More context is not always better
Longer histories raise cost and latency while making it harder for a model to focus on what matters most.
Context Engineering
Context Engineering is a Core Harness Engineer Discipline
Context engineering is the ongoing work of assembling, pruning, compressing, and prioritizing information so an AI agent gets the right context at the right time.
- Reduce context rot and attention dilution
- Manage long-running session costs and latency
- Create recoverable, dynamic context pipelines
Signal Layer Harness Engineer Index
Primary concern Dynamic context
Failure mode Context rot
Strategy family Compaction
Why context engineering matters
Context should match the task stage
A production agent needs different context payloads for planning, execution, recovery, and summarization.
Harness Engineers create the rules
This work is less about one perfect prompt and more about a reliable system for context selection.
Useful compaction patterns
Keep the last N turns
Useful when recency dominates, but dangerous when important facts live further back in history.
Token-based truncation
Simple and cheap, but blind to semantic importance.
Recursive summarization
Helpful for long tasks, but it must be handled carefully because summaries can hide or distort important details.
Production-grade ideas
Keep recoverable references
A URL, file path, or retrieval key can often stand in for full content until that content is needed again.
Use recitation to refresh goals
Repeating the current objective or plan near the end of the context can reduce lost-in-the-middle effects.
Keep evidence of failure
Wrong turns can teach the model what not to repeat, so deleting them can be counterproductive.
Frequently Asked Questions
These questions cover high-frequency search intent around Harness Engineer, context engineering, sessions, and memory.
How is context engineering different from prompt engineering?
Prompt engineering focuses on phrasing a single input, while context engineering governs the larger system that decides what information enters the model context at all.
Why does a Harness Engineer need context engineering?
Because long-running AI systems fail when context grows unmanaged. Context engineering keeps the agent relevant, efficient, and stable.
Next Step
Need a context strategy for your AI product?
We can help map context flows, compaction rules, and retrieval design to real product requirements.