By skyforbes 
The Agentic AI shift demands a very different stack — not just in terms of tools, but in mindset, workflows, and design principles.
Here’s what you really need to know:
𝟭. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗦𝘁𝗮𝗿𝘁𝘀 𝘄𝗶𝘁𝗵 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻
Most people confuse AI agents with smart LLM wrappers. But true agents have:
• Goals — not just tasks
• Context management — not just one-off memory
• Autonomy & adaptability — not just API chains
• Multi-agent coordination — not just sequential steps
The rise of protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent) show where we’re headed: agents talking, negotiating, and collaborating.
𝟮. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗜𝘀𝗻’𝘁 𝗗𝗲𝗮𝗱 — 𝗜𝘁’𝘀 𝗘𝘃𝗼𝗹𝘃𝗶𝗻𝗴
To build agents, you still need the fundamentals:
• Languages: Python, JS, TypeScript, Shell
• Tooling: APIs, async execution, file handling, scraping
But now layered with:
• Prompt engineering → Chain-of-thought → Reflexion loops
• Goal decomposition + decision policies
• Tool use + action planning + retry logic
• Prompting is no longer a skill. It’s a system behavior.
𝟯. 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗔𝗿𝗲 𝗘𝘅𝗽𝗹𝗼𝗱𝗶𝗻𝗴 — 𝗕𝘂𝘁 𝗨𝘀𝗲 𝗧𝗵𝗲𝗺 𝗪𝗶𝘀𝗲𝗹𝘆
• Depending on your use case, you’ll want to explore:
• LangGraph and LangChain for flexible agent flows
• AutoGen and CrewAI for research-style agents
• Flowise for visual low-code orchestrations
• Superagent, Semantic Kernel, and others for modular design
Each framework has strengths and trade-offs — choosing one requires understanding your orchestration, memory, and collaboration needs.
𝟰. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗛𝗲𝗮𝗿𝘁 𝗼𝗳 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺𝘀
Forget linear pipelines.
Agent systems require:
• DAG-based flows
• Event-driven triggers
• Conditional loops
• Guardrails and validations
The goal is not to run code — it’s to simulate reasoning and adaptation over time.
𝟱. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗜𝘀𝗻’𝘁 𝗝𝘂𝘀𝘁 𝗮 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝘁𝗼𝗿𝗲
Real agents need:
• Short-term memory (context windows)
• Long-term memory (episodic retrieval)
• Dynamic knowledge integration (RAG + vector DBs)
• Technologies like Weaviate, Chroma, Pinecone, and FAISS make this possible — but only when paired with intelligent memory policies and indexing strategies.
𝟲. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗿𝗲 𝗡𝗼𝗻-𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲
As agents gain autonomy, we need:
• Tracing & logging (LangSmith, OpenTelemetry)
• Human-in-the-loop evaluation
• Auto-evaluation loops
• Security: prompt injection defense, API key mgmt, RBAC, red teaming
You can’t deploy what you can’t monitor.
And you shouldn’t deploy what you can’t secure.
The next generation of AI builders won’t just prompt LLMs — they’ll design intelligent systems.
Agentic AI blends programming, reasoning, memory, orchestration, and governance into one integrated discipline.
…it’s time to think agentically.