Building Agent-in-the-Loop Systems: Scaling AI Automation with Evals
victor_explore · x · 2026-07-20
The key to scaling AI automation lies not just in prompts, but in establishing a robust evaluation system (Evals).
The video explores how to upgrade systems from "human-in-the-loop" to "agent-in-the-loop". By introducing automated testing into AI workflows, models can self-correct and iterate overnight, similar to the automated research frameworks used by Andrej Karpathy and others.
More from coding & agent
- HeyGen adds a media-sourcing skill for coding agents with 75k images and 10k tracks — HeyGen · 2026-07-22
- Agent search bottlenecks are now about variance, not raw latency — rohanpaul_ai · 2026-07-22
- LangSmith adds tracing for Pipecat, LiveKit, OpenAI Realtime, and Gemini Live — LangChain · 2026-07-22
- An MCP server signs every AI agent tool call into a verifiable Merkle chain — Funky_Chicken_22 · 2026-07-22
- Annotated transcript of a Claude Code team interview is now available — trq212 · 2026-07-22
- Claude Code skill uses 10 Markdown rules to make outputs ADHD-friendly — alex_verem · 2026-07-22