Understanding Modern AI Agent Protocols

🤖 What does it really take to build an intelligent agent? Most people stop at the LLM. But real agents, ones that can think, reason, act, and learn, require much more than clever prompts. This framework breaks it down into 7 essential layers that power autonomous systems: 🔹 Experience Layer – The human interface: where users interact with the agent 🔍 Discovery Layer – How the agent gathers relevant information and context 🧠 Agent Composition Layer – Defines structure, goals, and behaviors 🗺️ Reasoning & Planning Layer – The agent’s "brain" for logic and decision-making 🛠️ Tool & API Layer – How agents act: calling APIs, running workflows, executing code 🧠💾 Memory & Feedback Layer – Enables learning, feedback, and contextual recall 🏗️ Infrastructure Layer – Scales everything: compute, orchestration, and security 💡 If you're serious about building real-world AI agents, you need more than an LLM—you need a system. A must-know mental model for founders, developers, and product leaders shaping the future of AI. #AIagents #LLM #AgentArchitecture #AutonomousAI #AgentFramework #AIproduct #AIengineering #MCP #RAG #ReasoningAI #AIinfrastructure #LLMOps #FutureOfAI #AIstrategy

🛠️Your Organization Isn't Designed to Work with GenAI. ❎Many companies are struggling to get the most out of generative AI (GenAI) because they're using the wrong approach. 🤝They treat it like a standard automation tool instead of a collaborative partner that can learn and improve alongside humans. 📢This Harvard Business Review article highlights a new framework called "Design for Dialogue" ️ to help organizations unlock the full potential of GenAI. Here are the key takeaways: 🪷Traditional methods for process redesign don't work with GenAI because it's dynamic and interactive, unlike previous technologies. ✍Design for Dialogue emphasizes collaboration between humans and AI, with each taking the lead at different points based on expertise and context. This approach involves  📋Task analysis ensures that each task is assigned to the right leader — AI or human 🧑💻Interaction protocols that outline how AI and humans communicate and collaborate rather than establish a fixed process 🔁Feedback loops to continuously assess and fine-tune AI–human collaboration based on feedback. 5-step guide to implement Design for Dialogue in your organization 🔍Identify high-value processes. Begin with a thorough assessment of existing workflows, identifying areas where AI could have the most significant impact. Processes that involve a high degree of work with words, images, numbers, and sounds — what we call WINS work are ripe for providing humans with GenAI leverage. 🎢Perform task analysis. Understand the sequence of actions, decisions, and interactions that define a business process. For each identified task, develop a profile that outlines the decision points, required expertise, potential risks, and contextual factors that will influence the AI’s or humans’ ability to lead. 🎨Design protocols. Define how AI systems should engage with human operators and vice versa, including establishing clear guidelines for how and when AI should seek human input and vice versa. Develop feedback mechanisms, both automated and human led. 🏋🏼♂️Train teams. Conduct comprehensive training sessions to familiarize employees with the new AI tools and protocols. Focus on building comfort and trust in AI’s capabilities and teach how to provide constructive feedback to and collaborate with AI systems. ⚖Evaluate and Scale. Roll out the AI integration with continuous monitoring to capture performance data and user feedback and refine the process. Continuously update the task profiles and interaction protocols to improve collaboration between AI and human employees while also looking for process steps that can be completely automated based on the interaction data captured.  By embracing Design for Dialogue, organizations can: 🚀Boost innovation and efficiency, 📈Improve employee satisfaction 💪Gain a competitive advantage 🗣️What are your thoughts on the future of AI and human collaboration? Please share your insights in the comments! #GenAI #AI #FutureOfWork #Collaboration

2024 - Start of Agenticness: 𝘼𝙜𝙚𝙣𝙩𝙞𝙘 𝘼𝙄 𝙨𝙮𝙨𝙩𝙚𝙢𝙨 are characterized by the ability to take actions which consistently contribute towards achieving goals over an extended period of time, without their behavior having been specified in advance. In the cultural imagination, an AI agent is a helper that accomplishes arbitrary tasks for its user. #Microsoft #research - https://lnkd.in/gbiBxRcF Practices of Governing Agentic AI Systems from #openai - https://lnkd.in/gcHajFiG Next year we are most likely to see the proliferation of interaction agents, embodied agents and multi-modal agents, large and small LLMs and symbolic agents. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: AI systems that can adaptably pursue complex goals in complex environments with 𝐥𝐢𝐦𝐢𝐭𝐞𝐝 𝐝𝐢𝐫𝐞𝐜𝐭 𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐢𝐨𝐧. The 𝘥𝘦𝘨𝘳𝘦𝘦 𝘰𝘧 𝘢𝘨𝘦𝘯𝘵𝘪𝘤𝘯𝘦𝘴𝘴 depends on factors such as goal complexity, environmental complexity, adaptability, and independent execution. The paper points out no clear line draw a binary distinction between "agents" and current AI Systems. The Human Parties in the AI 𝗔𝗴𝗲𝗻𝘁 𝗟𝗶𝗳𝗲-𝗰𝘆𝗰𝗹𝗲: The "model developer", the "system deployer", and the "user". Each party has different roles and responsibilities in creating, operating, and interacting with agentic AI systems. Potential Benefits of Agentic AI Systems: Agentic AI systems could 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 𝐭𝐡𝐞 𝐪𝐮𝐚𝐥𝐢𝐭𝐲, 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲, 𝐬𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲, 𝐚𝐧𝐝 𝐩𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐬𝐨𝐥𝐢𝐜𝐢𝐭𝐚𝐭𝐢𝐨𝐧 of AI outputs, as well as 𝘦𝘯𝘢𝘣𝘭𝘦 𝘸𝘪𝘥𝘦𝘳 𝘥𝘪𝘧𝘧𝘶𝘴𝘪𝘰𝘯 𝘰𝘧 𝘈𝘐 in beneficial applications and domains. Agenticness as an 𝐈𝐦𝐩𝐚𝐜𝐭 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐢𝐞𝐫 for any given field are also observed. Practices for Keeping Agentic 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗦𝗮𝗳𝗲 𝗮𝗻𝗱 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗹𝗲: The paper suggests seven practices that could help mitigate the risks of harm from agentic AI systems, such as evaluating suitability for the task, constraining the action-space and requiring approval, setting agents’ default behaviors, legibility of agent activity, automatic monitoring, attributability, and interruptibility and maintaining control. Also highlights the open questions, uncertainities and challenges around operationalizing these practices. Some examples of an Agentic AI System are AutoGPT, AutoGen, BabyAGI, AppAgent and more. Automatic Monitoring: An AI monitoring system that automatically reviews the primary agentic system's reasoning and actions to check if they are in line with the expectations for the given user's goals. OpenAI has called out for programs to launch research grants on above: https://lnkd.in/gM-kkViK

𝐖𝐚𝐧𝐭 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐭𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐰𝐨𝐫𝐤 𝐢𝐧 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐰𝐨𝐫𝐥𝐝? Here is a proven 7-part strategy to move from random prompts to fully functional autonomous agents 𝐅𝐨𝐥𝐥𝐨𝐰 𝐭𝐡𝐞𝐬𝐞 𝐬𝐭𝐞𝐩𝐬 𝐭𝐨 𝐝𝐞𝐬𝐢𝐠𝐧 𝐬𝐦𝐚𝐫𝐭𝐞𝐫, 𝐠𝐨𝐚𝐥-𝐝𝐫𝐢𝐯𝐞𝐧 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝟏. 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 Start by identifying the real problem Map pain points, define user behavior, and clarify what value the agent should deliver 𝟐. 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞 𝐃𝐞𝐬𝐢𝐠𝐧 Design where and how the agent will be used Think beyond chatbots - automate workflows, perform research, summarize docs, or handle scheduling 𝟑. 𝐒𝐤𝐢𝐥𝐥 𝐌𝐚𝐩𝐩𝐢𝐧𝐠 Define what the agent should be able to do From reasoning and planning to making decisions, generating outputs, and working with APIs 𝟒. 𝐓𝐨𝐨𝐥 𝐚𝐧𝐝 𝐌𝐨𝐝𝐞𝐥 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 Choose the right LLM and supporting tools Use orchestration frameworks, select tools (APIs, DBs), and decide how the agent will think (RAG, embeddings, rule-based) 𝟓. 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐚𝐧𝐝 𝐌𝐞𝐦𝐨𝐫𝐲 Let your agent stay intelligent over time Simulate real-world tasks, handle errors, recall context, and optimize latency and cost 𝟔. 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 Continuously improve your agent Collect feedback, run A/B tests, monitor performance, and integrate reward-based learning 𝟕. 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐂𝐡𝐚𝐧𝐧𝐞𝐥𝐬 Launch where it adds the most value Whether it is in Slack, CRMs, mobile apps, dashboards, or voice assistants - deploy where users already in work Smart agents are not built in one go, they are designed with systems integrated thinking Save this strategy as your go-to roadmap for AI agent development ♻️ Repost this to help your network get started ➕ Follow Prem N. for more