Definition
The technique of efficiently passing search result data between steps in a multi-step AI agent pipeline, using summarization, key extraction, or structured serialization to prevent context window overflow while preserving the information needed for downstream reasoning.
In Depth
Multi-step agents that perform web searches generate large amounts of context data. A single Google search returns 10+ organic results with titles, snippets, and URLs. An agent performing 5 searches accumulates 15-30KB of raw search text, which can consume a significant portion of available context window, especially when combined with system prompts, conversation history, and tool schemas. Handoff strategies ranked by context efficiency: (1) Key extraction -- after each search, extract only the specific facts needed (prices, dates, names) and discard the full results. Most context-efficient but loses supporting detail. (2) Structured summary -- use the LLM to summarize search results into a compact JSON object with predefined fields. Reduces 5KB of results to 200-500 bytes. (3) Top-N truncation -- keep only the top 3 results instead of all 10. Simple but may miss relevant results at lower positions. (4) Reference indexing -- store full results in an external store, pass only an index/ID to the next agent step, and retrieve when needed. Zero context overhead but requires external storage. For agents using Scavio's structured JSON responses, the data is already partially optimized for handoff. Structured fields (title, snippet, link) are more compact than raw HTML. Further optimization: strip fields the next step does not need. If the agent only needs URLs, pass only the link array. If it needs summaries, pass only snippet fields. Practical implementation: define a handoff schema per agent step that specifies which search result fields to retain. The schema acts as a filter, applied after each search step before results enter the context window.
Example Usage
The research agent performs 8 searches per task. Without handoff optimization, search context consumed 40KB (60% of available window). After implementing key extraction, each search result reduces to ~300 bytes of extracted facts, bringing total search context to 2.4KB (4% of window).
Platforms
Agent Context Window Handoff is relevant across the following platforms, all accessible through Scavio's unified API:
- Amazon
- YouTube
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