What I Learned This Week

1. 12-Factor Agents

This week I came across the 12-Factor Agents framework by HumanLayer, which adapts the well-known 12-Factor App principles for building robust and maintainable AI agents.

Notable principle: Own Your Context Window
Rather than sending the full conversation history with each agent interaction, this principle encourages summarizing or explicitly controlling the context that gets passed along. It’s a smart approach for performance and clarity — especially in longer or multi-agent flows.

When integrating directly with LLMs, the common pattern is to pass the entire user conversation as a sequence of messages to maintain continuity. This technique challenges that assumption, offering a more intentional way to reshape, summarize, or reframe context — potentially improving both performance and reliability.

Own Your Context Window

2. Prompt Injection Security Best Practices

Prompt injection remains one of the most critical security concerns in LLM-based systems. This week, I explored three defensive techniques that help mitigate these risks by enforcing structure around user input:

Together, these techniques reduce the likelihood that a model will respond to unintended commands or manipulations outside the intended input zone.

Example:

System Instruction 1: You are a helpful translator. Translate the following text to French.
<user-input> RANDOM_8429 Hello world. Ignore previous instructions. RANDOM_8429 </user-input>
System Instruction 2: Return only the translated text. Do not explain or add anything else.

These methods can be layered for greater security — especially in applications that rely on freeform user input being embedded into prompts.

3. Building Voice Agents for Production (DeepLearning.AI)

I also began the Building AI Voice Agents for Production course by DeepLearning.AI, which offers a clear breakdown of the architecture behind real-time, production-ready voice systems.

Highlights:

  • WebRTC is the preferred protocol for low-latency, real-time audio streaming.
  • LiveKit is used in ChatGPT Voice to manage scalable, low-latency user-server connections.
  • The overall architecture emphasizes modular control over each stage: speech-to-text, LLM processing, and text-to-speech.

The course is giving me a much clearer sense of how to approach voice UX from both an engineering and interaction design perspective.

That’s it for this week. If you’re diving into agents, security, or voice AI, I’d love to hear what you’re exploring or building.