LLMs

Everyone’s talking about MCP these days when it comes to large language models (LLMs)—here’s what you need to know.

SLMs are well-positioned for the agentic era because they use a narrow slice of LLM functionality for any single language model errand. LLMs are built to be powerful generalists, but most agents use only a very narrow subset of their capabilities.  They typically parse commands, generate structured outputs such as JSON for tool calls, or produce summaries and answer contextualized questions. These tasks are repetitive (up to the differences in prompt payloads), predictable, and highly specialized—well within the scope of specialized SLMs. An LLM trained to handle open-domain conversations is o

If SLMs have clear advantages, why do most agents still rely so heavily on LLMs? We hypothesize that the barriers are perception-based or caused by organizational culture rather than technical limitations. Shifting to SLM-enabled architectures requires an intentional mindset change. SLM research uses generalist benchmarks, even though agentic workloads demand different evaluation metrics. Plus, LLMs often dominate the headlines. As the cost savings and reliability of SLM-enabled systems become undeniable, momentum will shift. The transition could mirror past shifts in computing, such as the mo

"Agent" can be defined in several ways. Some customers define agents as fully autonomous systems that operate independently over extended periods, using various tools to accomplish complex tasks. Others use the term to describe more prescriptive implementations that follow predefined workflows. At Anthropic, we categorize all these variations as agentic systems, but draw an important architectural distinction between workflows and agents: Workflows are systems where LLMs and tools are orchestrated through predefined code paths.Agents, on the other hand, are systems where LLMs dynamically direc