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AI Agents · 10 April 2026 · 7 min read

Which AI Agents Are Actually Worth Building

Agents shine on multi-step work across systems — and fail expensively when applied to single-step tasks with clear rules.

Agent frameworks make it easy to build something that looks autonomous. The engineering question is narrower: which processes genuinely benefit from planning and tool use, and which are simply pipelines with extra latency?

01

The multi-system test

An agent adds value when a task requires gathering information from several places, deciding what to do, and acting — and when the sequence varies by case. If the sequence is fixed, write the pipeline.

02

Permissions are the product

The design work in an agent project is mostly about boundaries: which tools it may call, what it may write, when it must ask, and what gets logged.

  • Read-heavy tools open, write tools gated
  • Approval checkpoints for irreversible actions
  • Every tool call logged with inputs and outputs

03

Evaluate on real cases

Build a set of historical cases with known good outcomes and re-run them on every change. Without that harness you cannot tell whether a prompt change was an improvement or a regression.

The takeaway

Build agents for variable, multi-system work; invest in permissions and an evaluation set before scope.

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