AI Frameworks For Software Development

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

AI is not failing because of bad ideas; it’s "failing" at enterprise scale because of two big gaps: 👉 Workforce Preparation 👉 Data Security for AI While I speak globally on both topics in depth, today I want to educate us on what it takes to secure data for AI—because 70–82% of AI projects pause or get cancelled at POC/MVP stage (source: #Gartner, #MIT). Why? One of the biggest reasons is a lack of readiness at the data layer. So let’s make it simple - there are 7 phases to securing data for AI—and each phase has direct business risk if ignored. 🔹 Phase 1: Data Sourcing Security - Validating the origin, ownership, and licensing rights of all ingested data. Why It Matters: You can’t build scalable AI with data you don’t own or can’t trace. 🔹 Phase 2: Data Infrastructure Security - Ensuring data warehouses, lakes, and pipelines that support your AI models are hardened and access-controlled. Why It Matters: Unsecured data environments are easy targets for bad actors making you exposed to data breaches, IP theft, and model poisoning. 🔹 Phase 3: Data In-Transit Security - Protecting data as it moves across internal or external systems, especially between cloud, APIs, and vendors. Why It Matters: Intercepted training data = compromised models. Think of it as shipping cash across town in an armored truck—or on a bicycle—your choice. 🔹 Phase 4: API Security for Foundational Models - Safeguarding the APIs you use to connect with LLMs and third-party GenAI platforms (OpenAI, Anthropic, etc.). Why It Matters: Unmonitored API calls can leak sensitive data into public models or expose internal IP. This isn’t just tech debt. It’s reputational and regulatory risk. 🔹 Phase 5: Foundational Model Protection - Defending your proprietary models and fine-tunes from external inference, theft, or malicious querying. Why It Matters: Prompt injection attacks are real. And your enterprise-trained model? It’s a business asset. You lock your office at night—do the same with your models. 🔹 Phase 6: Incident Response for AI Data Breaches - Having predefined protocols for breaches, hallucinations, or AI-generated harm—who’s notified, who investigates, how damage is mitigated. Why It Matters: AI-related incidents are happening. Legal needs response plans. Cyber needs escalation tiers. 🔹 Phase 7: CI/CD for Models (with Security Hooks) - Continuous integration and delivery pipelines for models, embedded with testing, governance, and version-control protocols. Why It Matter: Shipping models like software means risk comes faster—and so must detection. Governance must be baked into every deployment sprint. Want your AI strategy to succeed past MVP? Focus and lock down the data. #AI #DataSecurity #AILeadership #Cybersecurity #FutureOfWork #ResponsibleAI #SolRashidi #Data #Leadership

When working with Agentic AI, selecting the right framework is crucial. Each one brings different strengths depending on your project needs — from modular agent designs to large-scale enterprise security. Here's a structured breakdown: ➔ 𝗔𝗗𝗞 (𝗚𝗼𝗼𝗴𝗹𝗲) • Features: Flexible, modular framework for AI agents with Gemini support • Advantages: Rich tool ecosystem, flexible orchestration • Applications: Conversational AI, complex autonomous systems ➔ 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 • Features: Stateful workflows, graph-based execution, human-in-the-loop • Advantages: Dynamic workflows, complex stateful AI, enhanced traceability • Applications: Interactive storytelling, decision-making systems ➔ 𝗖𝗿𝗲𝘄𝗔𝗜 • Features: Role-based agents, dynamic task planning, conflict resolution • Advantages: Scalable teams, collaborative AI, decision optimization • Applications: Project simulations, business strategy, healthcare coordination ➔ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗞𝗲𝗿𝗻𝗲𝗹 • Features: AI SDK integration, security, memory & embeddings • Advantages: Enterprise-grade security, scalable architecture • Applications: Enterprise apps, workflow automation ➔ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗔𝘂𝘁𝗼𝗚𝗲𝗻 • Features: Multi-agent conversations, context management, custom roles • Advantages: Simplifies multi-agent orchestration, robust error handling • Applications: Advanced chatbots, task planning, AI research ➔ 𝗦𝗺𝗼𝗹𝗔𝗴𝗲𝗻𝘁𝘀 • Features: Lightweight, modular multi-agent framework • Advantages: Low-compute overhead, seamless integration • Applications: Research assistants, data analysis, AI workflows ➔ 𝗔𝘂𝘁𝗼𝗚𝗣𝗧 • Features: Goal-oriented task execution, adaptive learning • Advantages: Self-improving, scalable, minimal human intervention • Applications: Content creation, task automation, predictive analysis    Choosing the right Agentic AI framework is less about the "most powerful" and more about 𝗺𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸’𝘀 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁'𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆, 𝘀𝗰𝗮𝗹𝗲, 𝗮𝗻𝗱 𝗴𝗼𝗮𝗹𝘀. → Which one have you used or are excited to try? → Did I miss any emerging frameworks that deserve attention?

Everyone's building AI agents, but few understand the Agentic frameworks that power them. These two distinct frameworks are the most used frameworks in 2025, and they aren't competitors but complementary approaches to agent development: 𝗻𝟴𝗻 (𝗩𝗶𝘀𝘂𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻) - Creates visual connections between AI agents and business tools - Flow: Trigger → AI Agent → Tools/APIs → Action - Solves integration complexity and enables rapid deployment - Think of it as the visual orchestrator connecting AI to your entire tech stack 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 (𝗚𝗿𝗮𝗽𝗵-𝗯𝗮𝘀𝗲𝗱 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻) by LangChain - Enables stateful, cyclical agent workflows with precise control - Flow: State → Agents → Conditional Logic → State (cycles) - Solves complex reasoning and multi-step agent coordination - Think of it as the brain that manages sophisticated agent decision-making Beyond technicality, each framework has its core strengths. 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗻𝟴𝗻: - Integrating AI agents with existing business tools - Building customer support automation - Creating no-code AI workflows for teams - Needing quick deployment with 700+ integrations 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵: - Building complex multi-agent reasoning systems - Creating enterprise-grade AI applications - Developing agents with cyclical workflows - Needing fine-grained state management Both frameworks are gaining significant traction: 𝗻𝟴𝗻 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: - Visual workflow builder for non-developers - Self-hostable open-source option - Strong business automation community 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: - Full LangChain ecosystem integration - LangSmith observability and debugging - Advanced state persistence capabilities Top AI solutions integrate both n8n and LangGraph to maximize their potential. - Use n8n for visual orchestration and business tool integration - Use LangGraph for complex agent logic and state management - Think in layers: business automation AND sophisticated reasoning Over to you: What AI agent use case would you build - one that needs visual simplicity (n8n) or complex orchestration (LangGraph)?

You've built your AI agent... but how do you know it's not failing silently in production? Building AI agents is only the beginning. If you’re thinking of shipping agents into production without a solid evaluation loop, you’re setting yourself up for silent failures, wasted compute, and eventully broken trust. Here’s how to make your AI agents production-ready with a clear, actionable evaluation framework: 𝟭. 𝗜𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁 𝘁𝗵𝗲 𝗥𝗼𝘂𝘁𝗲𝗿 The router is your agent’s control center. Make sure you’re logging: - Function Selection: Which skill or tool did it choose? Was it the right one for the input? - Parameter Extraction: Did it extract the correct arguments? Were they formatted and passed correctly? ✅ Action: Add logs and traces to every routing decision. Measure correctness on real queries, not just happy paths. 𝟮. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝘁𝗵𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 These are your execution blocks; API calls, RAG pipelines, code snippets, etc. You need to track: - Task Execution: Did the function run successfully? - Output Validity: Was the result accurate, complete, and usable? ✅ Action: Wrap skills with validation checks. Add fallback logic if a skill returns an invalid or incomplete response. 𝟯. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗣𝗮𝘁𝗵 This is where most agents break down in production: taking too many steps or producing inconsistent outcomes. Track: - Step Count: How many hops did it take to get to a result? - Behavior Consistency: Does the agent respond the same way to similar inputs? ✅ Action: Set thresholds for max steps per query. Create dashboards to visualize behavior drift over time. 𝟰. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿 Don’t just measure token count or latency. Tie success to outcomes. Examples: - Was the support ticket resolved? - Did the agent generate correct code? - Was the user satisfied? ✅ Action: Align evaluation metrics with real business KPIs. Share them with product and ops teams. Make it measurable. Make it observable. Make it reliable. That’s how enterprises scale AI agents. Easier said than done.

📊 What’s the right KPI to measure an AI agent’s performance? Here’s the trap: most companies still measure the wrong thing. They track activity (tasks completed, chats answered) instead of impact. Based on my experience, effective measurement is multi-dimensional. Think of it as six lenses: 1️⃣ Accuracy – Is the agent correct? Response accuracy (right answers) Intent recognition accuracy (did it understand the ask?) 2️⃣ Efficiency – Is it fast and smooth? Response time Task completion rate (fully autonomous vs guided vs human takeover) 3️⃣ Reliability – Is it stable over time? Uptime & availability Error rate 4️⃣ User Experience & Engagement – Do people trust and return? CSAT (outcome + interaction + confidence) Repeat usage rate Friction metrics (repeats, clarifying questions, misunderstandings) 5️⃣ Learning & Adaptability – Does it get better? Improvement over time Adaptation speed to new data/conditions Retraining frequency & impact 6️⃣ Business Outcomes – Does it move the needle? Conversion & revenue impact Cost per interaction & ROI Strategic goal contribution (retention, compliance, expansion) Gartner predicts that by 2027, 60% of business leaders will rely on AI agents to make critical decisions. If that’s true, then measuring them right is existential. So, here’s the debate: Should AI agents be held to the same KPIs as humans (outcomes, growth, value) — or do they need an entirely new framework? 👉 If you had to pick ONE metric tomorrow, what would you measure first? #AI #Agents #KPIs #FutureOfWork #BusinessValue #Productivity #DecisionMaking

The Future of AI is Open-Source! 10 years ago when I started in ML, building out end-to-end ML applications would take you months, to say the least, but in 2025, going from idea to MVP to production happens in weeks, if not days. One of the biggest changes I am observing is "free access to the best tech", which is making the ML application development faster. You don't need to be working in the best-tech company to have access to these, now it is available to everyone, thanks to the open-source community!   I love this visual of the open-source AI stack by ByteByteGo. It lays out the tools/frameworks you can use (for free) and build these AI applications right on your laptop. If you are an AI engineer getting started, checkout the following tools: ↳ Frontend Technologies : Next.js, Vercel, Streamlit ↳ Embeddings and RAG Libraries : Nomic, Jina AI, Cognito, and LLMAware ↳ Backend and Model Access : FastAPI, LangChain, Netflix Metaflow, Ollama, Hugging Face ↳ Data and Retrieval : Postgres, Milvus, Weaviate, PGvector, FAISS ↳ Large Language Models: llama models, Qwen models, Gemma models, Phi models, DeepSeek models, Falcon models ↳ Vision Language Models: VisionLLM v2, Falcon 2 VLM, Qwen-VL Series, PaliGemma ↳ Speech-to-text & Text-to-speech models: OpenAI Whisper, Wav2Vec, DeepSpeech, Tacotron 2, Kokoro TTS, Spark-TTS, Fish Speech v1.5, StyleTTS (I added more models missing in the infographic) Plus, I would recommend checking out the following tools as well: ↳ Agent frameworks: CrewAI, AutoGen, SuperAGI, LangGraph ↳ Model Optimization & Deployment: vLLM, TensorRT, and LoRA methods for model fine-tuning PS: I had shared some ideas about portfolio projects you can build, in an earlier post, so if you are curious about that, check out my past post. Happy Learning 🚀  There is nothing stopping you to start building on your idea! ----------- If you found this useful, please do share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI educational content and insights to help you stay up-to-date in the AI space :)

Check out this framework for building AI Agents that work in production. There are many recommendations out there, so would like your feedback on this one. This is beyond picking a fancy model or plugging in an API. To build a reliable AI agent, you need a well-structured, end-to-end system with safety, memory, and reasoning at its core. Here’s the breakdown: 1.🔸Define the Purpose & KPIs Start with clarity. What tasks should the agent handle? Align goals with KPIs like accuracy, cost, and latency. 2.🔸Choose the Right Tech Stack Pick your tools: language, LLM, frameworks, and databases. Secure secrets early and plan for production-readiness from day one. 3.🔸Project Setup & Dev Practices Structure repos for modularity. Add version control, test cases, code linting, and cost-efficient development practices. 4.🔸Integrate Data Sources & APIs Link your agent with whatever data it needs to take action intelligently from PDFs, Notion, databases, or business tools. 5.🔸Build Memory & RAG Index knowledge and implement semantic search. Let your agent recall facts, documents, and links with citation-first answers. 6.🔸Tools, Reasoning & Control Loops Empower the agent with tools and decision-making logic. Include retries, validations, and feedback-based learning. 7.🔸Safety, Governance & Policies Filter harmful outputs, monitor for sensitive data, and build an escalation path for edge cases and PII risks. 8.🔸Evaluate, Monitor & Improve Use golden test sets and real user data to monitor performance, track regressions, and improve accuracy over time. 9.🔸Deploy, Scale & Operate Containerize, canary-test, and track usage. Monitor cost, performance, and reliability as your agent scales in production. Real AI agents are engineered step by step. Hope this guide gives you the needed blueprint to build with confidence. #AIAgents

AI Adoption: Reality Bites After speaking with customers across various industries yesterday, one thing became crystal clear: there's a significant gap between AI hype and implementation reality. While pundits on X buzz about autonomous agents and sweeping automation, business leaders I spoke with are struggling with fundamentals: getting legal approval, navigating procurement processes, and addressing privacy, security, and governance concerns. What's more revealing is the counterintuitive truth emerging: organizations with the most robust digital transformation experience are often facing greater AI adoption friction. Their established governance structures—originally designed to protect—now create labyrinthine approval processes that nimbler competitors can sidestep. For product leaders, the opportunity lies not in selling technical capability, but in designing for organizational adoption pathways. Consider: - Prioritize modular implementations that can pass through governance checkpoints incrementally rather than requiring all-or-nothing approvals - Create "governance-as-code" frameworks that embed compliance requirements directly into product architecture - Develop value metrics that measure time-to-implementation, not just end-state ROI - Lean into understanability and transparency as part of your value prop - Build solutions that address the career risk stakeholders face when championing AI initiatives For business leaders, it's critical to internalize that the most successful AI implementations will come not from the organizations with the most advanced technology, but those who reinvent adoption processes themselves. Those who recognize AI requires governance innovation—not just technical innovation—will unlock sustainable value while others remain trapped in endless proof-of-concept cycles. What unexpected adoption hurdles are you encountering in your organization? I'd love to hear perspectives beyond the usual technical challenges.

Simular AI recently released Agent S2 - an open-source framework that outperforms OpenAI's and Anthropic's Computer-Use Agents across every major benchmark. The new framework introduces a compositional generalist-specialist architecture that changes how AI agents interact with computer interfaces. While existing solutions struggle with complex GUI navigation and multi-step workflows, Agent S2 achieves state-of-the-art results through intelligent task decomposition and experience-based learning. Highlights: (1) OSWorld benchmark - achieves 34% success rate on 50-step tasks, surpassing OpenAI's CUA at 32% (3) AndroidWorld - reaches 54% success rate, beating UI-TARS' 46% by a significant margin (4) Cross-platform support - works seamlessly across Mac, Windows, and Linux environments with a unified API What sets Agent S2 apart is its ability to learn from past interactions and build a knowledge base that continually improves performance. The framework leverages UI-TARS for visual grounding and integrates Perplexica for web-knowledge retrieval, enabling agents to handle tasks that require both GUI manipulation and real-time information gathering. For developers building automation tools, QA systems, or accessibility solutions, Agent S2 provides the missing infrastructure for reliable computer-use agents that actually work in production. GitHub repo https://lnkd.in/g2H-xcKM This repo and 40+ curated open-source frameworks and libraries for AI agents builders in my recent post https://lnkd.in/g3fntJVc