This topic examines how modern large language models (LLMs) — notably Anthropic’s Claude Opus 4.5, OpenAI’s GPT variants, and Google’s Gemini — are being used to generate code and power agentic automation, and how that capability maps onto today’s developer platforms and agent frameworks. It’s timely in late 2025 because model improvements, longer context windows, tool APIs, and stateful agent infrastructure have shifted focus from single-call completion to multi-step, tool-enabled automation across IDEs, CI pipelines and web services. Key categories include AI code generation tools and assistants (Code Llama, Salesforce CodeT5, WizardLM/WizardCoder, Tabnine), AI-native IDEs and agentic coding platforms (Windsurf, Replit, GitHub Copilot), and agent frameworks and marketplaces for composing and deploying agents (LangChain with LangGraph, GPTConsole, Cline, Flowpoint). Claude Opus 4.5, GPT variants and Gemini differ in safety and instruction-following tradeoffs, multi-/multi-modal strengths, ecosystem integrations, and enterprise deployment options — factors that matter for choosing a runtime for code-heavy agents. Practical trends: multimodel orchestration and hybrid stacks (combining specialized code models with generalist agents), stateful agent runtimes and memory for long-running workflows, live previews and inline execution in IDEs, and a growing demand for self-hosted/private deployments and governance. Tooling now emphasizes lifecycle management (testing, monitoring, memory/RETRIEVAL), event chaining, and observability. Selecting the right stack depends on priorities — integration with existing CI/CD and cloud, offline/private inference, latency and cost, or best-in-class code synthesis and safety — rather than a single “best” model.