In a sprint for artificial intelligence supremacy, the spotlight often shines on the titans building ever-larger models. But a recent blog post from the developers of the AI agent Manus suggests a crucial, overlooked discipline that could define the next wave of successful AI products: Context Engineering. This emerging science is less about the raw power of the AI model and more about the art of conversing with it.
For venture capitalists, aspiring entrepreneurs, and AI startups, this piece offers a new lens through which to view the landscape. It argues that the secret sauce isn't just having the best model, but knowing how to instruct it, guide it, and learn from its failures.
Demystifying Context Engineering
At its core, Context Engineering is the practice of meticulously structuring the information, or "context," that is fed to an AI agent to guide its behavior. The team behind Manus made a pivotal decision to focus on this approach rather than training their own custom models from the ground up. This allowed them to iterate and improve their product in hours instead of weeks, treating the underlying AI model as a powerful but interchangeable engine. As the author puts it, "If model progress is the rising tide, we want Manus to be the boat, not the pillar stuck to the seabed."
This process involves several clever techniques, honed through what the Manus team humorously calls "Stochastic Graduate Descent"—a manual, experimental process of finding what works.
One of the most critical concepts is designing around the KV-cache. Think of the KV-cache as a form of short-term memory for the AI. If the beginning of a command or conversation is identical to one it has seen before, the model can use cached results, drastically improving speed and cutting costs. The Manus team notes that cached inputs can be up to 10 times cheaper than uncached ones, making KV-cache hit rate a vital business metric. This means avoiding simple mistakes like putting a timestamp at the beginning of a prompt, as even a one-character difference can wipe out the cache.
Other key strategies from the Manus playbook include:
- Using the File System as Memory: Modern AI models have large, but not infinite, memory (context windows). To overcome this, Manus is taught to use the computer's file system like an external hard drive. It can write information to files and read it back later, creating a persistent, unlimited memory that it controls itself.
- Learning from Failure: A common impulse is to hide errors or failed attempts from the AI to avoid confusion. Manus does the opposite, intentionally keeping the wrong turns in the context. Seeing a failed action and the resulting error helps the model learn and makes it less likely to repeat the same mistake. The author argues that this ability to recover from errors is a hallmark of true agentic behavior.
- Manipulating Attention with a To-Do List: To prevent the AI from getting "lost-in-the-middle" of a complex, multi-step task, Manus is designed to create and constantly update a todo.md file. By "reciting" its goals and checking off completed items, it pushes the main objective into its recent attention span, ensuring it stays on track.
What This Means for the AI Industry
The principles of Context Engineering have profound implications for everyone in the AI space.
For Venture Capitalists:
Evaluating an AI startup should now go beyond asking "What model are you using?". VCs should probe deeper into a company's technical moat by asking about its context engineering strategies. What are their KV-cache hit rates? How does their agent handle errors? A startup with a brilliant approach to context engineering could easily outmaneuver a competitor that is simply waiting for the next, more powerful base model.
For AI Startups:
The barrier to entry may be lower than it appears. Startups don't necessarily need to compete with the giants in building foundation models. Instead, they can innovate on top of existing ones, creating value through clever and efficient implementation. The lessons from Manus show that a deep understanding of context can lead to a more responsive, reliable, and cost-effective product, which is a significant competitive advantage. This is where the real product differentiation will happen.
For Budding Entrepreneurs:
This is a call to action. You don't need a team of hundreds of PhDs to build a groundbreaking AI product. Context Engineering is a field ripe for innovation, where creativity and a deep understanding of the user's needs can lead to breakthroughs. The Manus blog post is a veritable playbook of strategies that can be adapted and built upon. The opportunity lies not in building a bigger brain, but in getting better at talking to it.
The Future is Engineered
As models become increasingly powerful and commoditized, the ability to effectively harness them will become the key differentiator. Context Engineering is an essential discipline for anyone building in the AI space. It is a reminder that in the quest for artificial intelligence, how we shape the context is just as important as the intelligence itself. The future of AI agents is being built one carefully engineered context at a time.
