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The tutorials get you an agent that works once. This is the plan for one that works twice. #
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[.]here Getting an agent to do a task once is easy. Wire up an LLM, hand it one tool, loop until it stops, and it works on the first try.
Getting it to do the same task tomorrow, recover when the tool returns an error, and show its work, that is the part nobody posts, and it is where the project dies. Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027 for this reason.
The other trap is the framework. Whatever tops the tutorial list the day you start won’t be the one you ship on: AutoGPT, LangChain, LangGraph, CrewAI, the Agent SDKs, that whole run happened in about 14 months. So the plan below is not about a framework. Eight weeks, one real agent, built on the parts that outlast the churn: the loop, the tools, the context you feed the model, and the evals that tell you whether it holds up tomorrow.
The One Reframe Before the Plan #
An agent is a while loop that makes tool calls.
The model gets a system prompt and a few tools. It loops by thinking, calling a tool…