What is context pollution in a long-running agent and what mitigation strategies are available?

Over a long run, an agent keeps adding to its **context** every single step. The user goal, every reasoning trace, every tool call, every tool result, and every error all pile up in one growing transcript. That is the agent's working memory. Context pollution is what happens when that transcript fills with junk. Failed tool calls, stale results that are no longer true, eight identical retries, and verbose dumps all stay in the window. The useful signal gets buried under noise. This hurts the model in two ways. First, long contexts suffer from a lost-in-the-middle effect, where the model pays most attention to the start and end and skims the middle. Second, contradictory or outdated entries can actively mislead the next step, so the agent repeats a mistake it already made. The fixes all share one idea: stop blindly appending. You can summarise old turns into a short note, compact away dead ends, keep a clean scratchpad of facts that matter, hand subtasks to a fresh sub-agent, or retrieve only the relevant slices instead of replaying the whole history.

**Context pollution** is the gradual buildup of low-value content in a long-running agent's context window. Failed tool calls, stale observations, retry spam, and verbose dumps accumulate until noise drowns the signal. It degrades reasoning two ways. Long contexts trigger lost-in-the-middle, where the model under-weights tokens in the middle of the window. And contradictory or outdated entries mislead the next decision, so the agent loops on a mistake it already made. Mitigations trade fidelity against cost. Rolling summarisation compresses old turns into a short recap. It is cheap on tokens but lossy, since a summary can drop the one detail the next step needed. Context compaction drops dead ends and collapses verbose tool output to keep raw signal. A structured scratchpad or memory store holds durable facts outside the transcript, so they survive truncation. Sub-agent isolation hands a subtask to a fresh agent with a clean context and returns only the result, keeping the parent uncluttered. The unifying principle is to retrieve over the full history rather than replay it. Treat context as a curated cache, not an append-only log.