As artificial intelligence agents grow more capable, a new discipline called context engineering is redefining how we wield the power of large language models (LLMs). Rather than simply crafting clever prompts, today’s innovators are learning to strategically curate the information fed to these models. In a world where context is both crucial and constrained, mastering this art is essential for building AI agents that are reliable, steerable, and consistently effective.
Beyond Prompt Engineering: The Rise of Context Management
Early efforts in AI centered on prompt engineering, where developers fine-tuned their instructions to elicit the best responses. However, as agents began handling complex, multi-step tasks and managing longer conversations, it became clear that overseeing the entire context including prompts, tool outputs, message history, and external data was even more important. Context engineering is all about selecting and preserving the most relevant pieces of information within the LLM’s limited context window, keeping the agent attentive and on track.
The Challenge: Working With Limited Attention
Just as humans can lose focus when overwhelmed, LLMs have a finite attention budget. Flooding the context window with too much data can lead to context rot, where the model’s reasoning and recall begin to falter. The architecture of LLMs requires every token to relate to every other, so a crowded context can dilute performance and introduce confusion. As such, effective context curation is vital as more information isn’t always better.
Key Elements of Effective Context
Great context engineering is about pinpointing the smallest, highest-signal set of tokens that drive the desired agent behavior. Here’s what works:
- System Prompts: Use concise, direct instructions at the right level of detail. Organize with sections and tags, but only include what’s truly necessary.
- Tools: Favor efficient, unambiguous tools over bulky ones. Limit functionalities to what matters, and design input parameters for clarity and precision.
- Examples: Choose a handful of diverse, representative examples rather than exhaustive lists of edge cases.
Across all areas, the mantra is to keep the context tight yet rich with meaning.
Dynamic Retrieval: Just-in-Time Context
Modern agents are moving toward dynamic, just-in-time context retrieval. Instead of front-loading every possible detail, agents store references like file paths or queries and fetch information as needed.
This mirrors how humans use calendars or notes to supplement memory. Through progressive disclosure, agents can surface details gradually, maintaining focus while accessing deeper context on demand. Balancing this flexibility requires well-designed tools and heuristics to avoid wasted effort or unnecessary context bloat.
Hybrid approaches are on the rise, where agents pre-load essential data and then retrieve more details as needed. Systems like Claude Code exemplify this method, adjusting the mix based on the task’s complexity and urgency.
Advanced Techniques for Long-Horizon Tasks
When tasks exceed the LLM’s context window, specialized strategies come into play:
- Compaction: Summarize and compress conversation history before reaching context limits, keeping only crucial decisions and discarding excess detail.
- Structured Note-Taking: Maintain persistent notes or to-do lists outside the immediate context, reintroducing them as needed to support multi-step projects.
- Sub-Agent Architectures: Deploy specialized sub-agents for focused subtasks, then have the main agent synthesize their summarized outputs to keep the primary context manageable.
The right method depends on whether the task is a flowing conversation, an iterative project, or an in-depth research endeavor.
Takeaway: Value Context as a Scarce Resource
Context engineering is rapidly becoming the cornerstone of advanced AI development. As models grow in power, the key challenge is no longer just prompt design, but thoughtful curation of context. Whether through smarter tools, data compaction, or agentic search, the guiding principle is simple: maximize outcomes by minimizing unnecessary context. This focus will be essential as AI agents evolve toward greater autonomy and sophistication.
Source: Anthropic Applied AI Team, “Effective context engineering for AI agents,” September 2025. Read the original blog.
Unlocking AI Agent Potential: Mastering Context Engineering