Source: Post by Rakesh Gohel on Linkedin AI Agent trends have drastically changed from 2024 to 2025 Here are the new trends driving innovation this year.... The Agentic AI field is constantly moving forward with new innovations and products. I'll share with you a few notable trends that are driving the market in 2025. Note: I've also included a few Langgraph codebooks if you want to build some of these solutions yourself. 📌 Agentic RAG - AI Agent workflow for reasoning-based real-time data retrieval and generation. - Agentic RAG is not limited to one use case but rather used in health care as well. - Used by companies like Perplexity, Harvey AI and Glean AI. 📌 Voice Agents - Intelligent agents that interact with users through natural spoken language utlizing a wide range of TTS and STTS embedding and Retrieval. - Used by companies like ElevenLabs, Cognigy, Vapi and Deepgram. 📌 AI Agent Protocols - Streamlining Multi-Agent Communication that supports communication between Agents built in different frameworks. - Used by Companies like Accenture, the top players include A2A, ACP, SLIM and others. 📌 CUA(Computer Using Agents) - AI agents that can interact with a computer the way a human does utilizing tools like a browser, CLI and even a mouse's cursor. - Used by Companies like OpenAI for their Operator, Claude's Computer Use, Runner H by H-Company and Manus AI. 📌 Coding Agents - Multi-Agents for building and debugging applications 10x faster with the help of clever tool use and LLM-based code gen. - Used by companies like Windsurf, Cursor and GitHub Copilot. 📌 Deepresearch Agents - Collaborative Multi-Agent system to build extensively researched reports from a large number of sources. - Popular products include Gemini DR, OpenAI DR and You(.)com DR. 🔗 Few Langgraph Codebooks to build some of the trending solutions 1. Agentic RAG: https://lnkd.in/gdqWyP3r 2. CUA: https://lnkd.in/gCdxpUBi 3. Deepresearch: https://lnkd.in/gexsW5qh 4. Code Gen Agents using RAG: https://lnkd.in/gDnsBFuM 📌 After months of feedback and iteration, we are finally releasing our first technical cohort, "AI Agent Engineering" 🔗 Enrol here: https://lnkd.in/gDEPcXBB If you are a business leader, we've developed frameworks that cut through the hype, including our five-level Agentic AI Progression Framework to evaluate any agent's capabilities in my latest book. 🔗 Book info: https://amzn.to/4irx6nI Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents https://www.linkedin.com/posts/rakeshgohel01_ai-agent-trends-have-drastically-changed-activity-7343981787842297858-PJkS #agenticrag #voiceagents #aiagentprotocols #cua #codingagents #deepresearchagents #langgraph #elevenlabs #cognigy #perplexity #harveyai #gleanai #openai #cursor #githubcopilot #geminidr #youcom #windsurf #a2a #acp #slim #tts #sts #rag #multiagent #aiagents #llm #browserautomation #aicoding

Abacus.AI has ChatLLM and AppLLM. ChatLLM is an AI assistant for various tasks like chatting, coding, and image generation, while AppLLM is a no-code platform that builds websites and apps from text prompts using a concept called "vibe coding". Essentially, ChatLLM is for conversational AI and general tasks, and AppLLM is for building applications with AI. Here's a breakdown: ChatLLM: Aims to be a comprehensive AI assistant, integrating various AI models for tasks like chatting, coding, and image generation. Features include voice mode, text-to-image generation, document summarization, and code debugging. Can be used for tasks like brainstorming, writing, research, and more. AppLLM: Focuses on building websites and applications from simple text prompts using "vibe coding". Allows users to create functional apps without coding or server setup. Integrates with tools like ChatLLM and CodeLLM for a streamlined AI development workflow. Can build various types of applications, including websites, chatbots, and more complex AI-powered applications. In short, ChatLLM is about interacting with and leveraging AI for diverse tasks, while AppLLM is about using AI to build applications, particularly websites, without traditional coding. https://appllm.abacus.ai/login #AbacusAI #ChatLLM #AppLLM #NoCode #AICoding #VibeCoding #WebsiteBuilder #AppBuilder #AIAssistant #Chatbot #TextToImage #OpenAI #Bubbleio #Adalo

How l switched back to Augment Code from Claude Code ! Tried new Claude Code ... verdict: Worth every penny love the little to-do lists but hell lot expensive ! So what do l do l head back home to the loving arms of Augment Code ... still the best and always will be ! (For now 🤫 ... you know relationships nowadays) So this article exaplins why they are nota big fan of Model Pickers ... makes sense though would be great to have the ooption still !). Here is a low done on Augment code and its recent updates: Augment Code is considered a top AI coding agent due to its advanced context engine, which enables personalized and efficient code generation, and its seamless integration with popular IDEs. It also boasts features like memory persistence, which allows it to adapt to individual coding styles, and Multi-Context Programming (MCP) for connecting to various tools and systems, enhancing its utility in the development workflow. Here's a more detailed breakdown of why Augment Code stands out: 1. Contextual Awareness and Memory: Context Engine: Augment's Context Engine is a key differentiator. It analyzes the entire codebase in real-time, providing context-aware suggestions and code completions, leading to more accurate and relevant AI-driven code generation. Memory Persistence: The platform remembers your coding style and project patterns, ensuring that its suggestions are tailored to your preferences and codebase over time, improving efficiency and reducing errors. MCP (Multi-Context Programming): Augment Code goes beyond just the code by integrating with various tools and systems, such as Vercel, Cloudflare, and more, allowing it to gather more information, automate tasks, and even fix issues in live systems. 2. IDE Integration and Workflow: Seamless Integration: Unlike some AI coding tools that require a separate editor, Augment Code integrates directly with popular IDEs like VS Code, JetBrains, Vim, and GitHub, allowing developers to leverage its AI capabilities within their familiar environment. Code Checkpoints: Augment Code automatically tracks changes and creates checkpoints, enabling easy rollback and providing peace of mind when the agent tackles complex tasks. Remote Agent Functionality: Augment Code can operate in a separate container, allowing developers to work on code even when their main machine is off or unavailable, and even run multiple agents in parallel. 3. Advanced Features and Capabilities: Multi-Modal Support: Augment Code can handle various inputs, including screenshots and Figma files, making it helpful for implementing UI elements and debugging visual issues. Terminal Interaction: Beyond code editing, Augment Code can run terminal commands, streamlining tasks like installing dependencies or running dev servers. Auto Mode: For a more streamlined experience, Augment Code offers an Auto Mode, where it automatically applies suggested changes without requiring explicit confirmation for each action. 4. Focus on Collaboration and Productivity: Developer-Centric: Augment Code is designed to work alongside developers, enhancing their existing workflow rather than replacing it, making it a collaborative rather than a replacement tool. Time Savings: By automating tasks, providing accurate suggestions, and handling repetitive coding tasks, Augment Code frees up developers' time to focus on more complex and creative aspects of their work. Continuous Improvement: Through feedback loops and learning from user interactions, Augment Code continuously improves its performance and adapts to the evolving needs of developers. https://www.augmentcode.com/blog/ai-model-pickers-are-a-design-failure-not-a-feature #augmentcode #claude #aicodingagent #aicode #vscode #jetbrains #vim #github #ideintegration #codingtools #productivitytools #memorypersistence #contextengine #multicontextprogramming #vercel #cloudflare #codecheckpoints #remotework #multimodal #figma #terminalinteraction #automode #developercentric #aitools #aidesign #coding #softwaredevelopment #aitool

Building complex, production-ready AI agents, especially multi-modal or multi-agent systems, can be challenging. Introducing the Agent Development Kit (ADK), a new open-source project from Google designed to simplify this process based on Google's internal experience. ADK provides a powerful, open foundation (model-agnostic, deployment-agnostic, interoperable) that makes agent development feel like software development, complete with native multi-modal streaming and easy local debugging via a built-in UI. Watch Product Manager Anand Iyer, Tech Lead Bo Yang, and Developer Advocate, Ivan Nardini introduce ADK and demo building a multi-agent, multi-modal travel planner quickly using the SDK. Learn about the core design principles and see how you can get started building your own sophisticated AI agents today. Resources: Get Started → https://google.github.io/adk-docs Sample Agents → https://goo.gle/3Rbdo4s Subscribe to Google for Developers → https://goo.gle/developers #ADK #AgentDevelopmentKit #GoogleAI #OpenSource #AIAgents #MultiModal #MultiAgentSystems #AIDevelopment #AIWorkflow #GenAI #LangChain #AutoGen #AIInfrastructure #SoftwareDevelopment #DebuggingUI #TravelPlanner #Python #AICommunity #ModelAgnostic #DeploymentAgnostic

Building complex, production-ready AI agents, especially multi-modal or multi-agent systems, can be challenging. Introducing the Agent Development Kit (ADK), a new open-source project from Google designed to simplify this process based on Google's internal experience. ADK provides a powerful, open foundation (model-agnostic, deployment-agnostic, interoperable) that makes agent development feel like software development, complete with native multi-modal streaming and easy local debugging via a built-in UI. Watch Product Manager Anand Iyer, Tech Lead Bo Yang, and Developer Advocate, Ivan Nardini introduce ADK and demo building a multi-agent, multi-modal travel planner quickly using the SDK. Learn about the core design principles and see how you can get started building your own sophisticated AI agents today. #ADK #AgentDevelopmentKit #GoogleAI #AIAgents #MultiModal #MultiAgentSystems #OpenSource #SoftwareDevelopment #AIDevelopment #AIWorkflow #AItools #AutoGen #LangChain #AIPlanning #Debugging #BuiltInUI #TravelPlanner #AgentFramework #ModelAgnostic #DeploymentAgnostic https://youtu.be/zgrOwow_uTQ

Boris from Anthropic introduces QuadCode, an AI coding assistant designed for building features and fixing bugs without changing existing workflows. It supports various IDEs and offers practical tips for setup and usage, emphasizing codebase Q&A for effective onboarding. Users can customize tools and context for enhanced productivity. #QuadCode #Anthropic #AICodingAssistant #CodeDebugging #FeatureDevelopment #IDEIntegration #CodebaseQA #AICodeCompletion #GitHubCopilot #CursorAI #CodeCustomization #ProductivityTools https://www.youtube.com/watch?v=6eBSHbLKuN0&ab_channel=Anthropic

Firebase Studio, a cloud-based development environment powered by Gemini, is now available in preview. This tool provides agentic assistance for coding, debugging, testing, and refactoring. Gemini in Firebase offers workspace-aware AI capabilities, including code completion, generation, tool running, and documentation assistance, enabling developers to work more efficiently within the Firebase ecosystem. It allows users to build with Gemini seamlessly. https://cloud.google.com/blog/products/application-development/firebase-studio-lets-you-build-full-stack-ai-apps-with-gemini #TechInnovation #FirebaseStudio #AIpowered #GeminiAI #CodingMadeEasy #DeveloperLife #CodingTools #AIforDevelopers #FirebaseEcosystem #CloudDevelopment #DebuggingSimplified #CodeSmarter #TechTrends2023 #FutureOfCoding #BuildWithGemini

Trae AI offers a configurable agent system with a built-in agent called "Builder" designed for task completion. It also features an AI programming assistant that provides code completion, explanations, and debugging functionalities to aid in faster development. Additionally, Trae provides a Pro Plan with enhanced agentic workflow and faster processing speeds. This suggests a focus on improving developer productivity through AI-powered tools and efficient workflows. https://www.trae.ai/

TestSprite facilitates test case generation with a clear structure, resulting in easily readable code. It also allows for online debugging and efficient expansion capabilities. This suggests that TestSprite is designed to streamline the testing process, making it more accessible and efficient for developers. https://www.testsprite.com/ TestSprite #SoftwareTesting #DebuggingMadeEasy #EfficientCoding #DeveloperTools #TechInnovation #ProductivityBoost #CodeSimplified #TestingSolutions #TechLife #ProgrammingMadeEasy #CodeSmart #OnlineDebugging #SoftwareDevelopment #StreamlineTesting