Understanding the Role of AI Agents

As we move from LLM-powered chatbots to truly 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀, 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, understanding 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 becomes non-negotiable. Agentic AI isn’t just about plugging an LLM into a prompt—it’s about designing systems that can 𝗽𝗲𝗿𝗰𝗲𝗶𝘃𝗲, 𝗽𝗹𝗮𝗻, 𝗮𝗰𝘁, 𝗮𝗻𝗱 𝗹𝗲𝗮𝗿𝗻 in dynamic environments. Here’s where most teams struggle:  They underestimate the 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 required to support agent behavior. To build effective AI agents, you need to think across four critical dimensions: 1. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 & 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 – Agents should break down goals into executable steps and act without constant human input. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 – Agents need long-term and episodic memory. Vector databases, context windows, and frameworks like Redis/Postgres are foundational. 3. 𝗧𝗼𝗼𝗹 𝗨𝘀𝗮𝗴𝗲 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 – Real-world agents must invoke APIs, search tools, code execution engines, and more to complete complex tasks. 4. 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 & 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 – Single-agent systems are powerful, but multi-agent orchestration (planner-executor models, role-based agents) is where scalability emerges. The ecosystem is evolving fast—with frameworks like 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, 𝗔𝘂𝘁𝗼𝗚𝗲𝗻, 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, and 𝗖𝗿𝗲𝘄𝗔𝗜 making it easier to move from prototypes to production. But tools are only part of the story. If you don’t understand concepts like 𝘁𝗮𝘀𝗸 𝗱𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻, 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹𝗻𝗲𝘀𝘀, 𝗿𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻, and 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀, your agents will remain shallow, brittle, and unscalable. The future belongs to those who can 𝗰𝗼𝗺𝗯𝗶𝗻𝗲 𝗟𝗟𝗠 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝘄𝗶𝘁𝗵 𝗿𝗼𝗯𝘂𝘀𝘁 𝘀𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻. That’s where real innovation happens. 2025 will be the year we go from prompting to architecting.

"What is an AI Agent and why do they matter?" AI is rapidly evolving, and one of the most exciting developments is the emergence of AI agents. What is an agent? An agent is a program that autonomously completes tasks or makes decisions based on data. What do I mean by autonomous? The agent understands task intent, can plan steps to solve the problem, decide and execute and actions and adapt to the environment. Consider how many of us use AI chat interfaces today. You might ask ChatGPT to write an article from start to finish and get a one-shot response. You probably need to do some work to iterate on it yourself. An agentic version is more nuanced - it might write an outline, decide if research is needed, write a draft, evaluate if it needs work and revise itself. Unlike traditional AI models that simply respond to queries, agents are designed to be autonomous and proactive. Think of them as assistants that can not only understand what you need but also take initiative to accomplish tasks by using various tools and making decisions along the way. For example, an AI agent might help a marketing team by not just analyzing campaign data, but actively monitoring performance, adjusting budget allocations, and even drafting social media posts based on real-time engagement metrics. The significance of AI agents lies in their potential to transform how we work. In customer service, agents can handle complex inquiries by accessing multiple databases, processing payments, and updating records - all while maintaining natural conversations with customers. In software development, they can assist programmers by not just suggesting code but actively debugging issues, writing test cases, and even refactoring entire codebases. This level of autonomy and capability represents a fundamental shift from AI as a tool to AI as a collaborative partner. While there remain many unknowns, I'm excited about the potential for agents and we're thinking about how they can help users and developers on the web over in Chrome. The key to success will likely be finding the right balance between human oversight and agent autonomy, ensuring that these powerful tools enhance rather than diminish the human element in business operations. I hope this post was helpful if you've been a little unsure what agents are about. You can check out my full talk "The AI Assisted Developer Workflow" for the rest of this talk: https://lnkd.in/gx7MY2xR I also recommend reading the excellent @Anthropic article "Building effective agents": https://lnkd.in/g9J3MX8V which covers definitions and examples in more detail. #softwareengineering #programming #ai

Lately, the term AI Agent has been popping up everywhere—but what actually makes an AI agent different from a regular chatbot or model? I came across this helpful guide that breaks it down beautifully. Here’s a simple summary in plain language: Core Principles Behind AI Agents: - Autonomy: They can act without constant human instructions. - Planning: They break big goals into small steps. - Reflection: They learn from past actions to improve. - Statefulness: They remember past conversations or tasks. - Prompting: They react to input or questions to decide what to do next. Key Capabilities That Make Agents Smart: - Task Decomposition: Breaking complex tasks into manageable pieces. - Memory Retrieval: Pulling information from memory to stay relevant. - Tool Use: Calling APIs, web browsers, or databases to get things done. - Observability: Tracking decisions and actions for transparency. Memory Types in Agents: - Short-Term Memory: Keeps track of recent conversations. - Long-Term Memory: Stores knowledge across different sessions. - Semantic Memory: Holds facts and meanings. - Procedural Memory: Remembers how to perform tasks. - Episodic Memory: Remembers past experiences or events. Different Agent Roles: - Researcher: Finds information from the web or data sources. - Planner: Breaks tasks into steps. - Executor/Coder: Performs the steps, like coding or summarizing text. Design Approaches: - Tool-Centric Agents: Rely heavily on external tools. - Model-Centric Agents: Depend more on language understanding and internal reasoning. - Many modern systems combine both for balance. How Agents Learn: They improve through feedback loops and self-reflection, making them smarter over time without constant human correction. The Agent Loop (ReAct Framework): Perceive → Plan → Act → Learn – a continuous cycle that makes agents adaptive and autonomous. There’s also a growing ecosystem of frameworks like LangChain, AutoGen, CrewAI, and others helping developers build smarter agents faster. AI agents are more than chatbots—they’re evolving toward systems that can think, plan, and act, much like human collaborators. Which of these agent concepts are you exploring in your work?

𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀: 𝗧𝗵𝗲 𝗕𝗿𝗮𝗶𝗻𝘀 𝗕𝗲𝗵𝗶𝗻𝗱 𝗠𝗼𝗱𝗲𝗿𝗻 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 AI agents are no longer just a concept — they’re the driving force behind autonomous systems, from personal assistants to industrial automation. But what exactly makes an AI agent intelligent, and how do these systems work? Here’s a high-level breakdown to help you or your team grasp the essentials: 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁?  An AI Agent is an autonomous system that perceives its environment, processes data, and takes actions to achieve goals — often interacting with humans, applications, and other agents. 𝗛𝗼𝘄 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗪𝗼𝗿𝗸:  AI Agents function by accessing memory, processing tasks, and reacting to environments — using tools like:  • API Calls  • Code Interpretation  • Internet Access 𝗞𝗲𝘆 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀:  • Perception Module: Gathers data from external sources.  • Decision-Making Module: Uses AI/ML to decide the next action.  • Action Module: Executes commands via automation.  • Learning Module: Continuously improves through insights.  • Technologies Powering AI Agents:  • Large Language Models (LLMs): ChatGPT, Claude, Gemini  • Natural Language Processing (NLP): Text understanding  • Reinforcement Learning: Learning from feedback  • Generative AI: Content generation  • Multi-Modal AI: Handling text, images, audio, and video   𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀:  • Simple Reflex Agents  • Model-Based Reflex Agents  • Goal-Based Agents  • Utility-Based Agents  • Learning Agents 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲:  • Single Agent: Acts independently.  • Multi-Agent: Collaborates with other agents.  • Human-Machine: Interacts with humans to provide assistance. As AI systems become more integrated into business operations, understanding how these agents perceive, decide, and act is critical for innovation, optimization, and scalability. Save this breakdown. Share it with your teams. Use it in your next AI project discussion. Follow Dr. Rishi Kumar for similar insights!

𝗛𝗼𝘄 𝗗𝗼𝗲𝘀 𝗮𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗪𝗼𝗿𝗸?  Here's a breakdown of what happens behind the scenes—with examples: 𝗦𝘁𝗲𝗽 𝟭. 𝗨𝘀𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 It starts when you submit a query to AI Agent. 📌 Example: “Summarize this research paper.” 𝗦𝘁𝗲𝗽 𝟮. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 - Once a query is received, the AI agent breaks it into actionable tasks and performs execution. 𝗧𝗼 𝗱𝗼 𝘁𝗵𝗶𝘀, 𝗶𝘁 𝘂𝘀𝗲𝘀 𝘁𝗵𝗲 𝗯𝗲𝗹𝗼𝘄 𝗸𝗲𝘆 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀: 𝗔. 𝗟𝗟𝗠 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹):  The agent’s brain.  𝙄𝙩 𝙝𝙖𝙣𝙙𝙡𝙚𝙨: 🔹 Prompt → The user’s query, transformed into a format the LLM can understand and act on. 🔹 Task → The specific action derived from the prompt—whether it's summarizing a document, writing code, answering a question, or generating ideas.     🔹 Role → The LLM adjusts its behavior based on context—acting as a tutor, assistant 𝙇𝙇𝙈 𝙥𝙚𝙧𝙛𝙤𝙧𝙢𝙨:    ↳ Reasoning → Determines the best logical path to approach a problem.    ↳ Planning → Strategizes how to fulfill complex prompts—like turning a paragraph into concise bullet points or structuring a blog post.    ↳ Reflection → Learns and adapts from prior inputs and user feedback to improve relevance, coherence, and accuracy over time.    📌 𝙀𝙭𝙖𝙢𝙥𝙡𝙚: An AI writing assistant that drafts an email, then revises it based on your edits and suggestions. 𝗕. 𝗠𝗲𝗺𝗼𝗿𝘆: 𝗦𝗵𝗼𝗿𝘁-𝗧𝗲𝗿𝗺 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗦𝗧𝗠): → Remembers recent interactions in a session. 📌 𝙀𝙭𝙖𝙢𝙥𝙡𝙚: In a chat, if you say “Tell me more about her,” the AI knows “her” refers to the person you mentioned earlier. 𝗟𝗼𝗻𝗴-𝗧𝗲𝗿𝗺 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗟𝗧𝗠): → Stores info across sessions to personalize future interactions. 📌 𝙀𝙭𝙖𝙢𝙥𝙡𝙚: A fitness AI that remembers your dietary restrictions and preferred workouts—even if you return after weeks. Platforms available for Memory: Supabase, Pinecone. 𝗖. 🛠️ 𝗧𝗼𝗼𝗹𝘀: AI agents can access tools to act beyond just conversation and perform tasks. 📌 𝙀𝙭𝙖𝙢𝙥𝙡𝙚𝙨: •    🌐 Web search — Fetches real-time stock prices or news •    📧 Gmail API — Schedules meetings or sends summaries to your inbox •    📊 Code interpreter tool — Analyzes a CSV you uploaded and gives visualizations 👥 𝗣𝗲𝗼𝗽𝗹𝗲 𝘁𝗼 𝗙𝗼𝗹𝗹𝗼𝘄: Nicholas Nouri Vishakha Sadhwani Andrew Ng Aravind Srinivas Ashish Vaswani 👩🎓𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗠𝗼𝗿𝗲:  N8N official Documentation - https://docs.n8n.io/ What are AI agents? (IBM) - https://lnkd.in/eGFygtvq  AI agents (Microsoft) - https://shorturl.at/rfwPo Understanding AI Agents - https://rb.gy/ma5fxk Please ♻️ 𝗥𝗲𝗽𝗼𝘀𝘁 or 𝘀𝗵𝗮𝗿𝗲 so that others can learn too For high-quality resources on AI and Immigration, join my newsletter here https://lnkd.in/eBGib_va #ai #ML #agenticai 

Have you heard of AI Agents? Do you know what they are? This post briefly explains, at a high level, what they are. An AI agent is a software system designed to perform specific tasks autonomously, deciding based on its programming and the data available to it. Unlike basic automation tools that follow rigid instructions, AI agents can adapt to new situations, learn from experience, and make contextual decisions within their designated domain. AI agents combine artificial intelligence capabilities (natural language processing, pattern recognition, predictive analytics) with automated execution features. They can track for events, analyze information, make decisions, and then take actions—all without requiring human intervention for each step. A hypothetical example would be a jurisdictional compliance agent: Imagine your firm represents a healthcare technology company expanding into five new countries. Your compliance team faces reviewing thousands of pages of regulations across multiple jurisdictions. Your AI compliance agent tackles this by: -Autonomously accessing the regulatory databases for each target country, extracting key provisions related to patient data protection, medical device certification, and clinical validation requirements. -Creating a structured comparison matrix that identifies conflicting requirements between jurisdictions (e.g., Germany requires on-soil data storage while Singapore allows cloud hosting with specific certifications) -Flagging provisions where the client's existing policies need changes, calculating implementation timelines based on regulatory deadlines -Maintaining an active tracking system that alerts attorneys when relevant regulations change in any jurisdiction Rather than associates manually searching for regulations and building comparison spreadsheets, they start with the agent's analysis and focus on developing compliance strategies that address the identified conflicts. Specific suggestions for implementing an AI Agent: 1) Bounded Problem Definition: They start with a defined process that consumes significant associate time (contract review, research memos, document categorization.) 2) Performance Verification: They run the AI agent alongside traditional methods, comparing results and measuring specific metrics (accuracy, speed, insight generation.) 3) Graduated Authorization: They begin by requiring attorney review of all agent outputs, then progressively reduce oversight as performance consistently meets standards. 4) Continuous Evaluation: They maintain regular quality checks, especially when applying the agent to new document types or legal domains. #legaltech #innovation #law #business #learning

I've noticed a lot of confusion lately about AI agents. Let's cut through the noise and focus on what they really are and why they matter: 🤖 What are AI agents? AI agents are intelligent software systems designed to perform tasks or solve problems autonomously. They're not just advanced chatbots, but sophisticated tools that can understand, decide, and act on complex information. 🧠 Key characteristics: - Utilize machine learning and natural language processing - Can handle a wide range of tasks, from simple to complex - Capable of multitasking and context-switching - Continuously improve through self-learning and adaptation 💡 How they function: 1. Perceive and collect data from various sources 2. Process and analyze information to make decisions 3. Execute actions based on their analysis 4. Learn from outcomes to enhance future performance 🌟 Benefits of AI agents: - Increased efficiency in handling repetitive tasks - 24/7 availability for customer support and other functions - Scalability to handle large volumes of interactions - Consistent performance and reduced human error - Freeing up human resources for more complex, creative tasks - Potential for personalized user experiences at scale As AI technology continues to evolve, understanding these systems becomes increasingly important. What potential impacts do you see AI agents having in your industry? #AITechnology #MachineLearning #AIAgents #BusinessInnovation #TechTrends

"Sorry, that's not an AI agent," I told the VP of Engineering. "You paid $80K for a workflow, it's pretty good though." Here's what most people miss about true AI agents: After a year into building a multi-agent platform and working with customers… I've learned that understanding the distinction isn't just academic - it's crucial for making the right technology choices. AI Workflows: 1: A program that calls an API (LLM) for one or more steps 2: Built on machine learning logic + fuzzy logic 3: Perfect when you need pattern recognition, structured decisions Example: Analyzing website content with defined criteria Single AI Agent: 1: Autonomous programs that handle non-deterministic tasks 2: Built on fuzzy logic + autonomy 3: Adapt to new variables, uncertainty for independent decisions Example: Conducting comprehensive market research with dynamic scope Multi- AI Agent Team: 1: Multiple specialized agents working in concert 2: Share context and build on each other's work 3: Handle complex, interconnected problems 4: Adapt collectively to new challenges 5: Enable sophisticated task decomposition Example: Research team where agents divide tasks, share findings, and synthesize insights together Here's the key insights: 1.  The future isn't just about individual agents, but about how they work together to solve complex problems. 2.  While AI agents are powerful, not every problem needs one. Sometimes an AI workflow is the perfect solution - especially for structured, predictable tasks. 3.  The real magic happens when you know exactly which tool fits your specific challenge. Curious to hear from others building in this space. Do you think most of your customers understand the difference, do they care? Thanks Alexandre Kantjas for starting this conversation. #AIAgents 

Earlier this year in article for The AI Journal I shared my thoughts on the emerging role of AI agents in corporate finance. Since then, the AI landscape has evolved significantly, reinforcing the views I expressed back in January and demonstrating even greater potential. At the time, I highlighted how AI agents were beginning to automate complex tasks, enhance decision-making, and boost overall efficiency. These agents are capable of independently planning and executing multi-step workflows, retaining context across interactions, and proactively achieving goals. Today, advancements in AI have accelerated further, notably: ▪️ OpenAI's Agents SDK: A toolkit simplifying the development of AI agents through streamlined frameworks for building and managing agent workflows. Features include agent orchestration, task handoffs, and built-in guardrails to ensure reliability. ▪️ Model Context Protocol (MCP): Introduced by Anthropic, MCP acts as an open standard for integrating AI models with external tools and data, effectively standardizing connectivity across platforms—essentially becoming the "USB-C for AI applications." ▪️ Google's Vertex AI Agent Builder: A comprehensive platform designed to facilitate the creation and deployment of AI agents. It offers tools like the Agent Development Kit (ADK) for building sophisticated multi-agent systems, and the Agent Garden for exploring sample agents and tools. The platform emphasizes seamless integration with enterprise data and tools, enabling the development of production-ready agents with minimal code. These developments highlight the growing maturity of AI agents and their increasingly critical role in finance, from predictive analytics and real-time anomaly detection to the automation of routine financial tasks. Yet, as we leverage these powerful tools, maintaining transparency, ensuring robust security, and actively managing ethical oversight remain essential. Clear audit trails, stringent data protection, and vigilance against biases are key to responsible AI integration. Revisiting my article now, it's promising to see how much progress has been made, reinforcing the original perspective and underscoring the exciting advancements still ahead. 🔗 Read the original article here: https://lnkd.in/eyDTi5VP #AI #Finance #Automation #Innovation #CorporateFinance #AIagents

With the intense hype cycle and hyperbole in full bloom, the term "Agentic AI" creates enormous confusion because we've had "agents" in computing for decades—those rule-following assistants that filter emails or recommend products based on predetermined logic. But today's AI agents represent something fundamentally different: rather than being mere tools that execute specific tasks within rigid boundaries, they function as thinking partners that understand goals rather than just commands, capable of planning, adapting, and even suggesting better approaches than what we originally requested. This isn't semantic nitpicking—it's a profound shift from automation to augmentation, from systems that follow our instructions to systems that actually think alongside us, extending human capabilities in ways traditional agents never could.