How AI Frameworks Are Evolving In 2025

If you’re an AI engineer building a full-stack GenAI application, this one’s for you. The open agentic stack has evolved. It’s no longer just about choosing the “best” foundation model. It’s about designing an interoperable pipeline, from serving to safety- that can scale, adapt, and ship. Let’s break it down 👇 🧠 1. Foundation Models Start with open, performant base models. → LLaMA 4 Maverick, Mistral‑Next‑22B, Qwen 3 Fusion, DeepSeek‑Coder 33B These models offer high capability-per-dollar and robust support for multi-turn reasoning, tool use, and fine-grained control. ⚙️ 2. Serving & Fine-Tuning You can’t scale without efficient inference. → vLLM, Text Generation Inference, BentoML for blazing-fast throughput → LoRA (PEFT) and Ollama for cost-effective fine-tuning If you’re not using adapter-based fine-tuning in 2025, you’re overpaying and underperforming. 🧩 3. Memory & Retrieval RAG isn’t enough, you need persistent agent memory. → Mem0, Weaviate, LanceDB, Qdrant support both vector retrieval and structured memory → Tools like Marqo and Qdrant simplify dense+metadata retrieval at scale → Model Context Protocol (MCP) is quickly becoming the new memory-sharing standard 🤖 4. Orchestration & Agent Frameworks Multi-agent systems are moving from research to production. → LangGraph = workflow-level control → AutoGen = goal-driven multi-agent conversations → CrewAI = role-based task delegation → Flowise + OpenDevin for visual, developer-friendly pipelines Pick based on agent complexity and latency budget, not popularity. 🛡️ 5. Evaluation & Safety Don’t ship without it. → AgentBench 2025, RAGAS, TruLens for benchmark-grade evals → PromptGuard 2, Zeno for dynamic prompt defense and human-in-the-loop observability → Safety-first isn’t optional, it’s operationally essential 👩💻 My Two Cents for AI Engineers: If you’re assembling your GenAI stack, here’s what I recommend: ✅ Start with open models like Qwen3 or DeepSeek R1, not just for cost, but because you’ll want to fine-tune and debug them freely ✅ Use vLLM or TGI for inference, and plug in LoRA adapters for rapid iteration ✅ Integrate Mem0 or Zep as your long-term memory layer and implement MCP to allow agents to share memory contextually ✅ Choose LangGraph for orchestration if you’re building structured flows; go with AutoGen or CrewAI for more autonomous agent behavior ✅ Evaluate everything, use AgentBench for capability, RAGAS for RAG quality, and PromptGuard2 for runtime security The stack is mature. The tools are open. The workflows are real. This is the best time to go from prototype to production. ----- Share this with your network ♻️ I write deep-dive blogs on Substack, follow along :) https://lnkd.in/dpBNr6Jg

I spent 3+ hours in the last 2 weeks putting together this no-nonsense curriculum so you can break into AI as a software engineer in 2025. This post (plus flowchart) gives you the latest AI trends, core skills, and tool stack you’ll need. I want to see how you use this to level up. Save it, share it, and take action. ➦ 1. LLMs (Large Language Models) This is the core of almost every AI product right now. think ChatGPT, Claude, Gemini. To be valuable here, you need to: →Design great prompts (zero-shot, CoT, role-based) →Fine-tune models (LoRA, QLoRA, PEFT, this is how you adapt LLMs for your use case) →Understand embeddings for smarter search and context →Master function calling (hooking models up to tools/APIs in your stack) →Handle hallucinations (trust me, this is a must in prod) Tools: OpenAI GPT-4o, Claude, Gemini, Hugging Face Transformers, Cohere ➦ 2. RAG (Retrieval-Augmented Generation) This is the backbone of every AI assistant/chatbot that needs to answer questions with real data (not just model memory). Key skills: -Chunking & indexing docs for vector DBs -Building smart search/retrieval pipelines -Injecting context on the fly (dynamic context) -Multi-source data retrieval (APIs, files, web scraping) -Prompt engineering for grounded, truthful responses Tools: FAISS, Pinecone, LangChain, Weaviate, ChromaDB, Haystack ➦ 3. Agentic AI & AI Agents Forget single bots. The future is teams of agents coordinating to get stuff done, think automated research, scheduling, or workflows. What to learn: -Agent design (planner/executor/researcher roles) -Long-term memory (episodic, context tracking) -Multi-agent communication & messaging -Feedback loops (self-improvement, error handling) -Tool orchestration (using APIs, CRMs, plugins) Tools: CrewAI, LangGraph, AgentOps, FlowiseAI, Superagent, ReAct Framework ➦ 4. AI Engineer You need to be able to ship, not just prototype. Get good at: -Designing & orchestrating AI workflows (combine LLMs + tools + memory) -Deploying models and managing versions -Securing API access & gateway management -CI/CD for AI (test, deploy, monitor) -Cost and latency optimization in prod -Responsible AI (privacy, explainability, fairness) Tools: Docker, FastAPI, Hugging Face Hub, Vercel, LangSmith, OpenAI API, Cloudflare Workers, GitHub Copilot ➦ 5. ML Engineer Old-school but essential. AI teams always need: -Data cleaning & feature engineering -Classical ML (XGBoost, SVM, Trees) -Deep learning (TensorFlow, PyTorch) -Model evaluation & cross-validation -Hyperparameter optimization -MLOps (tracking, deployment, experiment logging) -Scaling on cloud Tools: scikit-learn, TensorFlow, PyTorch, MLflow, Vertex AI, Apache Airflow, DVC, Kubeflow

2025 is the Year of Anthropic's MCP and Google's A2A. Everyone's talking about AI agents, but few understand the protocols that power them. 2025 is witnessing two pivotal protocols with two outstanding standards that aren't competitors, but complementary layers in the AI infrastructure: 𝗠𝗖𝗣 (𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) by Anthropic • Creates vertical connections between applications and AI models • Flow: Application → Model → External Tools/Data • Solves context window limitations and standardizes tool access • Think of it as the nervous system connecting your brain to your body's tools 𝗔𝟮𝗔 (𝗔𝗴𝗲𝗻𝘁-𝘁𝗼-𝗔𝗴𝗲𝗻𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) by Google • Enables horizontal communication between independent AI agents • Flow: Agent ↔ Agent (peer-to-peer) • Solves agent interoperability and complex multi-specialist workflows • Think of it as the language that lets different experts collaborate on your behalf Beyond technicality, each protocol has its core strengths. 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗠𝗖𝗣: • Building document Q&A systems • Creating code assistance tools • Developing personal data assistants • Needing fine-grained control over context 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗔𝟮𝗔: • Orchestrating multi-agent workflows • Automating cross-department processes • Creating agent marketplaces • Building distributed problem-solving systems Both protocols are gaining significant traction: 𝗠𝗖𝗣 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: • Backed by major LLM providers (Anthropic, OpenAI, Google) • Strong developer tooling and SDKs • Focus on model-tool integration • Open-source with growing community support 𝗔𝟮𝗔 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: • 50+ enterprise partners at launch • Emphasis on business workflow integration • Strong multimodal capabilities • Built for enterprise-grade applications Top AI solutions integrate both MCP and A2A to maximize their potential. • Use MCP to give your models access to tools and data • Use A2A to orchestrate collaboration between specialized agents • Think in layers: model-tool integration AND agent-agent communication Over to you: What tasks for AI agent do you think would benefit the most for A2A Protocol over MCP?