Building Reliable AI Tools

After building dozens of AI agents, I've developed a set of patterns that consistently work. Here's the playbook.

The Foundation: Structured Output

The single most important pattern is forcing structured output from your LLM. Never rely on parsing free-form text.

const schema = z.object({
  action: z.enum(["approve", "reject", "escalate"]),
  confidence: z.number().min(0).max(1),
  reasoning: z.string(),
  nextSteps: z.array(z.string()).optional(),
});

const result = await model.generate({
  prompt: buildPrompt(context),
  schema: schema,
});

This simple pattern eliminates 80% of production issues. The model either returns valid structured data or fails explicitly.

Error Handling That Actually Works

Most AI code has optimistic error handling. Here's what production requires:

class AgentError(Exception):
    """Base class for agent errors with context preservation."""

    def __init__(self, message: str, context: dict = None, recoverable: bool = False):
        self.context = context or {}
        self.recoverable = recoverable
        super().__init__(message)

async def execute_with_retry(
    func: Callable,
    max_retries: int = 3,
    backoff_base: float = 1.0
) -> Any:
    for attempt in range(max_retries):
        try:
            return await func()
        except AgentError as e:
            if not e.recoverable or attempt == max_retries - 1:
                raise
            await asyncio.sleep(backoff_base * (2 ** attempt))

The goal isn't to prevent all errors. It's to fail gracefully and give yourself a path to recovery.

The Three-Layer Architecture

Every reliable agent follows this structure:

1. Orchestration Layer — Manages state, routing, and recovery

   • Tracks conversation history
   • Handles tool routing
   • Implements retry logic

2. Tool Layer — Executes specific actions

   • Each tool is a pure function
   • Input/output schemas are explicit
   • No side effects outside the tool's scope

3. Integration Layer — Connects to external systems

   • Handles authentication
   • Manages rate limits
   • Transforms data formats

Here's why this matters:

• Testability — Each layer can be tested independently
• Debuggability — Clear boundaries make logs meaningful
• Maintainability — Changes are isolated to their layer

Tool Design Principles

Tools are the hands of your agent. Design them carefully:

interface Tool<TInput, TOutput> {
  name: string;
  description: string;  // This is what the LLM sees
  inputSchema: z.ZodType<TInput>;
  outputSchema: z.ZodType<TOutput>;
  execute: (input: TInput) => Promise<TOutput>;
}

const searchCustomers: Tool<SearchInput, Customer[]> = {
  name: "search_customers",
  description: "Search for customers by name, email, or phone number",
  inputSchema: z.object({
    query: z.string(),
    limit: z.number().default(10),
  }),
  outputSchema: z.array(CustomerSchema),
  execute: async (input) => {
    // Implementation
  },
};

Keep tools focused. A tool that does one thing well is more reliable than a tool that tries to do everything.

Monitoring and Observability

You can't improve what you can't measure. Essential metrics:

Log everything with correlation IDs. When something breaks at 3am, you'll thank yourself.

Common Pitfalls

After reviewing many failed AI projects, here are the patterns to avoid:

• Over-prompting — Long prompts don't mean better results. Be concise.
• Under-testing — If you haven't tested the edge case, assume it will fail.
• Ignoring latency — Users notice. Batch where possible.
• Monolithic agents — Break complex workflows into specialized sub-agents.

Getting Started

If you're building your first production agent:

1. Start with a single, well-defined use case
2. Build structured output first
3. Add comprehensive logging before adding features
4. Test with adversarial inputs early
5. Plan for the integration layer to take 60% of your time

Need help building reliable AI tools? Let's talk.