Presentation: The Right 300 Tokens Beat 100k Noisy Ones: The Architecture of Context Engineering
Coding agents fail because of bloated context. Here's the architecture that fixes it.
Why it matters
Context engineering is becoming a critical reliability discipline for production agents. This presentation distills practical patterns (lazy-loaded skills, versioned artifacts, external memory, LLM-as-a-judge) that turn unreliable agentic workflows into dependable systems — a gap practitioners are hitting right now.
The key facts
12 to knowContext window bloat identified as a root cause of agent failures
Lazy-loaded skills pattern for context efficiency
Versioned context artifacts for reproducibility
Externalized memory banks for state management
LLM-as-a-judge evals for agentic workflow validation
Target audience: software architects and engineering leaders deploying agents
Focus: turning markdown-based prompts into reliable agentic systems
Core problem: bloated context windows and stuffed prompts cause agent failure
Solutions presented: lazy-loaded skills, versioned context artifacts, externalized memory banks
LLM-as-a-judge evals used for context quality validation
Focus: turning markdown workflows into reliable agentic systems
Source: InfoQ presentation (practitioner-focused conference talk)
Go to the source
InfoQ AI/MLinfoq.com
Publisher excerpt: Baruch Sadogursky and Patrick Debois discuss why coding agents fail due to bloated context windows and stuffed prompts. They explain practical context engineering fixes, including lazy-loaded skills, versioned context artifacts, externalized memory banks, and LLM-as-a-judge evals. Software…