Building AI Agents: Why Complexity Isn't Understanding, and Basics Still Matter
aishashok14 · x · 2026-09-07
The author flags a common misconception in agent development: treating complexity as proof of understanding. Memory, context, tools, permissions, and execution loops get dismissed as "foundations," yet they're exactly what you revisit when an agent forgets a decision, repeats a step, pauses for approval, or declares things done too early.
Building a useful agent, the author argues, requires two kinds of understanding:
- How the system works: what the model can do, what information it receives, and what it can act on
- Understanding your own workflow: what each decision needs, which steps run independently, where a human must step in, and what counts as done
The second part is surprisingly hard—even for daily work, you'll uncover judgment calls you never wrote down, and those details matter when handing tasks to an agent. A working demo doesn't prove those decisions were handled well; workflows can be complex, but the fundamentals remain optional.
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