The problem Multi-agent LLM systems -” several models exchanging messages within one...
The problem
Multi-agent LLM systems -” several models exchanging messages within
one session -” pay for context, not intelligence. Every round trip in
natural language or verbose JSON burns tokens re-stating structured
context that a fixed, external schema could carry in a fraction of
Every engineering team deploying production-grade LLM agents or RAG pipelines faces the exact same...
A 1.5B model running on your laptop will return JSON that almost parses. The closing brace is...
The problem A single LLM call is easy to reason about. You send a prompt, you get tokens...
Every team we talk to has a version of the same story. They built an LLM integration that works well...
The layer between 'the model usually returns the right shape' and 'the rest of my system can depend on it' — schemas, validation…
Error compounding, stale state, and context rot break multi-agent LLM pipelines. Learn the failure modes and design patterns that…
