Context Management

Systematically manages the informational payload (context window and memory) for Large Language Models (LLMs) to ensure conversational coherence, cost efficiency, and complex, multi-step agentic reasoning.

Context Management is the essential discipline for operationalizing stateful AI, specifically for Large Language Models (LLMs) that are inherently stateless. It systematically orchestrates the informational payload—the 'context window'—to overcome token limits (e.g., 4,096 for GPT-3.5) and prevent the model from 'forgetting' critical details. Key mechanisms include 'sliding window' techniques, summarization (abstractive or extractive) to condense long conversational history, and dedicated memory modules (short-term and long-term) for personalization and persistent knowledge. Formalized approaches, such as the Model Context Protocol (MCP), standardize how AI agents securely access and utilize external data sources like content repositories and business tools, ensuring reliable, multi-step agentic reasoning.

https://www.anthropic.com/news/model-context-protocol