Anthropic Shows us How to Master Context Engineering to Build Smarter AI Agents

AI agents have become increasingly sophisticated, shifting the focus from simple prompt engineering to the broader, more nuanced discipline of context engineering. The central challenge now lies in managing the limited set of information, tokens, that directly influence an agent’s performance. Given the constraints imposed by large language models’ finite attention windows, mastering context engineering is essential to developing reliable, steerable AI agents.

From Prompts to Context: A New Engineering Paradigm

While prompt engineering revolves around crafting precise instructions for single-turn tasks, multi-step agent workflows require a more holistic approach. Context engineering encompasses orchestrating all the elements presented to the model including prompts, tools, conversational history, and external data while ensuring only the most relevant information fills the model’s limited context window. This process is iterative, demanding ongoing refinement and thoughtful selection as agents progress through tasks.

Why Context Management Matters

Just like humans, LLMs can lose focus when overwhelmed with information. As more tokens are packed into the context window, the model’s ability to recall and reason about specific details weakens, a phenomenon known as context rot. 

With each addition, the model’s finite attention budget gets depleted. The transformer architecture at the core of LLMs further intensifies these challenges, making it critical to maximize the value of each token included.

Principles for Crafting Effective Context

• System Prompts: Aim for clarity and appropriate specificity. Avoid overly rigid instructions or vague directions. Use structured formatting (like XML or Markdown) to organize content, but keep it concise and targeted.
  
  
• Tools: Provide a minimal, well-defined set of tools. Each should have a clear purpose and be efficient, avoiding overlap or ambiguity that could confuse the agent.
  
  
• Examples: Select diverse, canonical examples to illustrate desired behaviors. Focus on quality over quantity, well-chosen examples are more effective than exhaustive rule listings.
  
  
• Message History: Be selective with which past messages are retained in context. Prune redundant or irrelevant information to keep the agent’s focus sharp.

Dynamic Context Retrieval and Agent Autonomy

Modern agentic systems often leverage just-in-time strategies, where agents fetch relevant data during runtime using lightweight identifiers and specialized tools. This approach mirrors human habits like consulting notes or bookmarks, enabling agents to build their context incrementally. 

Although this may introduce slight latency, it helps prevent information overload and keeps working memory focused. Hybrid approaches, combining up-front context with runtime retrieval, can further optimize agent performance, especially for tasks requiring both speed and depth.

Techniques for Long-Horizon Tasks

• Compaction: Summarize and compress key information as the context window fills up, preserving coherence over extended interactions while discarding low-value data.
  
  
• Structured Note-Taking: Persist essential notes outside the context window and reintroduce them when needed. This strategy enables continuity across complex, multi-step undertakings.
  
  
• Sub-Agent Architectures: Delegate specific tasks to sub-agents with fresh context windows, then have the main agent synthesize their outputs for high-level orchestration without losing focus.

The ideal technique depends on the use case: compaction is excellent for ongoing conversations, note-taking suits iterative projects, and sub-agent systems are powerful for parallel research or analysis.

Treat Context as a Precious Resource

Context engineering is redefining best practices for working with LLMs. As models advance, the goal is to curate the smallest, most relevant set of information to maximize agent performance. 

Whether via compaction, tool optimization, or dynamic retrieval, maintaining a high signal-to-noise ratio within the model’s limited attention span remains paramount. Even as smarter models emerge, the art of managing context will remain a cornerstone of building trustworthy, effective AI agents.

Source: Anthropic Applied AI Team, “Effective context engineering for AI agents,” published September 29, 2025, Anthropic Engineering Blog.