From GenAI to Agentic AI: The Evolution of Context Engineering

Deep Agents – The Next Evolution in AI Agents Most AI agents today handle short loops — take an input, call a tool, return an output. But when tasks require multi-step reasoning, evolving context, and memory, that model falls short. That’s why Deep Agents are emerging as a new standard. 🛠️ Technical Characteristics of Deep Agents: • Planning engine → decomposes tasks into subtasks, re-plans dynamically as progress is made. • Context manager → retains structured memory across turns, enabling continuity in long chains of reasoning. • Sub-agent orchestration → spawns specialized agents (e.g., for analysis, coding, search) and coordinates results. • File system integration → persistent read/write access for externalized memory and reproducibility. • Workflow-driven system prompts → consistent execution via detailed instruction scaffolding and examples. 📌 Why this matters: Deep Agents unlock sustained reasoning and scalable workflows that go beyond single-tool calls. This is more than just “agents calling tools.” It’s about sustained reasoning, memory, and collaboration—a foundation for building truly autonomous AI systems. 👉 The future of agentic AI is here—and it’s Deep. Image source:Langchain #AI #DeepAgents #LangChain #AgenticAI #AutonomousAI #LLMEngineering

🚀 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐯𝐬. 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬: 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐂𝐨𝐫𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 🤖 While both are at the forefront of AI innovation, Generative AI and AI Agents serve distinct purposes. This quick comparison highlights what sets them apart and how they're shaping the future of technology. 👉 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 ► 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧: Creates new content (text, images, audio) based on input. ► 𝐏𝐫𝐢𝐦𝐚𝐫𝐲 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: Content creation, code generation, summarization. ► 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐲: Low; responds to queries but doesn't take independent actions. ► 𝐋𝐢𝐦𝐢𝐭𝐚𝐭𝐢𝐨𝐧𝐬: Produce incorrect/biased content, lacks real-world awareness. 👉 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 ► 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧: An AI system that makes dynamic decisions and acts on its own. ► 𝐏𝐫𝐢𝐦𝐚𝐫𝐲 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: Task automation, workflow execution, decision-making. ► 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐲: High; performs tasks independently, interacts with systems. ► 𝐋𝐢𝐦𝐢𝐭𝐚𝐭𝐢𝐨𝐧𝐬: Requires predefined goals/frameworks, complex to deploy. ♦️ 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 Generative AI focuses on creating new content, while AI Agents execute tasks autonomously. Ultimately, AI Agents are designed to automate workflows and reduce the need for human intervention. 💡 Which of these AI applications do you think will have the biggest impact on your industry? #GenerativeAI #AIAgents #ArtificialIntelligence #TechComparison #FutureOfWork #Innovation #MachineLearning #AI

The era of typing prompts and waiting for static responses is over. We're moving beyond mere text generation. The latest AI models are operating in real-time, processing complex multimodal inputs simultaneously. This isn't just an upgrade; it's a fundamental redefinition of human-computer interaction. These new agents understand nuances from voice tone, visual cues, and contextual environment instantly. They can interrupt, clarify, and guide conversations with a fluidity previously confined to science fiction. This capability transcends simple efficiency gains, enabling entirely new workflows and problem-solving paradigms. It's about AI becoming a genuine, active participant rather than a passive tool. The implications are profound, yet many organizations are still optimizing for last year's AI stack. Building for real-time multimodal AI requires a complete re-evaluation of data pipelines, interaction design, and trust frameworks. Simply bolting on new APIs won't suffice; a systemic overhaul of how we conceive and deploy intelligent systems is mandatory. Legacy approaches will be rapidly outmaneuvered. This shift brings us closer to truly intelligent agents that perceive and act within our world, not just a digital one. The promise is immense, from advanced personal assistants to dynamic industrial guidance. However, the ethical and operational challenges of such pervasive, real-time AI are equally significant. Are our organizational structures and regulatory frameworks prepared for this omnipresent intelligence? #AI #ArtificialIntelligence #MultimodalAI #RealTimeAI #FutureOfWork #Innovation

The industry is still fixated on prompt engineering, but the real leverage has already moved. We are past the point of merely crafting perfect single inputs for AI. The critical skill now is designing and orchestrating autonomous AI agents that manage complex, multi-step workflows. This isn't just an incremental improvement; it's a fundamental paradigm shift from five months ago. The focus has decisively transitioned from static prompt optimization to dynamic agentic design. AI systems now autonomously plan, execute, and self-correct across entire processes. These advanced agents seamlessly integrate with external tools, conduct intricate research, and adapt strategies in real-time. They transform AI from a simple task assistant into a robust workflow orchestrator. Businesses must understand this shift to delegate full missions, not just isolated micro-tasks. Organizations stuck on basic prompting are falling behind a rapidly evolving curve. The cutting edge demands sophisticated, self-improving AI systems capable of true operational autonomy. Is your strategy still about prompts, or are you building the agentic future? #AI #AIAgents #WorkflowAutomation #PromptEngineering #GenerativeAI #FutureofWork

🔥Forget the Buzzwords—Here Are 7 AI Terms Defining the Future 1️⃣ Agentic AI – autonomous agents that perceive, reason, act & learn. 2️⃣ Large Reasoning Models (LRMs) – LLMs fine-tuned for multi-step problem solving. 3️⃣ Vector Databases – store data as vectors for semantic similarity search. 4️⃣ RAG (Retrieval Augmented Generation) – enriches LLMs with external knowledge. 5️⃣ Model Context Protocol (MCP) – a standard to connect LLMs with tools & data. 6️⃣ Mixture of Experts (MoE) – efficient scaling using specialized subnetworks. 7️⃣ ASI (Artificial Superintelligence) – theoretical future AI far beyond human intelligence. 💡 These concepts aren’t just buzzwords—they’re shaping the next wave of AI applications across industries. #AI #MachineLearning #RAG #AgenticAI #Tech

AI Prompts: Genie or Engineer? Crafting the perfect AI prompt feels like summoning a genie – wish vaguely, get unpredictable results. But a well-defined prompt, like talking to a skilled engineer, yields precise, reliable outcomes. Learn to bridge the gap between wish and command for better AI results. * Clearly define your goals. * Specify your desired output format. * Iterate and refine your prompts. Unlock the full potential of AI by mastering prompt engineering! #AI,#PromptEngineering,#ArtificialIntelligence,#Innovation,#Productivity

🚫 LLM ≠ Generative AI ≠ AI Agents ≠ Agentic AI 🚫 We need to stop lumping all “AI” into one bucket. Each of these technologies is fundamentally different—with unique purposes, architectures, and value for problem-solving. 🔹 LLM Pattern prediction, pure and simple. No memory. No intent. Just transforms input into output based on statistical patterns. 🔹 Generative AI Built on top of LLMs, it creates text, code, images, and more by exploring latent space. Novel content—but always waits for instructions. 🔹 AI Agents Go beyond prediction: they execute clear “jobs to be done.” They can detect intent, call APIs, and deliver results. Modular, practical—yet still not truly autonomous. 🔹 Agentic AI This is next-level: goals, memory, planning, adaptation, and orchestration. Agentic AI reasons, calls sub-agents, monitors progress, and self-directs—no human step-by-step needed. ⚡ This isn’t just stacking up features. It’s a paradigm shift—from raw prediction to autonomous orchestration. If you’re building in the AI space, clarity on where your system sits in this stack determines everything: architecture, tooling, risk, and ultimately user value. Which layer do you see as the biggest disruptor for your industry? Drop your thoughts below! 👇 #AI #LLM #GenerativeAI #AIAgents #AgenticAI #Innovation #TechTransformation

The quality of an AI's answer isn't just in the prompt. It's in the world you build around it. This isn't about better phrasing; it's about Context Engineering. Forget merely optimizing queries. Context Engineering is the systematic discipline of designing a complete informational environment for your AI before it generates a single token. It's the critical evolution from basic prompt engineering to truly advanced AI performance. This "world" includes: ✅ System Prompts: Defining the AI's core persona and parameters. ✅ Retrieved Documents: Actively fetching knowledge from internal bases. ✅ Tool Outputs: Real-time data from APIs (e.g., user calendars, live stats). ✅ Implicit Data: User identity, interaction history, and environmental state. Even advanced LLMs underperform with a limited view. Context Engineering reframes the task: not just answering a question, but building a comprehensive operational picture for the agent. Imagine an AI that integrates your calendar, knows your relationship with an email recipient, and recalls past meeting notes all before drafting a response. This robust pipeline, often refined by specialized tuning systems (like Vertex AI's prompt optimizer), is how we transform stateless chatbots into highly capable, situationally-aware systems. Are you just prompting your AI Agents, or are you engineering its world? #ContextEngineering #PromptEngineering #AI #LLMs #AIAgents #GenerativeAI #MachineLearning #TechTrends #Innovation

From Workflows to Agentic AI: Understanding the Evolution of AI Systems 🚀 The landscape of AI systems is evolving quickly, and it helps to understand the differences between key approaches: 🔹 𝐋𝐋𝐌 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 – The starting point. A user prompt triggers predefined rules, which then use a large language model (and sometimes data sources or tools) to generate an output. 🔹 𝐑𝐀𝐆 (𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧) – Enhances LLMs with external knowledge. Prompts are paired with data retrieval from vector databases, ensuring responses are more accurate and grounded in facts. 🔹 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 – Introduce autonomy. Beyond generating text, agents can plan, reason, access memory, and use tools or databases to execute more complex tasks. 🔹 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 – The most advanced stage. Multiple agents collaborate, reason across tasks, and even involve humans in the loop. This enables adaptive, multi-step workflows that resemble teamwork rather than simple Q&A. In short: AI is moving from rule-based workflows → knowledge-enhanced generation → autonomous agents → collaborative agent ecosystems. 👉For those who’d like to go deeper, I’ve put together a 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐩𝐥𝐚𝐲𝐥𝐢𝐬𝐭 where I break down Gen AI concepts, RAG, agents, frameworks, and practical roadmaps in a structured way: https://lnkd.in/gkv-UHr7 #LLM #ArtificialIntelligence #AIAgents #RAG #GenerativeAI #DataScience #AIEngineering #MachineLearning

From Workflows to Agentic AI: The Evolution in Motion 🚀 I came across this great breakdown on how AI systems are maturing — from workflows → RAG → agents → agentic ecosystems. What resonated with me: ✨ LLM Workflows feel like the training wheels — effective but limited. ✨ RAG gave us grounding in truth, moving beyond “just eloquent text.” ✨ Agents are where orchestration and autonomy kick in. ✨ And Agentic AI? That’s where it starts to feel like teamwork — humans and AI agents collaborating across steps, memory, and reasoning. I’m seeing this shift every day from embedding RAG in pipelines to experimenting with agentic loops for ROI validation. The pace of evolution is thrilling, but also requires us to rethink design, governance, and trust. If you’re exploring these shifts, this YouTube playlist is a handy resource: https://lnkd.in/gkv-UHr7 Thanks Sumit Kumar Dash Curious: where do you think your org is on this spectrum today — workflows, RAG, agents, or agentic?