Context Engineering Inside the Harness: 4 Mechanisms That Beat Context Overflow and Goal Loss on Long-Horizon Tasks
AITopTools Editorial TeamSeptember 13, 2026
What changed
The article explains how the software layer around AI agents can reduce two common problems on lengthy, multi-step tasks: running out of conversation space and losing sight of the original objective. It examines approaches used by LangChain Deep Agents, Claude Code, Manus, OpenAI Codex, and Amazon Bedrock AgentCore, and includes a simulator showing how a 200,000-unit context limit fills up.
What this means for you
People building or using tool-connected AI agents can use these ideas to make longer workflows more reliable. The piece is mainly a technical guide, and the feed does not indicate a new product release or change in availability.