Meta Context Engineering via Agentic Skill Evolution

2026-01-29

AI Summary

This work proposes Meta Context Engineering (MCE), a novel framework that overcomes the structural biases and limited optimization space inherent in existing context engineering approaches, which rely on manually designed, fixed pipelines. MCE introduces a bilevel agent co-evolution paradigm that dynamically optimizes both the structure and content of prompts through agent crossover, skill history tracing, execution feedback, and programmable context representations. By moving beyond static heuristics, the method significantly enhances context adaptability, transferability, and training efficiency. Empirical evaluations across five tasks demonstrate consistent improvements, yielding relative performance gains ranging from 5.6% to 53.8% (average 16.9%) over state-of-the-art agent-based context engineering methods.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Search and Optimization: Metareasoning and Metaheuristics
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The operational efficacy of large language models relies heavily on their inference-time context. This has established Context Engineering (CE) as a formal discipline for optimizing these inputs. Current CE methods rely on manually crafted harnesses, such as rigid generation-reflection workflows and predefined context schemas. They impose structural biases and restrict context optimization to a narrow, intuition-bound design space. To address this, we introduce Meta Context Engineering (MCE), a bi-level framework that supersedes static CE heuristics by co-evolving CE skills and context artifacts. In MCE iterations, a meta-level agent refines engineering skills via agentic crossover, a deliberative search over the history of skills, their executions, and evaluations. A base-level agent executes these skills, learns from training rollouts, and optimizes context as flexible files and code. We evaluate MCE across five disparate domains under offline and online settings. MCE demonstrates consistent performance gains, achieving 5.6--53.8% relative improvement over state-of-the-art agentic CE methods (mean of 16.9%), while maintaining superior context adaptability, transferability, and efficiency in both context usage and training.

Problem

• Context Engineering
• structural bias
• design space
• context optimization
• large language models

Innovation

• Meta Context Engineering
• Agentic Skill Evolution
• Context Optimization
• Agentic Crossover
• Bilevel Framework