Context engineering is the discipline of systematically managing the full information ecosystem available to an AI agent — selecting, compressing, ordering, and delivering the right content to the context window at the right time.
Andrej Karpathy (2025) coined the canonical definition: “the delicate art and science of filling the context window with just the right information for the next step.” Where prompt engineering focuses on crafting a single instruction or query, context engineering addresses the architecture of everything the model sees: task descriptions, retrieved documents, tool definitions, memory, execution history, few-shot examples, and compaction strategies.
Context Engineering vs. Prompt Engineering
- Prompt engineering — one-off crafting of a single input message or instruction template
- Context engineering — systematic design of the entire information payload across all context window slots
Karpathy frames this as a scope shift: if prompt engineering was about writing a magical sentence, context engineering is about writing the full screenplay. This distinction matters operationally: as agents become long-running and multi-step, prompt-level adjustments are insufficient — the information architecture must be actively managed across sessions.
Five Dimensions of Context Quality
Synthesizing across the HumanLayer and martinfowler framing (Horthy 2026; Boeckeler 2026):
- Selection — what information to include and what to exclude; irrelevant content degrades performance
- Compression — reducing token usage without losing signal; compaction prevents context saturation
- Ordering — sequencing matters; attention degrades for information buried in the middle of long contexts
- Isolation — partitioning context using sub-agents prevents contamination across independent tasks
- Format — structured vs. prose presentation affects how reliably the model interprets the payload
Context Engineering in Harness Engineering
The two terms have an unresolved definitional boundary across sources:
- Horthy (2026): harness engineering is a subset of context engineering — the part focused on configuring coding agent harnesses
- Boeckeler (2026): context engineering is one of three components within harness engineering, alongside architectural constraints and entropy management
In practice, both framings agree on the core activity: continuously improving what the agent can see through codebase-embedded knowledge files, dynamic tool access, memory accumulation, and compaction strategies.
Academic Framing
Mei et al. (2025) provide a formal taxonomy decomposing context engineering into:
- Context retrieval and generation — sourcing relevant content
- Context processing — transforming and filtering for relevance
- Context management — memory systems, compaction, state tracking
These combine into production system patterns: RAG pipelines, tool-integrated reasoning, and multi-agent coordination.
Zhang et al. (2025) extend this with Agentic Context Engineering — treating contexts as evolving playbooks that self-improve through generation, reflection, and curation cycles, preventing “context collapse” (iterative rewriting that erodes important details).
Related Concepts
- Harness-Engineering — context engineering is a core component or subdiscipline of harness engineering
- Zhang-et-al-2026-Verified-Multi-Agent-Orchestration — academic framing of multi-agent context and orchestration
- Bui-2026-Building-Effective-AI-Coding-Agents — practitioner synthesis covering context management in coding agents
- Context-Rot
- Progressive-Disclosure-Context
- Adaptive-Context-Compaction
- AGENTS-md-Files
- Agent-Skills
Sources
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Karpathy, Andrej (2025). “Context Engineering.” X (Twitter). June 2025. Available: https://x.com/karpathy/status/1937902205765607626
- Canonical definition: “the delicate art and science of filling the context window with just the right information for the next step”; coined/popularized the term as the successor discipline to prompt engineering
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Mei, Lingrui, Jiayu Yao, Yuyao Ge, Yiwei Wang, Baolong Bi, et al. (2025). “A Survey of Context Engineering for Large Language Models.” arXiv:2507.13334. July 2025. Available: https://arxiv.org/abs/2507.13334
- Comprehensive academic taxonomy of context engineering across 1,400+ papers; decomposition into retrieval, processing, and management components
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Zhang, Qizheng, Changran Hu, Shubhangi Upasani, et al. (2025). “Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models.” arXiv:2510.04618. October 2025. Available: https://arxiv.org/abs/2510.04618
- Introduces ACE framework: contexts as evolving playbooks; addresses brevity bias and context collapse; +10.6% improvement on agent benchmarks
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Horthy, Dex (2026). “Skill Issue: Harness Engineering for Coding Agents.” HumanLayer Blog. Available: https://www.humanlayer.dev/blog/skill-issue-harness-engineering-for-coding-agents
- Positions harness engineering as a subset of context engineering; five dimensions for coding agents; AGENTS.md, MCP, skills, sub-agents, hooks as context engineering techniques
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Boeckeler, Birgitta (2026). “Harness Engineering.” Exploring Generative AI, Martin Fowler’s Blog. Available: https://martinfowler.com/articles/exploring-gen-ai/harness-engineering.html
- Context engineering as first of three harness components; continuously enhanced knowledge base; iterative improvement loop when agents encounter gaps
Note
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