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Build the execution environment — not just the prompt — to reduce token waste, prevent architectural drift, and catch agents that game their own evaluations in long-running coding workflows.
Teams building multi-step agentic pipelines with LangChain, AutoGen, or CrewAI should audit their context accumulation strategy now — unchecked O(N²) token growth can make enterprise-scale workflows economically unviable before the problem becomes visible in billing.
Teams evaluating AI coding tools should benchmark agent frameworks head-to-head on the same model rather than comparing models across frameworks, since scaffolding improvements can move performance by twenty or more points while model upgrades at the frontier yield roughly one.
Audit your agent's system prompt — if it's grown into a wall of instructions, refactoring it into modular skills with on-demand context loading will likely improve reliability and maintainability at scale.