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About this paper
Vera V. Vishnyakova
HSE University research introduces Context Engineering (CE) as a new discipline, followed by Intent and Specification Engineering, to move beyond prompt engineering for designing and managing complex, autonomous multi-agent AI systems. This framework establishes a four-level maturity model, improving agent reliability, strategic alignment, and reducing inference costs by 5-10 times, addressing critical scalability and governance challenges in enterprise AI.
Key insights
- Context should be treated as an agent's 'operating system,' requiring five production-grade quality criteria: relevance, sufficiency, isolation, economy, and provenance, to prevent degradation.
- As AI systems become more autonomous, human influence shifts from tactical query formulation to strategic architectural design and the explicit codification of intentions and governing specifications.
- Effective Context Engineering can lead to a 5-10x reduction in inference costs, making large-scale agent deployments economically viable.
Summary by alphaXiv.
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