From Proof-of-Concept to Production

1. From Proof-of-Concept to Production

So you've built an AI agent - maybe a chatbot, content assistant, or internal tool. It works well in development. But is it ready for production? In this video, we'll walk through how to test, safeguard, and deploy your AI application responsibly.

2. The bigger picture

Recall that, in most AI applications, we're not deploying a model from scratch. We're deploying an application that connects to a hosted model via an API - like GPT from OpenAI or Claude from Anthropic. That means our responsibility lies in everything around the model, such as the interface, system prompts, and tool integrations.

3. Step 1: Validate Real Interactions

The aerospace engineer Burt Rutan once said, "Testing leads to failure, and failure leads to understanding." This statement is as true for agentic systems as rockets. Only through a robust testing framework can we really be confident that our system is ready for production, and that will likely mean a few failures along the way.

4. Step 1: Validate Real Interactions

Before thinking about deployment, validate how your app handles real-world inputs. What happens when a user types nonsense, slang, or insults? Can your agent recover when the user changes topics suddenly, or types in all caps? Nothing beats real-world data for validating your applications, but if you don't have such a dataset, try simulating the most robust dataset you can. To make your app production-grade, testing is required across multiple dimensions, not just the input and output.

5. Step 2: Test Everything: Unit tests

Start with unit tests to check key application logic like prompt formatting and tool connections.

6. Step 2: Test Everything: Integration tests

Then move to integration tests to test the full user flow end-to-end. You're not just checking if it works - you're checking if everything works together.

7. Step 2: Test Everything: Evaluate system prompts

Next, evaluate the quality of your system prompt. Use high-quality golden datasets - fixed inputs with expected outputs - to measure consistency while you make changes and improvements.

8. Step 2: Test Everything: Subjective evaluations

For subjective evaluations like clarity, helpfulness, and safety, you can use either human raters or even

9. Step 2: Test Everything: Subjective evaluations

an LLM, utilizing the LLM-as-a-judge approach. Finally, simulate latency and costs for both typical and extreme usage.

10. Step 3: Guardrails and Observability

Adding safety layers like content filters to limit what can be inputted and generated are also a good idea before shipping the app into production. Create fallback responses for when connections to the model or tools fail, and add reasonable token limits and API usage caps to mitigate misuse and keep costs under control.

11. Step 3: Guardrails and Observability

Logging is also essential for observability. Log what you can, particularly user interactions, but make sure you're maximally transparent to your users about the data you collect. Try to offer an ability for users to choose the amount of data you collect from them, so privacy-conscious users aren't put off.

12. Step 4: Shadow Deployments

Before exposing your app to users, run it in shadow mode. This processes real user inputs, but outputs are just logged for review rather than being sent to users. This lets you identify bugs or hallucinations without risk.

13. Step 5: Deployment Strategies

When you're ready to go live, you need to select a smart rollout plan. A/B test your AI features against a control version.

14. Step 5: Deployment Strategies

Roll out gradually by geography or user group if you can.

15. Step 5: Deployment Strategies

In high-risk settings - like finance or healthcare - keep a human-in-the-loop to supervise interactions.

16. Final Checklist

Let's summarize with a pre-launch checklist. Your app is proven to handle messy inputs, safely. You've got logs, filters, and testing in place. And you're deploying gradually - with eyes on quality and impact. Deploying AI isn't just about sending a prompt to an API - it's about building reliable, safe, and observable software applications.

17. Let's practice!

Let's apply what you've learned about getting AI Agents into production with some exercises!

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