“No, I don’t have it.”
Simple message, right?
Now imagine it’s from a WhatsApp group — a reply in the middle of tens of different discussions.
Assume you have an AI agent monitoring this group to provide support. How does the agent make sense of that message? How does it interpret the message and decide what action to take?
The answer is context engineering.
It’s the means and ways with which AI agents make sense of conversations — detecting, maintaining, and tracking context accurately and reliably across the application.
As AI agents evolve to mimic intelligent human behaviour, context engineering becomes absolutely critical.
Here’s a real example from a government implementation we did:
Users could enquire about vehicle fines. The agent would collect the vehicle details, fetch the data, and share the response.
If the customer then asks follow-up questions like “show total outstanding fine” or “show the last fine amount,” the agent recognize the ongoing context and doesn’t ask for vehicle details again. But if the customer asks about another car, the agent detects the new context — and requests the new registration details.
And this works always – not ‘sometime’ or ‘most of the time’ – because this isn’t LLM logic. LLMs work on probabilities and estimations. They can estimate context but can’t guarantee it. In real-world scenarios with a large number of overlapping contexts (like the WhatsApp example above), it is almost impossible to detect context accurately using only prompt engineering. LLMs are a good starting point — but not enough.
It’s application logic, in our case.
And it’s not estimation; it’s mathematical calculation – hence the accuracy. Since it’s a calculation, the context is fail-safe and foolproof. By design, failure isn’t even a possibility anymore. And this context is tracked throughout the application with precision.
Beautiful, isn’t it?
The point I am making is that agentic AI applications are still in their early stages. There’s enormous potential, but also many unsolved challenges — even for the biggest companies. Context engineering is core to agentic AI. There’s plenty of discussion around prompt engineering, but not enough or almost no discussion about context engineering. It’s time we start focusing more on this.
NB: Food for thought, go back to the WhatsApp example at the start. This is a real use case we are discussing with an event management company. How would you calculate/manage the context if you were designing this system?
admin
Content writer at BotPlus