#048 Why Your AI Agents Need Permission to Act, Not Just Read
How AI Is Built
Navigating AI Writing Challenges
This chapter explores the complexities of applying retrieval-augmented generation (RAG) techniques in AI writing, focusing on maintaining stylistic coherence amidst diverse content. It addresses the tension between creativity and stylistic direction, highlights the importance of context and metadata in AI decision-making, and discusses user experiences with writing tools. Additionally, the chapter emphasizes the necessity of effective feedback mechanisms and the desire for high-quality language models to enhance AI performance.
Nicolay here,
most AI conversations obsess over capabilities. This one focuses on constraints - the right ones that make AI actually useful rather than just impressive demos.
Today I have the chance to talk to Dexter Horthy, who recently put out a long piece called the “12-factor agents”.
It’s like the 10 commandments, but for building agents.
One of it is “Contact human with tool calls”: the LLM can call humans for high-stakes decisions or “writes”.
The key insight is brutally simple. AI can get to 90% accuracy on most tasks - good enough for spam-like activities but disastrous for anything that requires trust. The solution isn't to wait for models to get smarter; it's to add a human approval layer for critical actions.
Imagine you are writing to a database or sending an email. Each “write” has to be approved by a human. So you post the email in a Slack channel and in most cases, your sales people will approve. In the 10%, it’s stopped in its tracks and the human can take over. You stop the slop and get good training data in the mean time.
Dexter’s company is building exactly this: an approval mechanism that lets AI agents send requests to humans before executing.
In the podcast, we also touch on a bunch of other things:
- MCP and that they are (atm) just a thin client
- Are we training LLMs toward mediocrity?
- What infrastructure do we need for human in the loop (e.g. DBOS)?
- and more
💡 Core Concepts
- Context Engineering: Crafting the information representation for LLMs - selecting optimal data structures, metadata, and formats to ensure models receive precisely what they need to perform effectively.
- Token Bloat Prevention: Ruthlessly eliminating irrelevant information from context windows to maintain agent focus during complex tasks, preventing the pattern of repeating failed approaches.
- Human-in-the-loop Approval Flows: Achieving 99% reliability through a "90% AI + 10% human oversight" framework where agents analyze data and suggest actions but request explicit permission before execution.
- Rubric Engineering: Systematically evaluating AI outputs through dimension-specific scoring criteria to provide precise feedback and identify exceptional results, helping escape the trap of models converging toward mediocrity.
📶 Connect with Dexter:
📶 Connect with Nicolay:
- X / Twitter
- Bluesky
- Website
- My Agency Aisbach (for ai implementations / strategy)
⏱️ Important Moments
- MCP Servers as Clients: [03:07] Dexter explains why what many call "MCP servers" actually function more like clients when examining the underlying code.
- Authentication Challenges: [04:45] The discussion shifts to how authentication should be handled in MCP implementations and whether it belongs in the protocol.
- Asynchronous Agent Execution: [08:18] Exploring how to handle agents that need to pause for human input without wasting tokens on continuous polling.
- Token Bloat Prevention: [14:41] Strategies for keeping context windows focused and efficient, moving beyond standard chat formats.
- Context Engineering: [29:06] The concept that everything in AI agent development ultimately comes down to effective context engineering.
- Fine-tuning vs. RAG for Writing Style: [20:05] Contrasting personal writing style fine-tuning versus context window examples.
- Generating Options vs. Deterministic Outputs: [19:44] The unexplored potential of having AI generate diverse creative options for human selection.
- The "Mediocrity Convergence" Question: [37:11] The philosophical concern that popular LLMs may inevitably trend toward average quality.
- Data Labeling Interfaces: [35:25] Discussion about the need for better, lower-friction interfaces to collect human feedback on AI outputs.
- Human-in-the-loop Approval Flows: [42:46] The core approach of HumanLayer, allowing agents to ask permission before taking action.
🛠️ Tools & Tech Mentioned
📚 Recommended Resources
🔮 What's Next
Next week, we will continue going more into getting generative AI into production talking to Vibhav from BAML.
💬 Join The Conversation
Follow How AI Is Built on YouTube, Bluesky, or Spotify.
If you have any suggestions for future guests, feel free to leave it in the comments or write me (Nicolay) directly on LinkedIn, X, or Bluesky. Or at nicolay.gerold@gmail.com.
I will be opening a Discord soon to get you guys more involved in the episodes! Stay tuned for that.
♻️ I am trying to build the new platform for engineers to share their experience that they have earned after building and deploying stuff into production. I am trying to produce the best content possible - informative, actionable, and engaging. I'm asking for two things: hit subscribe now to show me what content you like (so I can do more of it), and if this episode helped you, pay it forward by sharing with one engineer who's facing similar challenges. That's the agreement - I deliver practical value, you help grow this resource for everyone. ♻️
Remember Everything You Learn from Podcasts
