You Don't Need an Agentic Framework to Start Building Agents (www.gnanaguru.com)

AI developer and entrepreneur argues that you don’t need an “agentic” framework to start building useful AI agents—stop hunting for the perfect abstraction and just build. The piece reframes agents as a simple design pattern (inspired by ReAct): a continuous loop of Reason → Act → Observe → Repeat. Practically, that can be a Python while loop that prompts an LLM, parses its JSON output, calls a tool (e.g., CALL get_weather(location="San Francisco")), ingests the response, and loops. Frameworks like LangChain, Mastra, CrewAI or no-code builders (OpenAI Agent Builder, N8N, Dify) are useful for scaling, but they’re not necessary to learn the core pattern. Starting DIY forces you to confront real production problems early: how to store memory (raw list, summaries, vectors), how to declare and safely invoke tools without exposing keys or enabling destructive actions, guardrails to prevent loops and runaway costs, observability and logging to replay chains of thought, secure token/auth management for user-scoped APIs, fallbacks/human-in-the-loop behavior, and evaluation datasets to measure quality. The takeaway: begin with a single-scoped agent, one tool, and iterate—learn the loop, then pick a framework when you know which production gaps you need it to fill.