What This Is
Over the past year, most stories about enterprises deploying AI Agents have shared the same ending: the model runs fine in testing, then derails the moment it hits real business scenarios. The reason usually isn't that the model lacks intelligence — it's that it can't "see" what it needs to see when making decisions.
This pattern has recently been captured by a new term: Context Engineering. It refers to all the information an AI can actually "see" on each call — including conversation history, behavioral rules written by developers, and descriptions of available tools.
Why does it matter? It punctures an illusion. OpenAI researcher Weng Jiayi (翁家翌) is blunt: "Just like people, what matters most for a model is context." Picture an Agent as a genius engineer parachuted into your team — extraordinarily capable, but knowing nothing about your product architecture, business rules, or past decisions. No matter how smart, they can't deliver. That is exactly today's AI Agent predicament.
More importantly, this judgment reframes the bottleneck of AI deployment from a "technology problem" into an "organizational problem": if a team's critical knowledge is tacit and scattered in veteran employees' heads, even the best Agent has nothing to work with.
Industry View
Optimists see this as precisely the trigger for enterprise upgrades. The Linux kernel project has been maintained for over thirty years, with efficient global developer collaboration, precisely because of a highly transparent, documentation-driven culture — these kinds of teams are naturally AI-friendly. In other words, building an AI-native team starts with a documentation movement.
But we must flag the other side. Over-emphasizing documentation may not pay off for small and medium companies: writing and maintaining documentation is itself high-cost, and AI tools iterate extremely fast — today's standards may be obsolete in six months. More subtly, many business judgments rely on tacit understanding and veteran intuition; forcibly documenting them actually strips information out. Treating "context" as a universal cure is another form of techno-optimism.
The judgment worth bookmarking: context is a necessary condition, not a sufficient one.
Impact on Regular People
For enterprise IT: before deploying AI Agents, auditing the company's knowledge assets is more urgent than buying models — otherwise you're paying to hire a brilliant but out-of-touch genius.
For individual careers: deliberately documenting your decision rationale and work logic isn't just good hygiene — it's your personal asset for collaborating with AI in the future.
For the consumer market: the next time you see an AI product touted as "top benchmark scores," ask one more question — in what scenario, and with what information, was it actually tested?