Long-term memory is what separates agents that get smarter over time from agents that start from scratch on every session. The four memory patterns, how to implement each, and the engineering decisions that make agent memory practical.

The 2026 pattern for cost-efficient production agents: small language models handle simple tasks cheaply, large models handle complex tasks well. How to design the routing layer, what to measure, and what the cost-quality tradeoffs actually look like.

Reasoning models think before responding — generating extended chain-of-thought that improves accuracy on hard problems at the cost of latency and tokens. When the thinking investment pays off, and when it doesn't.

Claude Opus 4.8 and GPT-5.4 can control computers — take screenshots, click buttons, fill forms, navigate applications. Computer use agents open use cases that tool-calling can't reach. The capabilities, the risks, and where they actually fit.

Stateless agents are easy to build and fragile in production. Long-running tasks, multi-turn conversations, and recovery from failures all require explicit state management. The patterns that make agents durable.

Most RAG systems are deployed without a proper evaluation framework. Teams discover quality problems from user complaints rather than metrics. The evaluation metrics, frameworks, and test set design that make RAG quality measurable.

How you split documents determines what your RAG system can find. Fixed-size chunking is the beginner approach. Semantic chunking, parent-document retrieval, and document-type-aware splitting are what production systems actually use.

The vector database landscape has matured. Qdrant, pgvector, Weaviate, and Pinecone each have a clear profile. Here's the decision framework based on scale, existing infrastructure, and query patterns — not benchmarks.

Context windows are finite even at 100K+ tokens. Long-running agents accumulate state, conversation history, and tool outputs that eventually overflow the window. The strategies that keep production agents working correctly over time.

The RAG vs fine-tuning debate is largely resolved in 2026: the answer is hybrid. Volatile knowledge in retrieval, stable behaviour in fine-tuning. Here's the decision framework and the specific cases where each approach wins.