Context Engineering Inside the Harness: 4 Mechanisms That Beat Context Overflow and Goal Loss on Long-Horizon Tasks
An agent, in its simplest form, is an LLM calling tools in a loop. That loop works for short jobs. Give it a task that runs for an hour and 200 tool calls, and it breaks in 2 predictable ways. The AWS Samples design guide for autonomous cloud coding agents names them directly: shallow agents suffer from context overflow, get distracted (goal loss), and do not maintain state over long periods. The layer that fixes this is not the model. It is the harness, which AWS describes as managing everythi
An agent is a language model that calls tools in a loop, which works for short tasks but breaks predictably on long ones: the agent suffers from context overflow, loses track of its goal, and cannot maintain state. The harness is the software layer that manages everything except the model itself, and it uses four mechanisms to fix these problems: context budgeting and offloading decide what information never enters the model's context window, compaction summarizes conversations when the window fills up while trying to preserve the original goal, todo-state involves the agent maintaining and rewriting a task list to keep objectives in focus, and structured note-taking lets the agent write information to disk and reload it after context resets. These techniques exist because adding a larger context window helps less than expected, since each added token depletes a finite attention budget and the original instructions drift toward the middle of the window where recall degrades.
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