In a recent insightful article, the developers of the AI agent Manus shared their core philosophy: "Context Engineering is All You Need." [1] This statement marks a significant departure from the common pursuit of building specialized models from scratch. Instead, the Manus team argues for a more agile and ultimately more powerful approach: meticulously crafting the context provided to general-purpose large language models (LLMs). This analysis delves into the key lessons from their experience, offering a critical perspective on this emerging discipline.
The central argument presented is that the art and science of structuring the input for an LLM—or "context engineering"—can yield more significant and rapid improvements than the long and expensive process of model training. The Manus team's experience suggests that by focusing on the "prompt," one can "leverage the multi-billion dollar investment that OpenAI and others have poured into their models." [1]
This is a compelling proposition. It democratizes AI agent development, shifting the focus from resource-intensive model training to the more accessible and iterative process of prompt and context refinement. It implies that the key to unlocking an agent's potential lies not in creating a new brain, but in providing the existing one with the right information in the right way.
The article outlines several practical strategies that have been instrumental in the development of Manus. Let's explore them in detail.
The Manus team places a heavy emphasis on optimizing for the KV-cache, a mechanism in transformer models that stores intermediate calculations. A high cache hit rate means faster and cheaper operations. They state, "the KV-cache hit rate is the most important metric for agent performance." [1] To achieve this, they advocate for:
This focus on the KV-cache is a masterstroke of pragmatism. It acknowledges the economic realities of working with powerful LLMs and turns a technical constraint into a source of competitive advantage.
When an agent like Manus has a large suite of tools, deciding which ones to present to the model can be a challenge. The conventional approach might be to dynamically remove unavailable tools from the context. However, the article points out a critical flaw: this "breaks the KV-cache and confuses the model." [1]
Their solution is "masking," where unavailable tools are not removed but are instead made less likely to be chosen by manipulating the token logits. This is a more elegant and efficient way to guide the agent, nudging it towards the correct action without disrupting the context's integrity.
LLMs have a finite context window, a fundamental limitation for complex tasks. The Manus team's solution is to treat the file system as an external, persistent memory. An agent can save large pieces of information to a file and refer to it by its path, effectively giving it an "infinite context window." [1]
This is a simple yet profound shift in perspective. It moves beyond the limitations of the model and embraces the broader computational environment, a crucial step towards building truly capable AI agents.
Long-running tasks can cause an agent to "lose the plot." To combat this, the Manus team employs a technique they call "recitation." The agent maintains a todo.md file, which it periodically reviews and updates. This, they argue, "keeps the overall plan in the model's recent attention span." [1]
This is a fascinating example of how to manage the model's attention, a resource as valuable as the context window itself. It's a practical solution to a common problem in agentic systems.
Perhaps the most counter-intuitive but insightful lesson is to leave errors in the context. The article argues that "when the model sees that it performed an action and that it failed, it learns not to do it again." [1] This is a crucial element of what makes an agent truly "agentic"—the ability to learn from its mistakes.
This approach contrasts sharply with the tendency to hide errors and present a sanitized view to the model. By exposing the model to its own failures, the Manus team is fostering a more robust and adaptive learning process.
The Manus team's focus on context engineering is a significant contribution to the field of AI agent development. It offers a practical, efficient, and accessible alternative to the resource-intensive process of model training. Their strategies are not just theoretical but are grounded in the practical experience of building a real-world AI agent.
However, it's important to note that this approach is not a silver bullet. The effectiveness of context engineering is still ultimately dependent on the capabilities of the underlying LLM. As models evolve, the specific techniques of context engineering will need to adapt as well.
Furthermore, while the article provides a compelling vision, the successful implementation of these strategies requires a deep understanding of both the LLM's architecture and the specific domain in which the agent is operating. It is, in itself, a complex engineering challenge.
The development of Manus provides a powerful case study in the importance of context engineering. By shifting the focus from models to context, the Manus team has forged a path that is both pragmatic and innovative. Their lessons learned are a valuable resource for anyone looking to build the next generation of AI agents. As they aptly put it, in the world of AI agents, "Context is King."
Source: [1] Context Engineering for AI Agents: Lessons from Building Manus (https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus)