The Future is Context Driven - Part 3

Finding reliable, efficient, and capable AI agents remains a challenging endeavor in 2025. As explored in Part 1 and Part 2, the key to overcoming these challenges lies in mastering context engineering. By thoughtfully managing the information we provide to LLMs and AI agents, we can unlock their full potential.

Context engineering is rapidly emerging as one of those standards for serious, production-grade AI systems. Just as React introduced a philosophy that now underpins most web apps, a context-centric philosophy is taking hold in AI. The idea that an agent must manage its context intelligently (and that we, as engineers, must design for that) will become a basic assumption in AI development.

As LLMs continue to advance, one might wonder: if models get smart enough or context windows get huge, does this context engineering stuff become obsolete? All signs point to no! If anything, it becomes even more crucial. Larger context windows alone won’t save us; experiments show that model performance actually degrades as you stuff more into the prompt without care. Unlimited context is not the same as unlimited understanding. Even if we had an “infinite” context window, we’d still face the challenge of deciding what information to include and how to structure it. That is exactly what context engineering is about. In fact, retrieval-augmented generation remains essential in the era of long-context models. Practitioners argue that even with million-token contexts, we will still need to retrieve and focus the most relevant information rather than dump everything in.

Looking ahead, the real differentiation in AI products and teams will not just be who has the most advanced model, but who best manages and leverages context. Many basic coding or chat agents may become commoditized as base capabilities. The true competitive edge will come from workflow transformations. New ways of organizing knowledge, context, and human oversight so that AI systems are reliable and efficient in solving real problems. Context engineering techniques like shared traces, intentional compaction, and strategic context isolation directly translate to lower error rates, lower costs, and higher success on complex tasks. Companies already report significant productivity boosts by adopting these methods.

In summary, context is king for LLMs and AI agents. Providing an AI with the right context is like giving a human the right briefing and tools, it unlocks their potential. Mastering context engineering is not just about tweaking prompts, but about architecting the entire information flow around the AI. It’s how we ensure AI agents truly understand what we want, stay on track, and produce results we can trust. The future of AI isn’t just bigger models or longer prompts, it’s smarter context. By embracing context-driven design now, we are future-proofing our AI systems for the increasingly ambitious challenges to come. With the right context, there’s no limit to what our AI agents can achieve.