What Does It Take to Run an AI Agent in a Real Business? Cost, Monitoring and Rollout
Running an AI agent for real: how to estimate cost per task, handle rate limits, monitor it once live, and roll it out in stages, with a start-here checklist.
Practical writing on hiring developers and building software in Nigeria.
Running an AI agent for real: how to estimate cost per task, handle rate limits, monitor it once live, and roll it out in stages, with a start-here checklist.
Keep an AI agent safe: what prompt injection is, why it has no complete fix, and how permissions and human approval limit the damage.
Test an AI agent with evals: realistic tasks, clear graders and repeated runs. Learn pass@k versus pass^k and how to build a first test set.
Several AI agents can beat one on big jobs that split into parts, but they cost more and are harder to run. See the designs, the costs and how agents talk.
How a simple AI agent is built: tool descriptions, a loop, a step limit and a stop condition, shown as a plain sketch, plus what a first version leaves out.
An AI agent only sees its context window. Learn why it forgets, how notes and memory tools help, and what retrieval-augmented generation (RAG) adds.
MCP is an open standard that lets AI apps connect to tools and data the same way every time. Learn how it works, who runs it and where the risks are.
The model never runs a tool itself. It asks for one in a structured request, and your code usually does the work. See the round trip, step by step.
Many jobs need only a plain automation or a single AI call. Here is a simple way to tell when a task really needs an AI agent, with a worked example.
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