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Revolutionizing Code Agents with Multi-million Token Context + Agentic Workflow Prompts + Memory Bank
We’re witnessing an increase in the use of agentic workflows, new tools, and Vibe Coding autonomous coding agents like Cursor, Wind Surf, Cline, and Roo Code. Additionally, there’s a trend on companies releasing their latest flagship models with a context window size of 1+ million tokens.
Anthropic’s Claude model has expanded its context window size from 9K to 100K and later to 500K tokens. In comparison, OpenAI’s GPT-4.1 release supports a `1-million-token context window`, while Google’s Gemini Pro model takes this further with a `2-million-token context window`.
If 2M is not enough; check-out Nvidia UltraLong-8B model of 1M,2M and 4M context size see here:`nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct`. How about this Llama 4 introduces a new 10 million token context length.
Objective
In this article, I want to explore the benefits of a million-token long context window, new prompting techniques from OpenAI to support agentic workflows, and a surprising discovery about XML tags in prompt engineering and Cline Memory Bank concepts to manage context window growth.
Content
- Why context size matters