MapReduce for Agents

Problem

Stuffing the whole input into a long-context model still degrades quality past a certain point; quality drops in the middle of long documents and the model conflates entities across the input. Chunking the input and processing each chunk in isolation loses anything that depends on more than one chunk, such as cross-document deduplication or per-entity aggregation. Without a structured reduction step, conflicts between chunk answers go unresolved, and the team ends up either rerunning the whole thing in a giant call or hand-merging chunk outputs.

Solution

Map: split input into chunks; process each independently (per-chunk LLM call). Reduce: aggregate intermediate answers via a structured information protocol that surfaces dependencies, plus a confidence-calibration step to resolve conflicts.

When to use

• Input is too large for any single context window to handle well.
• Chunks are mostly independent and a structured reducer can resolve cross-chunk dependencies.
• A confidence-calibration step can reconcile conflicting per-chunk answers.