Context Engineering - Unlocking Agentic AI’s True Potential
The art and science of giving the right information to LLMs. As AI systems become more complex and sophisticated, prompt engg is not enough to convey the complexities of designing modern AI Apps.
What is context engineering and how does it differ from Prompt Engineering
Context engineering is the practice of designing systems that give AI models the right information and tools they need to do their tasks effectively.
Context is much more than just your prompt. Context is everything the model has access to respond to your request. Prompt engineering focused on asking the right question or providing detailed instructions to the AI model. Prompt engineering emerged as a popular term when LLMs were in their infancy. They had access to a limited context window of a few thousand tokens and tools, memory, agents and MCP / A2A were not on the horizon.
Today's LLMs are far more complex with context size of millions of tokens and the ability for calling external systems, tools, and even agentic orchestration with multi-agent AI systems. Context has therefore evolved beyond the prompt to include System Prompt, User Input/Prompt, Memory, Retrieved Information (RAG etc.), information on tools (MCP), responses from tools, and structured output format specifications.
Context engineering matters because most failures in real-world implementations happen not because of limitations in the model's capability, but because the model does not have the correct information to work with.
Common issues:
Context poisoning - When wrong information gets included.
Context distraction - When too much irrelevant information overwhelms the model.
Context confusion - When conflicting information leads to poor decisions.
In a recent article I wrote about context bloating. I am linking to that here for reference.
The key to successful AI implementations is fundamentally context engineering. Rather than looking to get to a perfect prompt, context engineering forces you to think about designing these systems in terms of data strategy, information structure, and experience in ways that prompt engineering never attempted to convey.
Four context engineering strategies
Writing Context: This involves saving information outside the context window to help agents perform tasks effectively. This may be in the form of note taking scratch pads or memory systems. Planning activity which is very common in agentic research is an example where a planing agent creates a plan in a scratch pad for later use by a sub agent which may execute parts of the sub tasks.
Selecting context: This is critical as you need the right information relevant for the task at hand. Using techniques like knowledge graphs and semantic search, you will be able to select only the relevant context and provide it to the agent executing a specific activity.
Compressing context: This involves retaining only the necessary tokens to perform the task at hand effectively. Techniques used include context summarization, selective summarization etc.
Isolating context: Isolating context becomes important in multi agent systems to maintain efficiency and to prevent confusion. Sub-agents will operate with isolated context windows and including the tools and environments available using sandboxed containers.
The relevance of context engineering for agentic AI in enterprises.
Enterprises are keen to embrace Agentic AI systems in 2025. Context engineering is going to be the key for successful implementations given the models are sufficiently intelligent for most business problems at hand.
This new wave of Agentic AI is a huge leap in complexity from what we had before. We're shifting from AI assistants that just react to our questions to proactive systems that can sense, proactively take action, and manage entire business processes on their own. Think about an AI that can plan and run marketing campaigns or autonomously handle your supply chain. The catch is that, for an AI to make decisions in these scenarios, it needs to understand the right context. If it's missing the proper business context, it will be a GIGO (garbage-in garbage-out) scenario. An autonomous agent making critical decisions with bad information is way more serious than a chatbot giving a weird answer. A lot of thoughtful design is needed to pull the right into from sales data, market news, internal reports, and compliance rules, and piece it all together. This means companies have to completely rethink how their information is organized as a knowledge fabric, so the AI can actually understand and use it effectively.
Ultimately, getting this context right is how businesses will win with AI. As we move into a future where different AI agents will need to work and communicate with each other, having this solid foundation of context backed by enterprise knowledge will be even more critical. Organizations that recognize this shift and invest in context engineering capabilities will be able to build AI systems that truly understand their business context, leading to business outcomes like faster decision-making and reduced costs that traditional AI implementations cannot deliver.