This topic surveys the current generation of developer AI and code‑assistance platforms that pair GPT‑5.5‑class generative models with Codex‑style code models or integrations to accelerate software development. It covers in‑IDE copilots, enterprise assistants, open/self‑hosted alternatives, and agent frameworks and marketplaces that automate workflows. Key offerings include GitHub Copilot (inline completions, Copilot Chat, and autonomous agent workflows across editors and terminals), JetBrains AI Assistant (context‑aware generation and refactorings inside JetBrains IDEs), and Amazon CodeWhisperer’s evolution into the Amazon Q Developer experience for real‑time suggestions. Enterprise and privacy‑focused tools like Tabnine and Tabby emphasize private or self‑hosted deployments, model governance, and multi‑repo context. Model and research alternatives—Code Llama, Salesforce CodeT5, and Anthropic’s Claude family—supply specialized code understanding or conversational capabilities accessible via APIs and platforms such as Google’s Gemini. Quality‑focused solutions like Qodo address code review, automated test generation, and SDLC governance. No‑code/low‑code agent platforms (Lindy, StackAI) and AI tool marketplaces are enabling rapid composition, deployment, and governance of autonomous developer agents. As of May 2026, the ecosystem is driven by two clear trends: consolidation of powerful code models into ubiquitous editor and cloud integrations, and stronger enterprise requirements for privacy, reproducibility, and governance that favor self‑hosted or auditable deployments. For teams choosing a solution, the tradeoffs are integration and developer UX (Copilot, JetBrains), enterprise governance and data locality (Tabnine, Tabby, Qodo), and extensibility through agent frameworks and marketplaces (Lindy, StackAI, marketplace ecosystems). This overview helps compare capabilities across code generation, review, testing, agent automation, and deployment models.