Autonomous agent toolkits combine agent frameworks, model platforms, retrieval systems and deployment pipelines to build software that can plan, act and automate tasks with minimal human intervention. This topic examines the features, security controls and deployment models that matter when moving agent proofs-of-concept into production: developer frameworks for orchestration and state (e.g., LangChain’s engineering stack and stateful LangGraph), retrieval-and-RAG layers for document-driven agents (LlamaIndex), and end-to-end agent builders and workflows (AutoGPT, StackAI, IBM watsonx Assistant). Relevance in early 2026 stems from three converging trends: wider enterprise adoption of agentic automation, greater emphasis on safety and governance, and diverse infrastructure choices (cloud-managed platforms, self-hosted stacks, and emerging decentralized compute). Managed platforms such as Google Vertex AI and enterprise LLM providers like Cohere offer hosted model, fine-tuning and monitoring capabilities; acceleration clouds (Together AI) and marketplaces enable scalable inference, model exchange and cost-optimized deployment. Tooling now focuses on observability, access controls, prompt and chain testing, and guardrails to mitigate data leakage, prompt-injection and unintended actions. Key considerations for teams include choosing the right combination of frameworks (agent orchestration vs. document agents), deployment footprint (self-hosted vs. fully managed vs. hybrid), and governance controls (audit trails, role-based access, safe-exit and human-in-the-loop policies). Marketplaces and decentralized infrastructure introduce new distribution and sovereignty models but raise supply-chain and attestation questions. This overview helps engineering and security stakeholders evaluate trade-offs across Agent Frameworks, AI Agent Marketplaces, AI Tool Marketplaces, Decentralized AI Infrastructure and AI Security Governance to make pragmatic, auditable decisions for production agent deployments.