Context is your budget
In AI engineering, context is your most valuable and expensive resource. Every token you send to an LLM costs money and adds latency. More importantly, the more irrelevant information you cram into the context window, the more likely the model is to lose track of its instructions.
The art of context curation
Context engineering is the active curation of what the model sees at each step of its execution loop. A great harness manages this dynamically:
- Short-Term Memory: Pruning and summarizing past conversation turns so the agent doesn't exceed its token budget.
- RAG (Retrieval-Augmented Generation): Pulling in only the most relevant snippets from your vector database based on semantic search.
- Tool Schema Rendering: Formatting tool descriptions cleanly so the model understands exactly how to invoke your APIs.
By treating context as a precious resource, you build faster, cheaper, and vastly more reliable AI agents.