Building AI Agents That Actually Work

After months of building with AI agents, I've learned that the difference between a demo and production isn't about the model, it's about understanding limitations and designing around them.

When I first started building AI-powered features for our products, I made every mistake in the book. I assumed the AI would "just work." I didn't plan for failures. I trusted outputs without verification. The result was a mess.

But after shipping several production AI features and building tools like PromptEngine, I've developed a framework for thinking about AI agents that actually holds up under real-world conditions. Here's what I've learned.

The Fundamental Problem with AI Agents

Most AI agent demos are impressive because they show the happy path. The agent receives a clear instruction, executes perfectly, and produces exactly what was expected. But production is never the happy path.

In production, you encounter:

• Ambiguous user inputs that the agent interprets incorrectly
• Edge cases in your data that produce unexpected outputs
• Rate limits, timeouts, and API failures
• Prompt injection attempts (intentional or accidental)
• Model updates that subtly change behavior

The Three-Layer Architecture

I've settled on a three-layer architecture for building reliable AI agents. Each layer has a specific responsibility and failure mode.

Layer 1: The Orchestration Layer

This layer handles the "meta" concerns: routing requests to the right agent, managing state across multi-step workflows, and handling failures gracefully.

class AgentOrchestrator
  def execute(task, context:, max_retries: 3)
    agent = select_agent(task)

    with_retry(max_retries) do
      result = agent.execute(task, context)

      # Always validate before returning
      if valid?(result, task.expected_schema)
        log_success(task, result)
        result
      else
        raise ValidationError, "Output failed schema validation"
      end
    end
  end
end

The key insight here is that validation is not optional. Every agent output must be validated against an expected schema before it's used.

Layer 2: The Agent Layer

Individual agents are responsible for a single, well-defined task. They receive structured input, call the LLM, and return structured output.

class CodeReviewAgent
  def initialize(llm_client:, prompt_engine:)
    @llm = llm_client
    @prompts = prompt_engine
  end

  def execute(diff, context)
    prompt = @prompts.render(
      "code_review/analyze",
      diff: diff,
      language: context[:language],
      style_guide: context[:style_guide]
    )

    response = @llm.complete(prompt, schema: ReviewSchema)
    CodeReview.new(response)
  end
end

Layer 3: The Prompt Layer

This is where PromptEngine came from. Prompts are data, not code. They should be versioned, tested, and managed separately from your application logic.

Handling Failures Gracefully

The most important thing I've learned is that failure is not exceptional, it's expected. Every AI agent will fail. The question is how you handle it.

1. Implement circuit breakers to prevent cascade failures when an upstream service is degraded.
2. Use exponential backoff with jitter for retries, especially during high-traffic periods.
3. Provide graceful degradation paths. If the AI can't help, what's the fallback?
4. Log everything. You can't fix what you can't see. Structured logging is essential.

The best AI agents are the ones that know when they don't know. Building in uncertainty estimation and explicit "I can't help with this" responses is crucial.

Lesson learned after many production incidents

Testing AI Agents

Testing AI is hard because outputs are non-deterministic. But that doesn't mean you can't test. Here's my approach:

Test Type	What It Tests	When to Use
Schema tests	Output structure	Every PR
Golden tests	Known input/output pairs	Critical paths
Eval suites	Quality metrics over datasets	Weekly/before releases
Adversarial tests	Prompt injection, edge cases	Security reviews

What's Next

I'm continuing to build tools and frameworks for reliable AI agents. PromptEngine is just the start. I'm working on:

• An evaluation framework for Rails AI features
• Better observability tooling for agent workflows
• Patterns for human-in-the-loop verification

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If you're building AI agents and want to chat about architecture, testing, or just share war stories, reach out. I'm always happy to talk shop.