Context Engineering for AI Agents: 4 Patterns That Replace Prompt Hacking

Your AI agent works on the first call. By turn 20, it forgets your name. That is not a prompt engineering problem. That is a context engineering problem. Prompt engineering optimizes how you ask . Context engineering optimizes what information surrounds the ask — the schemas, memory, tool definitions, and retrieval architecture that determine whether your agent succeeds or fails at complex tasks. Anthropic defines context engineering as "the set of strategies for curating and maintaining the optimal set of tokens during LLM inference." The shift matters because autonomous agents persist across multiple interactions, make sequential decisions, and operate with varying levels of human oversight. A well-crafted prompt means nothing if the context window is full of irrelevant conversation history. Here are 4 patterns that move your agents from prompt hacking to systematic context management — with working Python code for each. Pattern 1: Message Trimming With Token Budgets The simplest con