Context Engineering
The discipline replacing prompt engineering: designing dynamic context systems that give agents the right information at the right time. Covers lazy loading, memory-augmented architectures, and production patterns.
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After this section you can
- Explain why context engineering replaced prompt engineering for agentic systems, and name the four context layers — system instructions, policy & guardrails, knowledge, and user state — with what belongs in each
- Plan a context budget for a 128K window: allocate fixed slices for system, tools, RAG, history, and scratchpad, enforce the keep-input-under-25% rule, and reserve the remainder for generation
- Apply the five production patterns — lazy loading, memory-augmented context, compression, tool-based retrieval, and context routing — to a concrete agent design and defend each trade-off
- Diagnose the four context failure modes (overflow, irrelevant retrieval, memory decay, tool-definition bloat) from their symptoms and pair each with the right fix
SD-6
Context Engineering
The discipline that replaced prompt engineering for agentic AI systems. Context engineering is the art of dynamically assembling the right information, tools, and state at runtime so an LLM can reliably complete complex tasks.
Prompt Engineering vs Context Engineering
GARTNER 2025:
"Context engineering is in, prompt engineering is out. 80% of AI tools by 2028 will use context engineering over static prompting."