LangChain Argues Goal Engineering Will Replace Prompt Engineering for Autonomous Agents

·2026.11.15 01:00

Key point

The authors argue that aligning agent scope with human review capabilities is the key to extracting tacit knowledge and building reliable long-horizon agents.

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Details

Core thesis: Design agent scope to make human feedback as easy as possible, as this is the primary method for providing context that is not already written down.
Actively AI case study: Shifted from per-account agents to "motions" (specific jobs like outbounding) to reduce context switching and allow feedback to transfer across similar tasks.
Future outlook: Prompt and context engineering will disappear, but goal engineering will remain critical for teaching agents subjective domain knowledge and preferences.
Implementation: Use background agents to gather objective account context, while motion-based agents handle the subjective work of progressing deals through the funnel.

langchain ai-agents human-feedback agent-design goal-engineering actively-ai

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