How to Perform Effective Agentic Context Engineering

Agentic context engineering involves optimizing all data fed into an AI agent to maximize performance on complex, multi-step tasks. This practice is crucial because an agent's context can become bloated with irrelevant information over time, which degrades performance. Key techniques include few-shot learning, using structured prompts with XML or markdown, and prompting the model for step-by-step reasoning. Additionally, the process involves actively shortening the context, for example by using a secondary LLM to summarize or cut out entire irrelevant sections like previous tool calls or documents.