-
Agent memory: short-term, long-term, and avoiding context rot
An AI agent can process thousands of messages, call dozens of tools, and work across multiple sessions. Yet it can still forget a crucial decision from twenty minutes ago, resurrect an outdated requir
-
Multi-agent orchestration: when more agents help and when they hurt
Multi-agent systems have become one of the most discussed patterns in modern AI engineering. The idea sounds intuitive: instead of asking one model to solve a difficult problem, create a team of speci
-
The Model Context Protocol (MCP): connecting agents to real tools
Large language models are good at reasoning over text, but real-world software systems rarely live inside text alone. A useful AI agent needs to interact with calendars, databases, ticketing systems,
-
Your first tool-using agent: function calling done right
A tool-using agent is not a language model that somehow reaches into your Python process and executes functions by itself.
-
Hybrid search: combining BM25 keyword and dense vectors
Dense vector retrieval is excellent at matching meaning.
-
Choosing a vector database in 2026: pgvector vs the specialists
Retrieval-augmented generation (RAG) systems have moved from prototypes into production software. In 2026, choosing a vector database is no longer just about asking, “Can this store embeddings?”
-
Chunking strategies that actually matter for retrieval quality
If you’ve experimented with embedding models, vector databases, rerankers, or prompts, but your Retrieval-Augmented Generation (RAG) system still misses obvious answers, the bottleneck may be much sim