Agentic Context Engineering

Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models link

Motivation

Contexts should function not as concise summaries, but as comprehensive, evolving playbooks—detailed, inclusive, and rich with domain insights.

Component

• Generator: produces reasoning trajectories.
• Reflector: distills concrete insights from successes and errors
  • separates evaluation and insight extraction from curation, improving context quality and downstream performance.
• Curator: integrates these insights into structured context updates.

Incremental Delta Updates

1. context as a collection of structured, itemized bullets, rather than a single monolithic prompt. (1) metadata: including a unique identifier and counters tracking how often it was marked helpful or harmful. (2) content: capturing a small unit such as a reusable strategy, domain concept, or common failure mode.
2. Properties: (1) localization, so only the relevant bullets are updated. (2) fine-grained retrieval, so the Generator can focus on the most pertinent knowledge. (3) incremental adaptation, allowing efficient merging, pruning, and de-duplication during inference.
3. Incrementally produces compact delta contexts: small sets of candidate bullets distilled by the Reflector and integrated by the Curator.

Grow-and-Refine

1. bullets with new identifiers are appended, while existing bullets are updated in place (e.g., incrementing counters).
2. A de-duplication step then prunes redundancy by comparing bullets via semantic embeddings. This refinement can be performed proactively (after each delta) or lazily (only when the context window is exceeded), depending on application requirements for latency and accuracy.