Context Engineering 101: Best Practices to Build AI Agents

Prompt engineering has been a staple for everyone who has used generative AI tools. Prompt engineering is the process of writing and improving the right instructions, aka prompts, to guide an AI model to generate desired, accurate outputs. Many professionals have spent the last two years obsessing over prompts, and to be real with you, they still matter. However, 2025 is the year of AI agents and agentic AI, and we have seen many companies release new AI agents, targeted either as general-purpose or domain-specific.

AI agents are much more complicated and more capable than the generative AI tools we are used to. AI agents are autonomous systems that can reason and act (ReAct) and complete tasks with minimal to no human supervision. Hence, if you want to upgrade from generative AI tools to AI agents that can plan, use tools, and keep working for hours, you need to upgrade your skills from prompt engineering to context engineering.

What is Context Engineering?

If you haven't heard of context engineering before, you may ask what context engineering is. Let's break it down.

Context is a finite and precious resource, like instruction, data, and memory, that an AI agent is given to perform a task. As the amount of information (the context) grows, the AI model's ability to accurately recall specific details can degrade, a phenomenon known as "context rot." Hence, the future of AI development isn't just about writing better prompts, but about intelligently managing the flow of information an AI agent has access to at any given moment.

Context engineering is the process of systematically planning and managing the information, tools, and instructions that AI models receive to ensure they produce accurate, reliable, and domain-specific outputs.

Context engineering vs Prompt engineering

Now, if you look at prompt engineering against context engineering, you may have noticed context engineering is the natural evolution of prompt engineering. It is improving upon the limitations of prompt engineering.

While prompt engineering optimizes phrasing, context engineering optimizes the set of tokens the model receives, including everything beyond the prompt. Context engineering is iterative and continuous, where with every loop of an AI agent, you can decide what to preserve, what to retrieve, and what to drop before the next step. That means writing clearer system instructions, designing token-efficient tools, and choosing examples that teach behavior without stuffing edge cases.

Anthropic AI's Guide on Effective Context Engineering for AI Agents

Anthropic, a leader in AI research and development, has outlined several key strategies for effective context engineering that are important for anyone building with an AI agent today. These practices can help you create AI agents that are more focused, efficient, and reliable, especially when tackling long and complex problems.

Here are some of the best practices to build AI agents suggested by Anthropic AI:

• Write Clear and Balanced Prompts: System prompts need to hit a "Goldilocks zone," avoiding overly strict hardcoded logic that makes the system less reliable. At the same time, system prompts should not be too vague, which can leave the AI without a clear direction. The best approach is to provide specific guidance and heuristics without micromanaging every step.

• Designing Efficient Tools: For an AI agent, tools are its hands and eyes, allowing it to interact with its environment to get the work done. It's important that these tools are well-defined, don't have overlapping functions, and return information in a token-efficient manner. A bloated or ambiguous toolset can confuse your AI agent just as easily as it would a human engineer.
• "Just-in-Time" Context Retrieval: Instead of overwhelming the AI model by loading all potentially relevant data upfront, a more effective strategy is to let the AI agent dynamically pull in information as needed. This mirrors how humans work; we don't memorize an entire library before writing a research paper. Instead, we use bookmarks, file systems, and search queries to retrieve information on demand. This "just-in-time" approach keeps the AI agent's working memory clean and focused.
• Managing Long-Horizon Tasks: For complex projects that require an AI to work for extended periods (more than its limited context window), special techniques are needed. These include:
  • Compaction: This involves summarizing a long conversation or work history, filtering the most critical details, and starting a new session with that compressed summary. This preserves key decisions and unresolved issues while discarding unnecessary information.
  • Structured Note-Taking / Agentic Memory: This technique allows an AI agent to maintain an external "memory," like a digital notepad. The AI agent can write down key findings, to-do lists, or strategic notes, which can be pulled back into context when needed. This provides a persistent memory without cluttering the immediate context window.
  • Sub-Agent Architectures: For highly complex tasks, a single AI agent can delegate work to specialized sub-agents. Each sub-agent can perform a deep dive on a specific part of the problem and return a condensed summary to the main agent, which then focuses on synthesizing the results.

The bottom line

We are not disregarding or demeaning prompt engineering; it will always matter. However, the composition of context (tight, timely, and token-efficient) is what turns a clever model into a dependable coworker/ AI agent. The AI field is moving and growing fast, and newer models will need less hand-holding, but the constraint won't disappear: context is scarce. The teams that win will be those that treat context like a budget and spend every token where it counts.

In Conclusion:

If you're building an AI agent, start measuring success in how well tokens are being spent. Make your prompts more balanced, your tools surgical, your retrieval just-in-time, and your memory externalized. Use compaction to stay coherent, and break work into sub-agents when depth is required. The payoff will give you fewer wrong turns, faster convergence, and AI agents that feel less like demos and more like durable systems.