Articles, tutorials, and guides tagged Context Engineering.
A single tool call can dump a whole file, a full API response, or a thousand log lines straight into the context window. The agent needed one field. Now every turn after that re-pays for the whole blob. Offloading the payload to a store and keeping only a handle in context fixes both the cost and the window.
In a long session an agent keeps every tool result it ever saw, including the three older versions of a file that has changed twice since. It then acts on the wrong one. This is agent drift, and it is a correctness bug, not a token bill. Here is how staleness-aware pruning fixes it.
LLMs are stateless, so every new session starts from zero. Here is how to give an agent persistent memory that recalls the right facts across sessions without bloating the context window or the token bill.
Connect a dozen MCP servers to an agent and you pay for every tool definition on every turn, before the user has said a word. Here is why tool-calling bloats context and how the code execution pattern cuts it by orders of magnitude.
Long-running AI agents fail when context grows without bound, blowing up token costs, latency, and reliability. Here is how anchored summarization and server-side compaction keep agents cheap and coherent.