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Written by Max Zeshut
Founder at Agentmelt · Last updated Jul 8, 2026
The discipline of deciding exactly what information goes into an AI agent's context window on each turn—system prompt, tool definitions, retrieved documents, prior messages, memory, and skills—and what stays out. Context engineering replaces 'prompt engineering' as the dominant skill for production agents: in long-running agents the prompt is fixed, and quality is determined by what context is curated for each step. Done badly, context engineering causes hallucination, cost overruns, and lost-in-the-middle failures.
A legal agent reviewing a contract used to stuff the entire 80-page contract plus 30 similar past contracts into context (190K tokens, $1.50 per review, lost-in-the-middle errors on key clauses). After applying context engineering—chunking the target contract, retrieving only the 5 most similar past clauses for each section, and pruning prior conversation turns—context drops to 35K tokens, cost falls to $0.18, and clause-extraction accuracy rises from 89% to 96%.