Understanding Context Engineering in AI Agents: Memory and Tools
goyalshaliniuk · x · 2026-08-03
This post breaks down the mechanism of "Context Engineering" in AI agents, explaining how they combine user inputs, memory, and tools to deliver smarter answers.
Core Workflow:
- Input & Decision: The agent receives a query and decides whether external tools or previous context are needed.
- Retrieval & Action: It performs vector searches via RAG across long-term memory (like MCP servers) or calls external APIs to run code.
- Prompt Update & Output: All gathered context is compiled into a single prompt to generate the final answer.
- Memory Management: Current interactions are saved to short-term memory for continuity, while key insights are added to long-term memory to improve future responses.
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