24 Checks to Run on Your AI Agent Design Before You Write a Single Line of Code

Most AI agent projects fail not because the model is wrong but because the architecture was under-specified before coding started. Scope creep, missing fallback logic, and runaway tool loops are all detectable at the design stage -- if you know what to look for. This audit gives you 24 concrete checks organized by failure category.

The 6 Failure Categories This Audit Covers

Agent failures cluster into six categories. The audit has 4 checks per category (24 total):

1. Scope bleed -- the agent takes actions outside its intended domain
2. Tool loop / infinite retry -- the agent gets stuck calling the same tool repeatedly
3. Memory mismanagement -- context window fills, summaries lose critical state
4. Permission overreach -- the agent is granted more API/filesystem/network access than the task requires
5. Failure silence -- errors are swallowed, the agent returns a hallucinated success
6. Human handoff gaps -- no defined trigger for when the agent escalates to a human

Each category contains checks that are binary (pass/fail) and can be answered before writing code, purely from the design document or architecture diagram.

8 of the 24 Checks (Free Preview)

Here are 8 checks from the full 24 -- one or two per category -- to show the format:

• [Scope] Is there a written list of tools the agent is explicitly not allowed to call? If not, any tool you add to the environment is implicitly in scope.
• [Scope] Does the agent's system prompt include a hard stop condition that triggers when the task is complete? Without it, some models continue taking actions past completion.
• [Loop] Is there a maximum tool-call count per session defined in code, not just in the prompt? Prompts can be overridden by injected user messages; code-level limits cannot.
• [Loop] What happens if the tool returns an error on every retry? Is there an exit path, or does the agent retry indefinitely?
• [Memory] If the context window fills during a long task, does the agent summarize and continue, or silently drop earlier context? Have you tested which behavior your model exhibits?
• [Permission] Is the agent running under a service account with the minimum permissions required for its specific task? Principle of least privilege applies to agents as much as to cloud IAM.
• [Silence] Does the agent distinguish between "task completed" and "task completed successfully"? Returning a success message after a tool call error is one of the most common silent failures.
• [Handoff] Is there a defined condition (confidence threshold, error count, time limit) that causes the agent to stop and ask a human rather than continue?

How to Run the Audit on Your Design

The audit is designed to be run before the first line of agent code. Sequence:

1. Write a one-page design doc first: inputs, tools available, outputs expected, scope boundaries, success definition.
2. Run the 24-check audit against the design doc, not against running code. Most failures are findable at this stage at zero cost.
3. For every FAIL: decide -- fix the design now, or consciously accept the risk with a written note explaining why it is tolerable in this context.
4. Re-run after any scope change. The most common time to re-introduce a failure is when a new tool is added mid-build.

The audit takes 15-30 minutes on a typical agent design. Finding a tool-loop or permission-overreach failure at design time costs 15 minutes. Finding it in production costs days of debugging and potentially real-world side effects that cannot be undone.

What This Does Not Replace

The pre-build audit is not a substitute for production monitoring, evaluation datasets, or security pen-testing. It is specifically the before-you-code gate that most teams skip. After shipping, you still need:

• A logging layer that captures every tool call and its response
• An eval harness with golden test cases covering the edge cases you identified in the audit
• A circuit breaker that halts the agent if error rate exceeds a threshold in production

The audit catches structural failures. Monitoring catches runtime drift. Both are required.

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