Technology Radar
Context engineering has evolved from an optimization tactic into a foundational architectural concern for modern AI systems. Unlike prompt engineering, which focuses on wording, context engineering treats the context window as a design surface and intentionally constructs the AI’s information environment.
As agents tackle more complex tasks, dumping raw data into large context windows leads to "context rot" and degraded reasoning. To combat this, teams are shifting from static, monolithic prompts to progressive context disclosure. Instead of front-loading every instruction and reference an agent might need, these systems start with a lightweight index of what's available. The agent determines what prompts or contexts are relevant and pulls in only what’s needed, keeping the signal-to-noise ratio sharp at every step.
We’re seeing several techniques mature in this space: Context setup leverages prompt caching to front-load static instructions, reducing costs and improving time to first token. Dynamic retrieval goes beyond basic RAG by selecting tools and loading only the necessary MCP servers, avoiding unnecessary context expansion. Context graphs model institutional reasoning — such as policies, exceptions and precedents — as structured, queryable data. Context management techniques use stateful compression and sub-agents to summarize intermediate outputs in long-running workflows.
Treating AI context as a static text box is a fast track to hallucinations. To build resilient enterprise agents, teams must engineer context as a dynamic, precisely managed pipeline.
Context engineering is the systematic design and optimization of the information provided to a large language model during inference to reliably produce the desired output. It involves structuring, selecting and sequencing contextual elements — such as prompts, retrieved data, memory, instructions and environmental signals — so the model’s internal layers operate in an optimal state. Unlike prompt engineering, which focuses only on the wording of prompts, context engineering considers the entire configuration of context: how relevant knowledge, instructions and prior context are organized and delivered to achieve the most effective results.
Today, engineers use a range of discrete techniques that can be grouped into three areas: Context setup covers curation tactics such as using minimal system prompts, canonical few-shot examples and token-efficient tools for decisive action. Context management for long-horizon tasks addresses finite context windows through context summarization , structured note-taking to persist external memories and sub-agent architectures to isolate and summarize complex sub-tasks. Dynamic information retrieval relies on just-in-time (JIT) context retrieval, where agents autonomously load external data only when immediately relevant, maximizing efficiency and precision.
