How to Prepare for Agentic AI Systems

If you’re getting started with AI agents, this is for you 👇 I’ve seen so many builders jump straight into wiring up LangChain or CrewAI without ever understanding what actually makes an LLM act like an agent, and not just a glorified autocomplete engine. I put together a 10-phase roadmap to help you go from foundational concepts → all the way to building, deploying, and scaling multi-agent systems in production. Phase 1: Understand what “agentic AI” actually means → What makes an agent different from a chatbot → Why long-context alone isn’t enough → How tools, memory, and environment drive reasoning Phase 2: Learn the core components → LLM = brain → Memory = context (short + long term) → Tools = actuators → Environment = where the agent runs Phase 3: Prompting for agents → System vs user prompts → Role-based task prompting → Prompt chaining with state tracking → Format constraints and expected outputs Phase 4: Build your first basic agent → Start with a single-task agent → Use UI (Claude or GPT) before code → Iterate prompt → observe behavior → refine Phase 5: Add memory → Use buffers for short-term recall → Integrate vector DBs for long-term → Enable retrieval via user queries → Keep session memory dynamically updated Phase 6: Add tools and external APIs → Function calling = where things get real → Connect search, calendar, custom APIs → Handle agent I/O with guardrails → Test tool behaviors in isolation Phase 7: Build full single-agent workflows → Prompt → Memory → Tool → Response → Add error handling + fallbacks → Use LangGraph or n8n for orchestration → Log actions for replay/debugging Phase 8: Multi-agent coordination → Assign roles (planner, executor, critic) → Share context and working memory → Use A2A/TAP for agent-to-agent messaging → Test decision workflows in teams Phase 9: Deploy and monitor → Host on Replit, Vercel, Render → Monitor tokens, latency, error rates → Add API rate limits + safety rules → Setup logging, alerts, dashboards Phase 10: Join the builder ecosystem → Use Model Context Protocol (MCP) → Contribute to LangChain, CrewAI, AutoGen → Test on open evals (EvalProtocol, SWE-bench, etc.) → Share workflows, follow updates, build in public This is the same path I recommend to anyone transitioning from prompting → to building production-grade agents. Save it. Share it. And let me know what phase you’re in, or where you’re stuck. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: 𝗪𝗵𝗲𝗿𝗲 𝗗𝗼 𝗬𝗼𝘂 𝗘𝘃𝗲𝗻 𝗦𝘁𝗮𝗿𝘁? Over the last few months, I’ve been exploring what it really takes to go from a simple chatbot to a fully autonomous 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺 — something that can 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗮𝗰𝘁, 𝗹𝗲𝗮𝗿𝗻, 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 on its own. And one thing became clear: 𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗯𝘂𝗶𝗹𝗱 𝗶𝘁 𝗮𝗹𝗹 𝗮𝘁 𝗼𝗻𝗰𝗲. 𝗬𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝗶𝘁 𝗶𝗻 𝗹𝗮𝘆𝗲𝗿𝘀. That’s why I created this 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 — to break down the full stack of an agentic AI system into 6 clear modules: ↳ 𝗨𝘀𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 – Web apps, chatbots, APIs using tools like Next.js, FastAPI, Streamlit ↳ 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 – AutoGen, CrewAI, LangGraph coordinating tasks across agents ↳ 𝗧𝗼𝗼𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 – Services like Zapier, Make, OpenAI Functions to act in the real world ↳ 𝗖𝗼𝗿𝗲 𝗟𝗼𝗴𝗶𝗰 – Memory, reasoning, and decision-making with LangChain, LlamaIndex ↳ 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 – LLMs like GPT-4, Claude, Mistral, and Whisper for intelligence ↳ 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 – Cloud, containers, and DBs: AWS, Azure, GCP, SingleStore, Docker ➤ It's not just about plugging in a GPT model. Agentic AI is about combining 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴 + 𝗮𝗰𝘁𝗶𝗼𝗻 + 𝗺𝗲𝗺𝗼𝗿𝘆 + 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 — across a coordinated system. If you're a dev, architect, or founder thinking about how to build this — I hope this gives you a clear path forward. Would love to hear from others: Which part of this stack are you working on right now? What challenges are you seeing in building real-world AI agents?

✅ Day 7 → What Business Leaders Need to Do Today to Get Ready Whether you lead a product team, a data organization, or an enterprise function, here’s where your focus needs to be: 1. Treat AI Agents Like New Hires  We don’t expect new employees to perform without onboarding, access, context, and feedback. The same applies to agents. Before you ask, “What can we automate?” ask, “What would we delegate to a new hire. 2. Stop Building Proof-of-Concepts, Start Building for Use It’s tempting to keep AI in labs or pilots. But many agentic use cases, internal search, ticket resolution, onboarding, document parsing are ready for production today. Don’t aim for perfect. Aim for contained and valuable. One strong use case will teach you more than six experiments. 3. Rethink Accountability, Not Just Accuracy Agentic AI acts with autonomy. That’s the opportunity and the risk. What matters most isn’t whether it gets something wrong (humans do too), but what happens next. Can the system flag uncertainty? Escalate decisions? Show its work? Trust isn't built on precision. It's built on visibility and fallback. 4. Invest in Change Readiness, Not Just Technical Readiness If your teams still see AI as an assistant or novelty, you’ve got a leadership challenge, not a technology one. Start building literacy across product, operations, data, and risk teams now, not when the agent is live. 5. Keep Asking: Where Can AI Think for Us? Not Just Work for Us. We’re no longer just speeding up tasks, we’re offloading choices. That means leadership’s role is changing too. Not just what to build, but how to design teams, decisions, and trust in a world where software can act.

After 2 months deep in #AgenticAI workspace, here's what I've discovered: Your agents are only as good as your business process documentation. Building #AgenticAI without process documentation is like giving Thor's hammer to someone who doesn't know they need to be worthy The power is there, but without the right foundation, it's just an expensive paperweight. Think of documentation as the "worthiness" factor for AI agents. Just like Mjolnir responds only to those who understand its true nature, Agentic AI systems need exhaustive business rules to function effectively. Think detailed training manuals that capture every decision point, exception, and nuance your teams navigate daily. The plot twist: Most organizations lack comprehensive process documentation. What exists is often outdated, incomplete, or trapped in tribal knowledge. Here's your Avengers team for Agentic AI success: 1. Business process experts who map the real workflows (not the theoretical ones) 2. Technology architects who understand system integrations 3. AI specialists who translate human decisions into agent behaviors To AI startups and product managers: Stop leading with the hammer's power. Start by helping organizations become "worthy" through comprehensive process mapping. The companies that dominate B2B Agentic AI will be those who obsess over understanding what people actually do, not what they say they do. The real superpower isn't having the most advanced AI models. It's having the clearest blueprint of how your business actually operates. Only then will your Agentic AI lift off. #AgenticAI #DigitalTransformation