According to God of Prompt, effective compaction strategies are crucial for AI agents to avoid context limit issues and maintain optimal performance (source: @godofprompt, Jan 12, 2026). Professional AI practitioners employ intelligent context compression during conversations by summarizing ongoing interactions, retaining architectural decisions, discarding redundant outputs, and preserving key findings. This approach keeps the context streamlined, enables the agent to remain focused, and allows for more efficient handling of large-scale conversational data. These compaction methods present significant business opportunities for AI developers and enterprises seeking to maximize the efficiency and reliability of conversational AI solutions.