Building AI Agents for Production: What We've Learned

The AI demos you see on Twitter look effortless. But production AI agents need to handle edge cases, maintain consistent performance, integrate with existing systems, and fail gracefully when things go wrong.

We've found that about 80% of the effort in AI projects isn't the AI itself—it's the infrastructure, error handling, monitoring, and integration work that makes it reliable enough for real users.

Here are the challenges that matter most in production:

• Latency management: Users won't wait 30 seconds for LLM responses. We've implemented streaming, caching, and parallel processing to keep response times under 3 seconds.
• Error handling: LLMs fail in unpredictable ways. We build robust validation, retry logic, and fallback mechanisms so one bad response doesn't break the entire workflow.
• Cost control: Running GPT-4 on every request adds up fast. We use smaller models where possible, implement smart caching, and batch operations to control costs.
• Prompt stability: Prompts that work today might fail tomorrow as models update. We version prompts, test extensively, and monitor output quality continuously.
• Context management: Most workflows need more context than fits in a single prompt. We've built systems to selectively include relevant information and maintain state across multi-turn interactions.

After building several systems, we've converged on some patterns that work well:

Agent orchestration layers that coordinate multiple specialized agents rather than one general-purpose agent. This makes systems more maintainable and testable.

Human-in-the-loop workflows for high-stakes decisions. AI handles 80-95% of cases automatically, but routes edge cases to humans with context and suggested actions.

Extensive logging and observability. We log every prompt, response, decision point, and error. This data is invaluable for debugging, improving prompts, and measuring system performance over time.

Traditional testing approaches don't work well for AI systems because outputs are non-deterministic. We use several strategies:

Golden dataset testing with example inputs and expected outputs (allowing for semantic similarity rather than exact matches).

Property-based testing that verifies outputs meet structural requirements even if content varies.

A/B testing in production with careful rollout and metrics monitoring.

Continuous evaluation where we sample production outputs and grade them (sometimes with another LLM, sometimes with human reviewers).

Not every problem needs AI. We've found AI agents work best when:

The task requires understanding unstructured data (documents, emails, free-text)

Perfect accuracy isn't required (or humans aren't perfect either)

The cost of mistakes is low or there's human oversight

Traditional rule-based systems would be too complex or brittle

For deterministic tasks, structured data processing, or cases where mistakes are unacceptable, traditional software is usually better.