2. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗟𝗼𝗼𝗽 The Memory Loop helps agents store, organize, and retrieve information to stay consistent and context-aware. 1. Receive new information or input from the user/environment. 2. Decide whether it’s short-term or long-term memory. 3. Encode or summarize the information into a usable format. 4. Save it in the correct memory store. 5. Ensure new memory doesn’t conflict with existing facts. 6. Remove duplicate or low-value entries. 7. Summarize important knowledge for quick future access. 8. Retrieve the right memory whenever the agent needs it. _____________________________ 3. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽 The Feedback Loop collects user or system feedback continuously to improve behavior and fix errors. 1. Capture feedback from the user, logs, or system events. 2. Analyze the feedback to understand what went wrong. 3. Identify the specific issue or failure point. 4. Suggest corrections to fix the problem. 5. Communicate findings to other components or agents. 6. Update the agent’s logic, model, or process. 7. Validate whether the fix actually worked. 8. Apply the improvements and prepare for new feedback. _____________________________ 4. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗟𝗼𝗼𝗽 The Learning Loop helps agents improve over time by absorbing new data, feedback, and real-world experience. 1. Collect new data from interactions or environments. 2. Identify patterns, trends, or repeated behaviors. 3. Measure accuracy or performance. 4. Update the agent’s knowledge with new insights. 5. Expand or retrain the underlying model. 6. Reinforce actions that produced good outcomes. 7. Test the updated agent for stability and quality. 8. Keep the core logic intact while improving capabilities. ✅ 𝗜𝗻 𝘀𝗵𝗼𝗿𝘁: • 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗟𝗼𝗼𝗽 = Agents working together. • 𝗠𝗲𝗺𝗼𝗿𝘆 𝗟𝗼𝗼𝗽 = Agents storing and recalling information. • 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽 = Agents improving from responses. • 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗟𝗼𝗼𝗽 = Agents getting smarter over time. ✅ Repost for others in your network who can benefit from this. Activate to view larger image,