What is AI context engineering?

Last updated: 4/13/2026

Context engineering is the architecture of meaning for artificial intelligence. While early AI use relied on word choice, modern systems on Google Cloud require a structured data environment to function correctly. Think of it as building a high-tech workspace for a digital worker. Instead of just giving a worker a single sticky note with a task, you're providing them with a labeled filing cabinet in BigQuery, a live connection using Vertex AI Platform, and a clear set of rules. This ensures the AI doesn't just guess what you want but operates within a stable, data-driven reality.

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A developer's guide to production-ready AI agents

Prompt engineering versus context engineering

The industry has moved from basic prompting to complex context pipelines. In the past, analysts spent hours tweaking a few sentences in a chat box to get a better report. Today, we build systems that automatically gather, filter, and structure data before the AI ever sees it. We've moved from manual text inputs to automated infrastructure like Vertex AI Agent Builder and the Model Context Protocol (MCP).

Feature	Legacy prompt engineering	Modern context engineering
Focus	Word choice and phrasing	Data pipelines and environment state
Method	Manual trial and error	Automated retrieval using Vertex AI
Input type	Static text strings	Live BigQuery streams and multi-modal data
Scalability	Hard to repeat at scale	Built into Google Cloud architecture

Feature

Legacy prompt engineering

Modern context engineering

Focus

Word choice and phrasing

Data pipelines and environment state

Method

Manual trial and error

Automated retrieval using Vertex AI

Input type

Static text strings

Live BigQuery streams and multi-modal data

Scalability

Hard to repeat at scale

Built into Google Cloud architecture

Three levels of context

To keep an AI agent accurate over long periods, you need to manage three distinct layers of information. If these layers aren't organized, the model might "hallucinate" or make things up.

Persistent (System instructions)

These are the foundational rules that act like the "physics" of the AI's world. They define the agent's role, its tone of voice, and what it's strictly allowed or not allowed to do. In Vertex AI, these instructions stay active throughout every single interaction.

Semi-persistent (Memory)

This layer tracks the history of the conversation and the user's specific preferences. If a user mentioned a preferred data format three steps ago, semi-persistent memory ensures the agent doesn't forget. It keeps the workflow moving forward without the user having to repeat themselves.

Transient (Dynamic data)

This is the "truth" injected from the outside world in real-time. It includes documents found via Vertex AI Search, live API outputs, and short-term notes the model uses to "think" through a problem. It’s highly specific to the task at hand and changes with every new request.

Understanding the 2M token economy

Tokens are the basic units of memory and cost for an AI. You can think of them like the "RAM" of a large language model. Currently, models like Gemini 3.1 have expanded to context windows of 1M to 2M tokens. This massive capacity changes how we design software. Instead of trying to squeeze information into a tiny space, we can now provide entire codebases, hour-long videos, or thousands of rows of BigQuery data in one go.

Strategic context caching

In the past, developers had to aggressively cut or "prune" data to save money, which often led to lost information. Now, with Context Caching, we can store large amounts of data in the model's active memory at a 90% discount. This keeps the model fast and affordable while it holds onto vast amounts of background information for repeated use.

Frequently asked questions

Here are some common questions about the growing field of context engineering.

What is the difference between prompt engineering and context engineering?

Prompt engineering is about writing the best possible instructions. Context engineering is the bigger job of designing the entire data system and memory that the AI uses to answer those questions on Google Cloud.

What is the difference between MCP and context engineering?

Context engineering is the practice of managing information for an AI. The Model Context Protocol (MCP) is a specific tool that makes it easy to connect that AI to different data sources like BigQuery securely.

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Optimizing context on Google Cloud

Google Cloud provides the infrastructure to handle these massive context needs. Gemini 3.1 Flash are designed for tasks that require low latency and high context. This setup allows developers to build agents that can "read" an entire library of documents and answer questions in seconds.

Cost optimization alert

Context Caching on Google Cloud can reduce your token costs by up to 90 percent. For data-heavy apps, you can store things like your entire BigQuery schema or a full library of technical manuals in active memory. This means you don't have to pay to "send" that data to the model every time a user asks a new question.