Context Engineering Inside the Harness: 4 Mechanisms That Beat Context Overflow and Goal Loss on Long-Horizon Tasks

New research identifies four mechanical strategies used by systems like LangChain Deep Agents, Claude Code, and Amazon Bedrock AgentCore to manage long-horizon tasks. These methods prevent performance degradation by addressing context overflow and goal loss that typically occur when large language models execute extended tool-use loops. By stabilizing the harness layer, these frameworks allow agents to maintain operational consistency over complex, multi-step workflows.