Ollama Cloud is the primary inference platform for M2, Minimax, and Kimi Thinking models, enabling users to offload heavy compute workloads.

Kimi 2 is being used as a primary model capable of general web searches and deep agent reasoning, positioning it as a cost-effective alternative to GP...Show more

LangChain leverages this deep agent builder architecture as its secret source under the hood to power complex multi-step AI workflows.

The agent builder uses a Claude-inspired checklist planning tool to sequence and structure sub agent tasks effectively.

Langsmith may host the agent builder as a starter application that seamlessly transitions into a deployable AI agent workflow.

Position the agent builder as a starter app within Langsmith that users can iterate on, then export or convert into deployed agents with generated cod...Show more

Opening access of agent engineering tools to non-developers democratises the creation and iteration of AI agents beyond traditional developer roles.

Agent builders target automating tasks that developers typically wouldn’t bother building manually, even if they had the technical skills.

A simple yet effective architecture for deep agents consists of a planning tool (inspired by Claude checklists), a detailed and structured system prom...Show more

Offer a specialized package from the Build AI podcast focused on LangChain deep agents, compiling previous discussions and enhancements.

Illustrated the power of remote development by accessing a DGX Spark–hosted deep agent UI from a MacBook during a live session.

Deploy deep agent UIs on GPT OS 120 hosted on DGX Spark and access them remotely from a MacBook for flexible development.

Leverage NVIDIA DGX Spark hardware to run and showcase high-performance deep agents for demonstration and enterprise proof-of-concept.

Develop and market a packaged LangChain deep agents solution, leveraging past podcast discussions and feature highlights to attract developers.

Streamline complex multi-step processes by moving from manual chatbot interactions to fully automated workflows for improved efficiency.

Prioritize live UI demonstrations of deep agents on real hardware to validate capabilities beyond theoretical discussion.

Integrate research workflows with systematic tracking of feature and tool additions to ensure visibility and reproducibility in AI development.

Use an interactive UI as a reference point to iteratively research and develop deep agents, refining capabilities through successive demos.

There’s a market opportunity to build customizable AI workflow platforms that integrate seamlessly into individual users’ iterative cycles.

AI capabilities are nearly worthless without robust tooling to make them accessible and actionable for developers.