This topic covers platforms and toolchains that enable organizations to train, deploy, monitor and continuously improve autonomous (self‑improving) AI agents. An end‑to‑end approach spans curated training data, fine‑tuning and scalable GPU training, agent frameworks that act inside software, no‑code/low‑code builders for rapid assembly, quality tooling for code and tests, and production inference with observability and governance. As of 2026‑05‑08 this area is urgent for enterprises adopting agentic automation: open‑source model innovation and more efficient GPU clouds have lowered the cost of iteration, while operational concerns—data pipelines, testing, retraining cadence, model drift, and compliance—make integrated platforms valuable. Key categories include AI Automation Platforms (no‑code/low‑code agent builders and orchestration), AI Data Platforms (data curation and labeling pipelines), Agent Frameworks (action‑oriented models and runtime APIs), and AI Agent Marketplaces (distribution and reuse of agents and connectors). Representative tools illustrate the stack: StackAI positions itself as an enterprise no‑code/low‑code platform for building, deploying and governing agents; Tate‑A‑Tate offers a visual drag‑and‑drop path from idea to agent without coding; Adept focuses on agentic models (e.g., ACT‑1) that observe and act inside software to automate multistep workflows; Together AI provides GPU‑centric training, fine‑tuning and serverless inference for open and specialized models; DatologyAI targets model readiness by turning raw datasets into curated training data; Qodo emphasizes code quality, context‑aware reviews and automated test generation across repos; and JetBrains AI Assistant supports in‑IDE developer workflows. Effective end‑to‑end platforms combine continuous data curation, rigorous testing, secure deployment, human‑in‑the‑loop feedback and governance to support agents that can be iteratively improved in production.