This topic covers the tools, frameworks and governance needed to build autonomous AI agents for spacecraft and mission operations. It spans on‑board agents that handle navigation, fault recovery and vision processing; ground‑side orchestrators that plan and coordinate multi‑agent workflows; and marketplaces and developer tools that accelerate mission‑specific agent construction. The subject is timely as growing commercial small‑sat constellations, increased mission complexity and stronger LLM capabilities have pushed autonomy from research into operational prototyping. Key categories include agent frameworks (LangChain for building, observing and deploying LLM‑powered agents), agentic systems that act inside software interfaces (Adept/ACT‑1), and enterprise assistant platforms that enable no‑code or developer‑driven multi‑agent orchestration (IBM watsonx Assistant, Kore.ai, MindStudio). Developers rely on AI‑native IDEs and in‑IDE copilots (Windsurf, JetBrains AI Assistant, Tabby, Aider) to prototype and maintain mission code, while LLM providers like Cohere supply private, customizable models and embeddings for constrained or classified environments. Governance and auditability are covered by platforms such as Monitaur, which centralize policy, monitoring and vendor validation for regulated deployments. Operational considerations emphasized by current tooling trends include edge AI vision processing for real‑time perception, hybrid cloud/edge orchestration to balance latency and compute limits, strict verification and observability for safety‑critical behaviors, and curated marketplaces for reusable mission agents and connectors. Practitioners should plan for constrained hardware, radiation and communications limits, formal testing and policy enforcement rather than black‑box deployment. Together these tool categories form a practical stack for prototyping and maturing autonomous agents for space missions while meeting operational and regulatory requirements.