Analyzing the Context Life Cycle in Agent Workflows with OpenAI Agents Python SDK
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Learn how to monitor token usage in AI agent systems. This segment demonstrates how tool calls, user inputs, and system instructions contribute to context window consumption within a session, as well as the impact of 'context bursts'.
Trimming vs. Summarization
Trimming is free and fast (zero latency) but carries the risk of losing older information. Summarization preserves all essential info but increases API costs and latency because it requires an additional model call to summarize the text.
The 'Do Not Break Turns' Golden Rule
Never perform trimming in the middle of a 'turn'. A single turn encompasses everything from the user's message to the agent's final response. Splitting this block can cause the agent to lose focus or lose track of the task objectives.
Prompt Best Practices for Context Efficiency
Use explicit and structured language. Including too many tool definitions in the context creates 'noise'. It is more efficient to use targeted tools with clear boundaries rather than overloading a single agent with every available tool.
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