When we are Context Engineering, where does the context come from?

The buzz around Context Engineering has reached a fever pitch in the AI community. Everyone's talking about it as if it's purely a technical challenge – optimize the prompts, fine-tune the models, engineer better retrieval systems. But here's the question that's been nagging at me: where exactly does this magical "context" actually come from?

If we're honest about it, context doesn't just materialize out of thin air. It's not something we can simply download from a repository or generate through clever algorithms. Context is fundamentally human-created, emerging from the messy, iterative process of people talking, debating, aligning, and ultimately agreeing on what matters.

The Human Origins of Context

Think about the last time you worked on a complex project. The context that guided your decisions didn't spring from a technical specification or a perfectly crafted prompt. It came from countless conversations where your team hammered out goals, wrestled with competing objectives, clarified confusing tasks, updated each other on status, and constantly re-negotiated priorities as circumstances changed.

This is where real context lives – in the dynamic interplay between humans who are trying to accomplish something together. When someone says "we need to prioritize user experience over speed to market," that's context being created. When a team decides that "good enough" trumps "perfect" for this particular sprint, that's context being established. When stakeholders agree that customer retention metrics matter more than acquisition numbers for the next quarter, context is being forged in real-time.

But here's the rub: most of this context creation happens in ephemeral conversations, quick Slack exchanges, hallway discussions, and meeting rooms where decisions get made but rarely documented. We create context organically, then wonder why our AI systems lack the nuanced understanding needed to truly help us.

The Documentation Dilemma

This brings us to a crucial realization: without systematic processes to capture and organize these points of alignment, we're essentially flying blind when it comes to context engineering. How can we engineer context into our AI activities if we haven't first documented what that context actually is?

This is where methodologies like Agile development become invaluable – not just for software teams, but for any group trying to maintain shared understanding while navigating complexity. Agile frameworks force teams to articulate their goals explicitly, break down objectives into manageable tasks, maintain visible status updates, and regularly reassess priorities.

User stories, sprint planning sessions, retrospectives, and backlog grooming aren't just project management rituals – they're context creation and documentation mechanisms. When a team writes "As a customer, I want to be able to easily find my order history so that I can track my purchases," they're not just describing a feature. They're encoding context about user needs, business priorities, and success criteria.

The daily standup isn't just a status meeting – it's a context synchronization ritual where everyone aligns on current reality and immediate priorities. Sprint reviews aren't just demos – they're opportunities to validate and refine the context that guides future work.

The Maintenance Challenge

But here's where things get tricky. Many teams resist these documentation practices precisely because they know how quickly context shifts and how burdensome it becomes to keep everything current. There's nothing more frustrating than spending hours updating project documentation only to have priorities change the next week.

This maintenance overhead creates a vicious cycle. Teams avoid documenting context because it's too much work to maintain. Without documented context, they struggle to onboard new team members, maintain consistency across workstreams, and provide meaningful input to AI systems. The lack of context then creates more confusion, leading to even more resistance to documentation.

It's like being too busy driving to stop for gas – eventually, you're going to run out of fuel.

AI as Context Curator

This is where the real opportunity lies, and it's been hiding in plain sight. Instead of viewing context documentation as a burden that humans must bear, what if we flipped the script? What if maintaining and evolving context documentation became one of the primary jobs we assign to AI?

Think about it: AI systems are naturally suited for tasks that require consistency, attention to detail, and the ability to process and synthesize information from multiple sources. They don't get tired of updating documentation. They don't forget to capture decisions made in meetings. They don't get frustrated by the iterative nature of context refinement.

An AI assistant could sit in on sprint planning sessions and automatically update user stories based on the discussion. It could monitor Slack channels for decisions that affect project priorities and suggest updates to the backlog. It could notice when status updates indicate that assumptions have changed and prompt the team to revisit their documented context.

More importantly, AI can help teams maintain the living, breathing nature of context without the administrative burden. Instead of context documentation being a snapshot that quickly becomes stale, it could become a dynamic resource that evolves continuously as the team's understanding deepens and circumstances change.

The Symbiotic Relationship

This creates a beautiful symbiotic relationship. Humans focus on what they do best – having nuanced conversations, making judgment calls, and creating meaning through collaboration. AI handles what it does best – capturing, organizing, synthesizing, and maintaining the outputs of those human interactions.

The context that emerges from this partnership becomes far richer than what either humans or AI could create alone. Humans provide the wisdom, creativity, and judgment that gives context its meaning. AI provides the consistency, thoroughness, and persistence that keeps context useful and current.

Building Better Context Engineering

When we approach context engineering from this perspective, the technical challenges become much more manageable. Instead of trying to engineer context from scratch, we're engineering systems that can capture, process, and apply context that humans are already creating.

The prompts become more effective because they're grounded in documented team agreements. The retrieval systems find more relevant information because the context they're searching through has been systematically organized. The AI outputs align better with human intentions because the context reflects actual human priorities rather than assumed ones.

The Path Forward

The future of context engineering isn't about building more sophisticated technical systems in isolation. It's about creating better partnerships between human context creators and AI context curators. It's about recognizing that the most valuable context comes from human collaboration, then using AI to help us capture and leverage that context more effectively.

Teams that embrace this approach – using structured processes to create context and AI to maintain it – will find themselves with a significant advantage. They'll have richer, more current context to feed their AI systems. They'll have better organizational memory and more effective knowledge transfer. Most importantly, they'll have created a sustainable cycle where better context leads to better AI assistance, which leads to more capacity for the human work that creates even better context.

The question isn't where context comes from – it comes from us. The question is how we'll use AI to help us do that uniquely human work even better.