# Context Engineering for AI Agents: What Actually Works at Scale

DevFeed: [Context Engineering for AI Agents: What Actually Works at Scale](<https://devfeed.tech/articles/context-engineering-for-ai-agents-what-actually-works-at-scale-12194.md>)

Original publisher: [Read original article](<https://www.port.io/blog/context-engineering-for-ai-agents>)

Author: Gur Shafriri

Published: 2026-08-10T11:31:32Z

Content type: article

Language: en

Sources: [Developer Experience & Platform Engineering Blog | Port](<https://devfeed.tech/sources/developer-experience-platform-engineering-blog-port.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [MCP](<https://devfeed.tech/topics/mcp.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [data](<https://devfeed.tech/topics/data.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [cost](<https://devfeed.tech/tags/cost.md>), [data](<https://devfeed.tech/tags/data.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [rag](<https://devfeed.tech/tags/rag.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

## AI overview

The article argues that AI agents perform poorly when their context is fragmented across tools such as GitHub, Jira, and PagerDuty. It presents a unified relational context layer as a way to improve accuracy, reduce token consumption, and speed up decisions. It contrasts this approach with MCP, which provides tool access but not an architecture for relationships, and RAG, which retrieves by vector similarity without reliably representing connections.

## Source excerpt

Context engineering for AI agents: why MCP and RAG fall short, and how a relational context lake cuts tokens and raises accuracy.