Building Reliable AI Agents That Don't Fall Over

Most agent demos work once and then quietly rot. The gap between a flashy notebook and an agent you trust to run unattended is mostly boring engineering — the same discipline you'd apply to any distributed system.

The failure modes nobody shows you

When I started shipping agents at scale, three things broke first:

1. Tool calls that silently return garbage. The model happily continues.
2. Infinite loops where the agent re-tries the same broken action.
3. Cost blowups from runaway token usage on a single stuck task.

Design tools like APIs, not chat

A tool is a contract. Give it a tight schema, validate inputs, and return structured errors the model can actually reason about.

type ToolResult<T> =
  | { ok: true; data: T }
  | { ok: false; error: string; retryable: boolean };

async function searchDocs(query: string): Promise<ToolResult<Doc[]>> {
  if (!query.trim()) {
    return { ok: false, error: "query was empty", retryable: false };
  }
  // ...
}

The retryable flag matters: it lets your orchestration layer decide whether to back off or hand control back to the model.

Budget everything

Every agent run gets a hard ceiling:

• max tool calls
• max wall-clock time
• max tokens

An agent without a budget is a denial-of-service attack you wrote yourself.

Observability is the whole game

You cannot debug what you cannot see. I log every step as a structured event:

• the prompt sent
• the tool chosen and its arguments
• the raw result
• the model's reasoning trace (when available)

With that, a failed run becomes a replayable trace instead of a mystery.

Takeaways

• Treat tools as validated APIs with structured errors.
• Put hard budgets on every run.
• Make every step observable and replayable.

Reliability isn't a model problem. It's a systems problem — and that's good news, because we already know how to build reliable systems.