This topic covers platforms and frameworks used to design, run and govern AI agents in production: core agent frameworks for developers, marketplaces and templates for reuse, low-code workflow platforms for rapid deployment, and security/governance tooling to keep autonomous systems auditable and safe. By mid‑2026, organizations are moving beyond proofs‑of‑concept to production agent deployments that require observability, multitenant orchestration, cost and latency controls, and regulatory compliance. Key categories include agent frameworks (e.g., LangChain’s open SDKs and deployment tools for building, testing and observing LLM‑powered agents), autonomous agent runtimes (AutoGPT and similar projects for self‑hosted or cloud agent loops), enterprise orchestration platforms (Kore.ai and Yellow.ai for multi‑agent CX/EX workflows and channel orchestration), and visual/low‑code platforms (MindStudio, Relevance AI) that accelerate design and governance for non‑developers. Platform and model providers such as Mistral AI supply enterprise‑oriented models and production toolchains, while specialist infrastructure (Xilos) and automation‑centric systems (Adept’s ACT‑1) focus on visibility and interface‑level actions. Developer productivity and integration are supported by tools like GitHub Copilot; Observe.AI illustrates agent use in contact‑center voice and QA workflows. Practical choices hinge on scale, safety posture and ownership model: self‑hosted vs managed, fine‑tuning vs retrieval/embedding pipelines, and the depth of governance (role controls, audit trails, observability, sandboxing). The current imperative is operational reliability and security — instrumented testing, continuous monitoring of hallucinations and failures, and policy controls — rather than novelty, making platform selection a balance of developer flexibility, enterprise controls, and proven production observability.