Long‑context and multi‑step reasoning LLMs focus on models and toolchains that can hold far larger input/state windows and reliably execute multi‑turn, multi‑step logic across documents, tools, and memory. As of 2026‑02‑23 this area matters because production use cases—complex document QA, code synthesis across large repositories, regulatory compliance checks, and automated agent workflows—depend on models that can access long context, use external tools, and maintain coherent multi‑step plans. Key model families include Anthropic’s Claude line (e.g., Sonnet variants) and Google’s Gemini family (e.g., Gemini 3.1 Pro), alongside GPT‑class long‑context variants; these prioritize expanded context windows, multimodal inputs, and interfaces for tool calling and retrieval. Complementary tooling enables production workflows: LangChain provides engineering frameworks to orchestrate agentic chains and evaluations; LlamaIndex converts unstructured corpora into retrieval‑ready indexes for RAG; Vertex AI offers managed infrastructure for training, deploying, and monitoring scaled models; and AutoGPT‑style platforms automate persistent agents and automation flows. Practical trends to watch include tighter integration of retrieval‑augmented generation, stateful memory, deterministic planning primitives for multi‑step tasks, and standardized evaluation pipelines for reasoning reliability and safety. For marketplaces, automation platforms, GenAI test automation, and AI data platforms, the focus is shifting from single‑prompt outputs to reproducible, debuggable pipelines that combine large contexts, tool use, and rigorous testing. Organizations evaluating these technologies should balance context capacity, latency/cost, orchestration tooling, and evaluation frameworks to deploy multi‑step applications reliably and safely.