How Robots Process User Feedback During Tasks

We often think of AI agents as black boxes: you give a prompt, and it replies. But 𝗯𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝘀𝗰𝗲𝗻𝗲𝘀, there’s a complex, multi-layered orchestration of memory, reasoning, tool use, and learning. This visual captures the 𝗳𝘂𝗹𝗹 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 — 𝗳𝗿𝗼𝗺 𝗣𝗿𝗼𝗺𝗽𝘁 𝘁𝗼 𝗔𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸, across 𝗺𝗮𝗻𝘆 𝗶𝗻𝘁𝗲𝗿𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱 𝘀𝘁𝗮𝗴𝗲𝘀. 1. 𝗜𝘁 𝘀𝘁𝗮𝗿𝘁𝘀 𝘄𝗶𝘁𝗵 𝗻𝗮𝘁𝘂𝗿𝗮𝗹 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 – but that’s just the trigger.      The agent immediately cleans, tokenizes, and checks readiness before doing anything else. 2. 𝗜𝗻𝘁𝗲𝗻𝘁 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹.      Without knowing 𝘸𝘩𝘢𝘵 the user actually wants (search vs summarize vs act), the agent can’t plan effectively. 3. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 + 𝗠𝗲𝗺𝗼𝗿𝘆 = 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻.      Episodic, long-term, and semantic memory shape the agent’s decision-making to feel more human. 4. 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗲 𝗯𝗿𝗮𝗶𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻.      Using techniques like ReAct and CoT, the agent creates a plan before touching any tools. 5. 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗻𝘆𝗺𝗼𝗿𝗲.      Search, APIs, bots, file systems—agent capabilities are tightly coupled with external execution layers. 6. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗰𝗹𝗼𝘀𝗲𝘀 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽.      Real-time signals and user feedback aren't just for metrics—they 𝘶𝘱𝘥𝘢𝘵𝘦 𝘮𝘦𝘮𝘰𝘳𝘺 𝘢𝘯𝘥 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘦 𝘣𝘦𝘩𝘢𝘷𝘪𝘰𝘳. This architecture isn’t theoretical. It’s what powers real-world agentic systems today—using frameworks like: LangGraph   CrewAI   AutoGen   AgentOps   Custom LLM orchestration stacks The future of AI isn’t just bigger models—it’s better agents. Agents that 𝗼𝗯𝘀𝗲𝗿𝘃𝗲, 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗮𝗰𝘁, 𝗮𝗻𝗱 𝗹𝗲𝗮𝗿𝗻. I'd love to hear—how are you using agents in your work?

Treating AI like a chatbot, AKA you ask a question → it gives an answer is only scraching the surface. Underneath, modern AI agents are running continuous feedback loops - constantly perceiving, reasoning, acting, and learning to get smarter with every cycle. Here’s a simple way to visualize what’s really happening 👇 1. Perception Loop – The agent collects data from its environment, filters noise, and builds real-time situational awareness. 2. Reasoning Loop – It processes context, forms logical hypotheses, and decides what needs to be done. 3. Action Loop – It executes those plans using tools, APIs, or other agents, then validates outcomes. 4. Reflection Loop – After every action, it reviews what worked (and what didn’t) to improve future reasoning. 5. Learning Loop – This is where it gets powerful, the model retrains itself based on new knowledge, feedback, and data patterns. 6. Feedback Loop – It uses human and system feedback to refine outputs and improve alignment with goals. 7. Memory Loop – Stores and retrieves both short-term and long-term context to maintain continuity. 8. Collaboration Loop – Multiple agents coordinate, negotiate, and execute tasks together, almost like a digital team. These loops are what make AI agents more human-like while reasoning and self-improveming. Leveraging these loops moves AI systems from “prompt and reply” to “observe, reason, act, reflect, and learn.” #AIAgents

User Feedback Loops: the missing piece in AI success? AI is only as good as the data it learns from -- but what happens after deployment? Many businesses focus on building AI products but miss a critical step: ensuring their outputs continue to improve with real-world use. Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly. Instead of treating AI as a one-and-done solution, companies need workflows that continuously refine and adapt based on actual usage. That means capturing how users interact with AI outputs, where it succeeds, and where it fails. At Human Managed, we’ve embedded real-time feedback loops into our products, allowing customers to rate and review AI-generated intelligence. Users can flag insights as: 🔘Irrelevant 🔘Inaccurate 🔘Not Useful 🔘Others Every input is fed back into our system to fine-tune recommendations, improve accuracy, and enhance relevance over time. This is more than a quality check -- it’s a competitive advantage. - for CEOs & Product Leaders: AI-powered services that evolve with user behavior create stickier, high-retention experiences. - for Data Leaders: Dynamic feedback loops ensure AI systems stay aligned with shifting business realities. - for Cybersecurity & Compliance Teams: User validation enhances AI-driven threat detection, reducing false positives and improving response accuracy. An AI model that never learns from its users is already outdated. The best AI isn’t just trained -- it continuously evolves.

Aligning AI with human preferences typically requires collecting a lot of explicit feedback, which can be costly and not reflective of real-world usage. But there are many signals already embedded in our everyday interactions with AI. It turns out that the casual “thanks” or “wait a sec” moments in a chat can be just as valuable when training a model as formal ratings – if we know how to use them. 📖 WildFeedback: Aligning LLMs With In‑situ User Interactions and Feedback (https://lnkd.in/gxGyb-ig), by Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Freddie Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, and Jennifer Neville. NeurIPS 2024 Workshop. What’s novel in this paper is not just that it incorporates human feedback, but how it does so. The authors turn weak, messy signals from real conversations (implicit cues like “thanks,” “wait,” or “revise this”) into clean preference pairs at scale, and then show those signals can actually nudge the model in the right direction. This reframes alignment from a one‑off RLHF sprint into an ongoing, in‑situ dialog with users. The paper is exceptionally well grounded in real data (mining 20,281 preference pairs from 148,715 multi‑turn chats), and complements the usual benchmark tests with a checklist‑guided evaluation. A good template if you’re thinking about continuous AI alignment in everyday use. #BeyondTheAbstract #NeurIPS2024 #AIAlignment #OAR #AppliedResearch