This topic covers the growing set of AI agent toolkits, frameworks, marketplaces and automation platforms that consultants and professional‑services teams use to build domain‑aware assistants and multi‑agent workflows. Consultants increasingly combine retrieval‑augmented generation (RAG), stateful orchestration, model hosting, and workspace integration to automate research, client onboarding, proposal drafting, ongoing delivery and analytics. Key categories include agent frameworks (e.g., LangChain and LlamaIndex) for building, debugging and scaling document and task agents; AI automation platforms and managed ML services (Vertex AI, IBM watsonx Assistant) that provide hosting, fine‑tuning, monitoring and enterprise controls; and workspace/integration platforms (Notion) that centralize knowledge, templates and automations. Developer‑centric tools such as Windsurf (formerly Codeium) and JetBrains AI Assistant accelerate code, plugin and connector development. Conversational and foundation model options (Anthropic’s Claude family) supply the assistant layer, while market consolidation—illustrated by the June 2025 acquisition of Shape AI—reflects vendor consolidation and shifting product footprints. For professional services the practical implications are straightforward: agent frameworks enable repeatable, auditable pipelines for client data and RAG; managed platforms reduce operational burden and add governance; marketplaces and prebuilt toolkits shorten time to value. Current trends to watch include emphasis on stateful orchestration (LangChain/LangGraph patterns), enterprise security and model governance, tighter workspace integrations for knowledge continuity (Notion), and multi‑model toolchains that mix hosted models with specialist agents. Evaluating toolchains now requires balancing developer control, operational maturity, and compliance needs rather than feature checklists alone.