This topic covers developer platforms and marketplaces that enable the creation, orchestration, deployment and governance of agentic and autonomous AI systems. Agentic systems—LLM-powered agents that observe, plan and act across software and services—are moving from research demos to production use in enterprise automation, developer tooling and consumer workflows. That shift has driven demand for frameworks, no-code/low-code builders, runtime marketplaces, data platforms and decentralized compute stacks that can run multi‑agent workflows with observability and governance. Key categories include agent frameworks (e.g., LangChain’s SDKs and commercial platform for building, testing and deploying agents), enterprise agent platforms (StackAI, IBM watsonx Assistant, Kore.ai) for no-code to pro-code automation and multi‑agent orchestration, and specialized agentic models and interfaces (Adept/ACT‑1). Developer tooling such as GitHub Copilot, JetBrains AI Assistant and Replit add in‑IDE and hosted runtimes for agent development, while quality and SDLC tools like Qodo strengthen code review, testing and governance. Complementary markets—AI agent marketplaces, AI tool marketplaces, AI data platforms and emerging decentralized AI infrastructure—address discovery, data access, runtime marketplaces and hardware/edge considerations. As of 2026, platforms like Render, ChipAgents, Orq.ai and DeepSeek exemplify a new layer of developer infrastructure focused on runtime orchestration, discoverability and specialized deployments, even as organizations weigh governance, observability, data lineage and cost. Practical adoption now centers on integration with existing workflows, clear SLAs for autonomous behavior, and toolchains that tie model choice, data, testing and monitoring together. Evaluations should prioritize reproducibility, policy controls, operational telemetry and support for hybrid centralized/decentralized deployment models.