Developer Advocate @ Milvus open-source vector database | RAG Pipeline | LLM Finetuning | Data Science | Model Training
Remember our amazement with GPT-3 in 2020? Back then, we thought AI writing articles was already magical... Today, AI can assemble teams and collaborate autonomously. This isn't just an upgrade. It's a fundamental shift in how AI works. 🚀 Four Stages of AI Evolution 𝐒𝐭𝐚𝐠𝐞 1: 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐋𝐋𝐌 → Pure text generation with stateless processing → Lightweight & fast, but lacks memory and contextual awareness → Best for: Simple conversations, content creation, basic Q&A 𝐒𝐭𝐚𝐠𝐞 2: 𝐑𝐀𝐆 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭 → Dynamic document retrieval, improve factual accuracy → Real-time data access with multimodal retrieval support → Best for: Domain-specific Q&A, knowledge-intensive tasks 𝐒𝐭𝐚𝐠𝐞 3: 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 → Integrates planning, memory, and external tool calling → Multi-step workflow automation execution → Best for: Research assistants, report generation, API-driven workflows 𝐒𝐭𝐚𝐠𝐞 4: 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 → Multiple specialized agents collaborating → Self-organizing AI teams with continuous learning and optimization → Best for: Cross-departmental coordination, large-scale automation operations 💡 Key Transformation The fundamental leap from "passive response" to "proactive collaboration": ✅ Memory Evolution: Short-term → Long-term → Episodic memory ✅ Decision Capability: Simple replies → Complex reasoning chains → Multi-agent negotiation ✅ Tool Integration: None → Single tool → Tool ecosystem ✅ Autonomy: Fully dependent → Semi-autonomous → Fully autonomous 🔮 Real Application Comparison 𝐋𝐋𝐌: "Help me write a market analysis report." 𝐑𝐀𝐆: "Write an analysis report based on the latest market data." 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭: "Collect data, analyze trends, generate a report, and send to relevant personnel." 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈: "Assemble research teams, collaborate on tasks, continuously track market changes, and update strategies." 🤝 Bottom Line: Choosing between LLM, RAG, AI Agent, or Agentic AI isn't about hierarchy. It's about fit. Match the solution to the scope of your problem.
The leap from “passive response” → “proactive collaboration” is the real game-changer. Agentic AI especially feels like the point where AI stops being just a tool and starts acting as a true digital co-worker. And while building these systems is exciting, one challenge that often gets overlooked is observability, how do we debug, monitor, and trust these multi-agent workflows at scale? That’s where platforms like LLUMO AI are stepping in bringing clarity and reliability to complex agent ecosystems. Explore: https://bit.ly/3GSyvXI Subscribe on LinkedIn: https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7264618895892758528
i cant help feeling that agentic ai is just a developers' fad. bit like when every website went responsive design because the devs were handed control of the UI/UX. then had it taken away when everyone realised responsive was fine as pipeline architecture to render across multiple displays from one server, but abysmal as a UI/UX. what we have here is devs realising that in the real world division of labour is a thing (c.f. Adam Smith describing the pin factory in The Wealth of Nations (Book I, Chapter I), 1776). All agentic AI is really is running asynchronous subroutines to implement a 250 year-old process. the kind of thing you did in the 1980s in BASIC (e.g. event loops), FORTH (cooperative multitasking, interrupt-driven assembly)...the only difference is the blackbox now is a stochastic text generator rather than deterministic code. So agenticAI= marketing layer on long-est. principles of concurrency and modularisation, dressed up in cognitive metaphors. you heard it here first. oh, and if you displace nlp with sfl across the board you redevelop global computing into a semiotic system built on context, not around it. and apart from replacing some/all hardware -the big guns can afford it- you save billions on efficiency gains.
No BS AI/ML Content | ML Engineer with a Plot Twist 🥷50M+ Views 📝
2moThis framework really clarifies when RAG alone is enough versus when full agentic AI is needed.