Embedding-based RAG for code search is falling out of favor because its arbitrary chunking often fails to capture full semantic context. Simpler, more direct approaches like agent-based search using tools like `grep` are proving more reliable and scalable for retrieving relevant code without the maintenance overhead of embeddings.

Rather than relying on a single LLM, LexisNexis employs a "planning agent" that decomposes a complex legal query into sub-tasks. It then assigns each task (e.g., deep research, document drafting) to the specific LLM best suited for it, demonstrating a sophisticated, model-agnostic approach for enterprise AI.

Training AI agents to execute multi-step business workflows demands a new data paradigm. Companies create reinforcement learning (RL) environments—mini world models of business processes—where agents learn by attempting tasks, a more advanced method than simple prompt-completion training (SFT/RLHF).

According to IBM's AI Platform VP, Retrieval-Augmented Generation (RAG) was the killer app for enterprises in the first year after ChatGPT's release. RAG allows companies to connect LLMs to their proprietary structured and unstructured data, unlocking immense value from existing knowledge bases and proving to be the most powerful initial methodology.

While vector search is a common approach for RAG, Anthropic found it difficult to maintain and a security risk for enterprise codebases. They switched to "agentic search," where the AI model actively uses tools like grep or find to locate code, achieving similar accuracy with a cleaner deployment.

While language models are becoming incrementally better at conversation, the next significant leap in AI is defined by multimodal understanding and the ability to perform tasks, such as navigating websites. This shift from conversational prowess to agentic action marks the new frontier for a true "step change" in AI capabilities.

Unlike chatbots that rely solely on their training data, Google's AI acts as a live researcher. For a single user query, the model executes a 'query fanout'—running multiple, targeted background searches to gather, synthesize, and cite fresh information from across the web in real-time.

The agent development process can be significantly sped up by running multiple tasks concurrently. While one agent is engineering a prompt, other processes can be simultaneously scraping websites for a RAG database and conducting deep research on separate platforms. This parallel workflow is key to building complex systems quickly.

While complex RAG pipelines with vector stores are popular, leading code agents like Anthropic's Claude Code demonstrate that simple "agentic retrieval" using basic file tools can be superior. Providing an agent a manifest file (like `lm.txt`) and a tool to fetch files can outperform pre-indexed semantic search.