This topic examines the state of long‑context large language models (LLMs) and the surrounding toolchain for multi‑step development as of 2026‑02‑20. It focuses on models optimized for extended context windows, persistent memory, retrieval‑augmented workflows, and reliable multi‑stage reasoning—typified by Google’s Gemini 3.1 Pro and Anthropic’s Claude Sonnet 4.6—and the infrastructure used to build, evaluate, and deploy them. Relevance: teams across research, competitive intelligence, and product development increasingly need LLMs that can hold 10k–100k+ tokens, maintain coherent multi‑step plans, and safely call external tools. This has driven rapid adoption of retrieval systems, agent orchestration frameworks, and enterprise-grade hosting and governance. Key evaluation axes include context capacity, chain‑of‑thought fidelity, hallucination rates, latency/cost tradeoffs, and integration with developer workflows. Key tools and roles: Google Gemini (multimodal LLMs, Vertex AI/AI Studio APIs) and Claude Sonnet (high‑context reasoning) are core model choices; LangChain provides the SDKs and orchestration primitives for building agent pipelines and retrieval‑augmented generation; IBM watsonx Assistant targets enterprise virtual agents and multi‑agent orchestration with governance; GitHub Copilot and JetBrains AI Assistant are in‑IDE copilots for stepwise code synthesis and refactoring; Replit and MindStudio accelerate prototyping and no/low‑code agent deployment. Practical considerations: selecting a stack requires balancing model capabilities, orchestration (LangChain, agent platforms), developer productivity (Copilot, JetBrains, Replit), and enterprise controls (watsonx, Vertex AI). Ongoing trends include larger attention windows, modular retrieval and memory layers, standardized agent APIs, and marketplaces for model endpoints—key for competitive intelligence workflows and reproducible research.