Context Engineering Lessons for Building AI Agents (zilliz.com)

AI agent builders are converging on a single hard truth: context management is the limiting factor for reliable, cost‑effective multi‑step agents. A recent industry debate (Anthropic vs Cognition) highlights the trade-off—multi‑agent collaboration reportedly yields big success gains (Anthropic cites ~90.2% higher success), while single agents with aggressive long‑context compression claim better stability and lower costs. The root problem is that LLM context windows are finite and expensive: excess tokens cause information overload, higher latency/cost, and accuracy drops. That makes context engineering (how you store, filter, compress and isolate information) the central design challenge for next‑generation agents. Practical patterns from LangChain, Lossfunk and Manus converge on complementary solutions. LangChain’s Write/Select/Compress/Isolate framework advocates scratchpads, relevance filtering, on‑demand summarization (auto‑compact), and modular sub‑agents. Lossfunk emphasizes task scoping (10–15 minute subtasks), whole‑file retrieval (benchmarks show ~95% vs ~80% for fragmented RAG), verification checkpoints, read/write tool access, and immutable contexts to leverage KV‑cache savings. Manus adds production tactics: KV‑cache‑friendly append‑only prefixes, tool masking instead of dynamic loading, using the file system as recoverable context, attention recitation (todo.md), and preserving failure traces for learning. Together these techniques reduce cost, improve reliability, and shift agent design toward hybrid memory + tool architectures that balance completeness, latency, and long‑term learnability.