Context Engineering
Deciding what goes into an LLM's context window: context limits, context rot, compaction and summarization, prompt caching and agentic context management.
A larger context window does not make an agent smarter; it makes the cost of a bad context larger. Models lose track of facts buried in the middle of long prompts, and a context that grows without pruning degrades on its own while the bill for every token keeps rising. Context engineering is the discipline of choosing what the model sees on each turn and throwing away what it no longer needs.
These articles look at long-context behaviour, compaction and summarization strategies, prompt caching and the case for treating context as something an agent manages rather than something it accumulates. For the difference between remembering across sessions and managing a single window, start with the comparison linked below.
Start here: Memory vs context windows · Context engineering (glossary) · Context rot (glossary) · Prompt caching (glossary)
TencentDB Agent Memory, Read From the Source: It Needs a Second LLM and Searches by Keyword by Default
The guides and news pages that rank for TencentDB Agent Memory repeat three claims its source code does not support: that it builds memory without any model API, that it runs hybrid vector search out of the box, and that no conversation data leaves the machine.
Context Language Models: What the UW and Meta Paper Changes for Agent Builders, and What It Leaves to Memory
A Context Language Model is an existing model that rewrites its own context like a file. It manages working context inside a task better than summarisation, with less compute, and throws that context away when the task ends.
Harness And Agents
Every LLM call is stateless, and a model on its own cannot act; the software that turns it into an agent is the harness.
Short-Term vs Long-Term Memory in LLMs, and What "Context Memory" Actually Means
Short-term memory in an LLM is whatever sits in the context window on the current call: the system prompt, the recent conversation, tool results and any retrieved text, all re-sent by the application on every request and gone when the request ends.
LLM Context Windows in 2026: GPT-4o Is 128K, the Frontier Is 1M, and What Happens When a Chat Runs Out
GPT-4o has a 128,000-token context window and can write at most 16,384 tokens in a single reply, with the reply counted inside the same 128,000.
Agentic Context Management: Agent Memory Is Not Merely a Storage & Retrieval Problem, It Is an Architecture Problem
We argue in our latest paper, that agent memory and cost is a lifecycle and architecture problem
An Anthropic Leader Told a Room of Founders to Stop Worrying About Context Windows. Here's the catch
An Anthropic researcher told a room of founders to stop worrying about context windows. Here is the question I did not get to ask, and why a bigger window solves short-term memory with bad tradeoffs and does nothing for long-term memory.
What Is Agentic Context Management?
Agentic context management is how you orchestrate what information your AI agents see and when. Learn why it's critical infrastructure, not a feature, and how to architect it properly.