AI Agent Features

Large Language Models (LLMs) are powerful, but their true potential is unlocked when we structure, augment, and orchestrate them effectively. Here’s a simple breakdown of how AI systems are evolving — from isolated predictors to intelligent, autonomous agents: 𝟭. 𝗟𝗟𝗠𝘀 (𝗣𝗿𝗼𝗺𝗽𝘁 → 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲) This is the foundational model interaction. You provide a prompt, and the model generates a response by predicting the next tokens. It’s useful but limited — no memory, no tools, no understanding of context beyond what you give it. 𝟮. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) A major advancement. Instead of relying solely on what the model was trained on, RAG enables the system to retrieve relevant, up-to-date context from external sources (like vector databases) and then generate grounded, accurate responses. This approach powers most modern AI search engines and intelligent chat interfaces. 𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗟𝗟𝗠𝘀 (𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 + 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲) This marks a shift toward autonomy. Agentic systems don’t just respond — they reason, plan, retrieve, use tools, and take actions based on goals. They can:  • Call APIs and external tools  • Access and manage memory  • Use reasoning chains and feedback loops  • Make decisions about what steps to take next These systems are the foundation for the next generation of AI applications: autonomous assistants, copilots, multi-step planners, and decision-makers.

Very useful tips on tool use and memory from Manus's context engineering blog post. Key takeaways: 1. Reversible compact summary Most models allow 128K context, which can easily fill up after a few turns when working with data like PDFs or web pages. When the context gets full, they have to compact it. It’s important to compact the context so that it’s reversible. Eg, removing the content of a file/web page if the path/URL is kept. 2. Tool use Given how easy it is to add new tools (e.g., with MCP servers), the number of tools a user adds to an agent can explode. Too many tools make it easier for the agent to choose the wrong action, making them dumber. They caution against removing tools mid-iteration. Instead, you can force an agent to choose certain tools with response prefilling. Ex: starting your response with <|im_start|>assistant<tool_call>{"name": “browser_ forces the agent to choose a browser. Name your tools so that related tools have the same prefix. Eg: browser tools should start with `browser_`, and command line tools should start with `shell_` 3. Dynamic few shot prompting They cautioned against using the traditional few shot prompting for agents. Seeing the same few examples again and again will cause the agent to overfit to these examples. Ex: if you ask the agent to process a batch of 20 resumes, and one example in the prompt visits the job description, the agent might visit the same job description 20 times for these 20 resumes. Their solution is to introduce small structured variations each time an example is used: different phrasing, minor noise in formatting, etc Link: https://lnkd.in/gHnWvvcZ #AIAgents #AIEngineering #AIApplications

New! If you want to skate to where the puck is going in AI, there are few safer bets than autonomous agents (easier to build than ever). Let's take a look... Technical capability tends to follow an 'S'-curve over time and while it may feel like we are in the high-gradient part of that curve today, I don't think we have hit the hockey-stick inflection point yet. We need to improve in multiple dimensions to get there, but one of the most promising components which are maturing quickly, is autonomous agents (aka, 'agentic systems'). Conceptually, an agent understands complex goals, plans how to achieve them, and completes tasks independently while staying true to the user's original intention. Getting these systems right opens up meaningful new paths to productivity, automation, time-savings, and product capabilities. It's lightening in a bottle. Building and operating agents has been right on the cusp of what's possible with generative AI technology, but there have been meaningful advances in the past few months which makes agents more accessible and useful today, than ever before (including some of the new capabilities we made available this week in Bedrock). ⚡️ Goal understanding: Bedrock includes a pre-flight evaluation of the user's intent, maps the intent to the data and tools available to the agent (through RAG or APIs), filters out malicious use, and makes a judicious call on the liklihood of creating and executing a successful plan. 💫 Planning: Alignment to strategic planning is improving in new models all the time, and Claude 3 Sonnet and Haiku are especially good (based on benchmarks and our own experience). The plans usually have more discrete steps, and a longer reliable event horizon than from even six months ago. Bedrock agents can now be built with Claude 3. ✨ Execution: Bedrock agents independently execute planned tasks, integrating information from knowledge sources, and using tools through APIs and Lambda functions. We made this significantly easier in Bedrock this week, with automated Lambda functions and extensive OpenAPI integration, to bring more advanced tools to agents, more quickly. 🔭 Monitoring and adaptation: Bedrock makes testing incredibly easy - there is nothing to deploy and no code to write to test an agent - it's all right there in the console, along with explanations, pre- and post-processing task monitoring, and step by step traces for every autonomous step or adaption of the agent's plan. With these new changes, and at the rate of improvement of these capabilities, it is a capability whose time has come. In some cases - without a crystal ball - it can be hard to know where to place bets for generative AI. While we still have a long way to go (on accuracy, capability, and ethical alignment), the odds that agents will play an increasingly central role in AI going forwards are good (and continue to improve). Fire them up in Bedrock today. 🤘 #genai #ai #aws

For the AI-curious innovator, here’s a visual guide that breaks down the 15 essential skills needed to get started with Agentic AI. Caveat: no need to become an expert in all of this to get started! 🔧 What’s inside: 1.🔸Python Programming – Master the fundamentals: syntax, APIs, data structures. 2.🔸Prompt Engineering – Craft system prompts, roles, and structured inputs. 3.🔸LLMs – Know your models: GPT, Claude, Gemini, HuggingFace. 4.🔸APIs & Webhooks – Connect services using Postman, FastAPI, Flask. 5.🔸Automation Tools – Orchestrate workflows with Zapier, Make, n8n. 6.🔸JSON & Schema Design – Enable tool/agent communication via structured data. 7.🔸Vector Databases – Store and retrieve embeddings using Pinecone, Chroma, Weaviate. 8.🔸DevOps & Deployment – Run agents locally or on Docker, Modal, Replit. 9.🔸RAG (Retrieval-Augmented Generation) – Integrate external knowledge with LangChain, FAISS, LlamaIndex. 10.🔸Agent Frameworks – Build and manage agents using CrewAI, LangChain, AutoGen. 11.🔸Tool Integration – Equip agents with calculators, databases, or APIs. 12.🔸Multi-Agent Systems – Coordinate memory and task routing with MetaGPT, CrewAI. 13.🔸Memory Management – Build short-term and long-term memory via Redis, Supabase. 14.🔸Logging & Monitoring – Track agent actions and errors with LangSmith, OpenTelemetry. 15.🔸Security & Guardrails – Keep agents safe using filters, moderation, and content policies. 🔍 Hope this playbook helps get started! 👉 Save this post. Share it with your team. And follow me for more AI breakdowns like this. #AgenticAI #AIAgents #ArtificialIntelligencew

If you’re an AI engineer, here are the 15 components of agentic AI you should know. Building truly agentic systems goes far beyond chaining prompts or wiring tools. It requires modular intelligence that can perceive, plan, act, learn, and adapt across dynamic environments - autonomously and reliably. This framework breaks it down into 15 technical components: 🔴 1. Goal Formulation → Agents must define explicit objectives, decompose them into subgoals, prioritize execution, and adapt dynamically as new context arises. 🟣 2. Perception → Real-time sensing across modalities (text, visual, audio, sensors) with uncertainty estimation and context grounding. 🟠 3. Cognition & Reasoning → From world modeling to causal inference, agents need inductive, abductive reasoning, planning, and introspection via structured knowledge (graphs, ontologies). 🔴 4. Action Selection & Execution → This includes policy learning, planning, trial-and-error correction, and UI/tool interfacing to interact with real systems. 🟣 5. Autonomy & Self-Governance → Independence from human-in-the-loop oversight through constraint-aware, initiative-taking decision frameworks. 🟠 6. Learning & Adaptation → Support for continual learning, transfer learning, and meta-learning with feedback-driven self-improvement loops. 🔴 7. Memory & State Management → Episodic memory, working memory buffers, and semantic grounding for contextually-aware actions over time. 🟣 8. Interaction & Communication → Natural language generation and understanding, negotiation, and multi-agent coordination with social signal processing. 🟠 9. Monitoring & Self-Evaluation → Agents should monitor their own performance, detect anomalies, benchmark against goals, and recover autonomously. 🔴 10. Ethical and Safety Control → Safety constraints, transparency, explainability, and alignment to human values - non-negotiable for real-world deployment. 🟣 11. Resource Management → Optimizing compute, memory, and energy with intelligent resource scheduling and infrastructure-aware orchestration. 🟠 12. Persistence & Continuity → Agents must preserve goal state across sessions, maintain behavioral consistency, and recover from disruptions. 🔴 13. Agency Integration Layer → Modular architecture, orchestration of internal components, and hierarchical control systems for scalable design. 🟣 14. Meta-Agent Capabilities → Delegation to sub-agents, participation in agent collectives, and orchestration of agent teams with diverse roles. 🟠 15. Interface & Environment Adaptability → Adaptation across domains and tools with robust APIs and reconfigurable sensing-actuation layers. 〰️〰️〰️ 🔁 Save and share this if you’re designing agents beyond the demo stage. 🔔 Follow me (Aishwarya Srinivasan) for more data & AI insights

What actually makes an AI “agentic”? It’s not just a chatbot with a fancy wrapper. It’s an autonomous system that can reason, plan, and act—on its own. But building one? That’s where it gets complex. I came across a brilliant breakdown of the 10 core components of Agentic AI, and it’s one of the most practical frameworks I’ve seen so far. If you’re building or evaluating AI agents, this is a must-read. Here’s a quick look at what’s covered: Experience Layer – where users interact (chat, voice, apps) Discovery Layer – retrieves context using RAG, vector DBs Memory Layer – stores episodic, semantic, long-term memory Reasoning & Planning – uses CoT, ToT, ReAct to make decisions Agent Composition – modular agents collaborating as teams Tool Layer – actually executes code, APIs, SQL, etc. Feedback Layer – learns from outcomes via evaluation loops Infrastructure – handles deployment, scaling, versioning Multi-Agent Coordination – planner-worker patterns in action Observability – monitors memory, tools, decisions in real-time The stack is evolving fast. And frameworks like LangGraph, CrewAI, AutoGen, and LangChain are leading the charge. Would love to hear: which layer do you think is the hardest to get right?

Came back from vacation Monday. Inbox? On fire.🔥 Buried in the chaos: a customer story that stopped me in my tracks (and made me so happy). A Customer Support leader at a fast-growing financial services company used AI to transform his team - in just a few weeks. This leader works for a financial services company that’s in high-growth mode. Great news, right? Yes! For everyone except his Customer Support team… As the business grew faster, they were bombarded with repetitive questions about simple things like loan statuses and document requirements. Reps were overwhelmed. Customers faced longer response times. The company has been a HubSpot customer for nearly 10 years. They turned to Customer Agent, HubSpot’s AI Agent, and got to work: - Connected it to their knowledge base → accurate, fast answers - Set smart handoff rules → AI handles the simple, reps handle the complex - Customized the tone → sounds like them, not a generic bot (you know the type) In a short space of time, things changed dramatically: - Customer Agent now resolves more tickets than any rep - 94.9% of customers report being happy with the experience - For the first time, the team can prioritize complex issues and provide proactive support to high-value customers It’s exciting to see leaders using Customer Agent to not just respond to more tickets, but to increase CSAT and empower their teams to drive more impact. 2025 is the year of AI transformed Customer Support. I am stunned by how quickly that transformation is playing out!

🤖 AI Agents aren’t just assistants: they’re your new Chief of Staff superpower. We’re entering a new era where AI doesn’t just support your work... ...it does the work. 𝗧𝗵𝗶𝗻𝗸: complex project breakdowns, stakeholder-ready briefings, and real-time decision-making… all without human burnout. In this week’s Ask a Chief of Staff newsletter, guest author Lawrence Coburn, CEO at Ambient, explores how AI Agents are redefining what’s possible for strategic operators like us. 𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐬𝐧𝐞𝐚𝐤 𝐩𝐞𝐞𝐤: 🔍 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝘃𝗲 – Research markets, evaluate criteria, and draft implementation plans 📊 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 – Chunking large projects into manageable tasks 🤝 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗼𝗿 – Synthesize information and create comms that align cross-functional teams 🧠 𝗬𝗼𝘂𝗿 𝗧𝗶𝗿𝗲𝗹𝗲𝘀𝘀 𝗧𝗲𝗮𝗺𝗺𝗮𝘁𝗲 – Works 24/7, never needs coffee, and follows your strategic guidance exactly 💡 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿: AI Agents aren’t about replacing humans. They’re about amplifying human potential. 👉 Read the full article and subscribe for cutting-edge insights on the future of strategic leadership ⬇️

With 4,000 stars on GitHub, this YC-backed startup is making waves with an open-source framework that automates operational workflows with LLM-powered agents. Superagent empowers developers to enhance their applications with robust LLM-powered AI assistants. Imagine a customer support workflow. With Superagent, an agent, could access various data sources like FAQs, product manuals, and customer data in databases to provide accurate and contextually relevant responses. The memory feature ensures the conversation context is maintained, enhancing customer experience. For inquiries requiring more sophisticated handling, the workflow feature can route the conversation to human agents (using the "hand-off" feature) or escalate it through a sequence of increasingly sophisticated AI agents. This system can significantly reduce response times, improve customer satisfaction, and decrease operational costs. Highlights: (1) Ingest various data sources, including PDFs, CSVs, Airtable, and YouTube videos (2) Execute different actions, from searching on Bing, to generating speech from text, calling a custom function, or hitting a Zappier endpoint (3) Features different generative models such as OpenAI’s GPT, Mixtral, or Stable Diffusion (4) Integrates with known vector bases such as Pinecone, Weaviate, and Supabase (5) Supports Langfuse and LangSmith for LLM observability (cost, latency, etc.) It is fully open-source and has Python + Node/Typescript SDKs. Superagent GitHub repo https://lnkd.in/gKrMq-sQ I recently wrote about the rise of autonomous agents and how packages like Superagent facilitate such a change https://lnkd.in/gNsKaeA4

While 2023 was the year of the transformer, I think 2024 is going to be the year of the autonomous AI agent. What is an agent? If an LLM-powered chatbot is an intern that answers questions directly, an agent is a more experienced and proactive employee that takes initiative, seeks out tasks, learns from interactions, and makes decisions aimed at achieving specific objectives. While chatbots are passive assistants, agents work autonomously towards the goals set by their “employer.” Like what? This week, Cognition AI unveiled Devin, an autonomous bot that can write software from scratch based on simple prompts. In the demo, Devin demonstrated exceptional capabilities by planning and executing intricate coding tasks, learning and debugging in real time, and even completing freelance jobs on Upwork. It notably outperformed the previous state-of-the-art agents by solving a significant percentage of real-world coding issues. So what? As agents like Devin become increasingly capable, they have the potential to democratize software development and make it more accessible to those without extensive coding expertise. By leveraging natural language prompts and advanced AI capabilities, these agents can help users translate their ideas into functional code, streamlining the development process. For example, imagine using a tool like Devin to quickly create customized financial analysis tools based solely on your text prompts. With only a simple set of natural language instructions, the agent would plan, gather data, write code, test that code, and create an application to automate the analysis process. This would allow the analyst to focus on higher-level strategic analysis and decision-making, while Devin handles the more time-consuming and tedious aspects of financial modeling. The analyst would still need to review and validate the outputs, but Devin could significantly streamline the process and improve efficiency. https://lnkd.in/dfQ3PC6R