We have another series of posts that’s from a business perpective. It’s called Building Successful AI Agents. There are steps in that series that help you decide which business tasks make good candidates for agentic AI projects. You’ll want too decide if it is worth the effort before diving in.

Building Agentic AI: Series Outline

The outline for my upcoming Building Agentic AI series.

1. Post 1 – Choosing Your Build Path: No-Code, Low-Code, or Full-Code

   • Define each approach
   • Pros/cons by skills, budget, complexity
   • Recommendations for different reader types
   • Tie back to the SDG ecosystem example

2. Post 2 – No-Code and Low-Code Starter Tools

   • OpenAI Agent Mode, n8n, Zapier + AI
   • Quick wins with minimal coding
   • Limits of no-code for complex agents

3. Post 3 – Building with LangChain

   • Core concepts: Reasoning, Actions, Memory
   • Simple Python example
   • Where LangChain fits in the agent ecosystem

4. Post 4 – Adding Memory and Context

   • Why agents need memory
   • Vector DBs: Pinecone, Weaviate, FAISS (plain English)
   • Short-term vs. long-term memory

5. Post 5 – Connecting Agents to the World

   • APIs, web scraping, knowledge bases as “senses”
   • Examples of enriching capabilities with data

6. Post 6 – Testing, Evaluating, and Improving Your Agent

   • Success metrics and early issue detection
   • User feedback loops
   • Ethical guardrails

7. Post 7 – Deploying and Maintaining Your Agent

   • Hosting options: cloud, local, hybrid
   • Maintenance and updates
   • Sustainability for long-running agents