Dillon McClendon
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Checklist
2 min read
strategy

An AI Agent Builder Checklist

A concise preflight checklist for deciding whether an agent idea has the context, capabilities, and controls needed to become a real system.

  • agents
  • strategy
  • production
  • checklist

Before building

  • Is there a specific task with a measurable outcome?
  • Is an agent the right shape for the task, or would a deterministic workflow be safer?
  • Who owns the result when the system is wrong?
  • What is the cost of a slow, incomplete, or incorrect outcome?
  • Which decisions must remain with a person?

Context and memory

  • What does the agent need to know for the current step?
  • Can the required information fit into the active context?
  • If information is retrieved, how is relevance evaluated?
  • How will stale, contradictory, or missing information be handled?
  • What data should never be placed in a model context?

System map

The context preflight

Active context and long-term memory The active context window is a limited desk where the current task is processed. A larger filing cabinet stores persistent information, but retrieval is required to bring relevant information onto the desk. CONTEXT IS A WORKING SURFACE PERSISTENT STORAGE Filing cabinet Available, but not active Documents & facts Past observations Business records LIMITED CONTEXT WINDOW The desk Current task + retrieved context GOAL RETRIEVED Instructions Relevant facts TOKEN BUDGET / PRUNE OR SUMMARIZE WHEN FULL retrieve write back only if designed
A memory system needs an intentional path from persistent storage to the active context.

Tools and permissions

  • Does every tool have a narrow, documented input contract?
  • Are tool arguments validated outside the model?
  • Are permissions enforced by the application or only described in a prompt?
  • Are retries safe for actions that can create side effects?
  • Are timeouts and tool failures represented clearly in the next step?
  • Is there a human approval step for irreversible actions?

Evaluation and operations

  • Do representative tasks cover both success and failure cases?
  • Can the team inspect the context, tool calls, and observations for a run?
  • Are quality, latency, cost, and safety measured separately?
  • Is there a way to stop, roll back, or disable the agent?
  • Is there an owner for reviewing failures and updating the system?

A useful decision rule

If the answer to several of these questions is unknown, the next step is not more prompt experimentation. It is a short design exercise: define the workflow, identify the required information, narrow the capability surface, and choose the smallest evaluation set that can prove the idea works.

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