Cover Image for How advanced tool calling transforms agentic use cases: A conversation with Moonshot AI
Cover Image for How advanced tool calling transforms agentic use cases: A conversation with Moonshot AI
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How advanced tool calling transforms agentic use cases: A conversation with Moonshot AI

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Join Feihu Tang from Moonshot AI team and Zain Hasan from Together AI for a technical session on how the Moonshot team post-trained Kimi K2 Thinking for agentic tool calling interleaved with its reasoning traces — and how to actually run it on Together.

Most models “think” and then make one tool call, then think again. This forces you to build complex orchestration layers with many API calls for each task. For real-world tasks like deep research, multi-step planning, or anything requiring multiple sequential tool calls, this architecture becomes brittle fast.

Kimi K2 Thinking takes a different approach: It can make 200–300 tool calls while it’s thinking, significantly simplifying complex tasks. This session will explain their full agentic post-training pipeline, why it’s different from earlier OSS releases from the likes of DeepSeek and Qwen and how to harness it to power your own agentic applications.

This is a learning-first webinar — great for folks building and scaling LLM-powered apps, tools and agents who need to understand what’s different about this architecture and how to use it in production

What we’ll cover

  • Why tool calling is the bottleneck for agentic apps: the limitations of single-turn workflows and why they force you into complex orchestration patterns that don't scale

  • Why this model is special: what “tool calling inside the thinking trace” actually means, and why K2 doing 200–300 tool calls in one run is so powerful.

  • How Moonshot  trained it: what Moonshot learned from earlier releases and how they built out their agentic post-training pipeline for tool calling.

  • Agentic workflows in one call: examples of tasks that used to need multi-step orchestration (research, planning, tool fan-out) and how K2 can keep that inside the model loop.

Live demo on Together AI: “before/after” comparison showing a regular thinking model vs Kimi K2 Thinking with interleaved tool calling on the same problem.

Register for a recording of the talk!

⚠️ Note - Only company (non-Gmail etc.) emails will be accepted. Please provide your valid corporate email upon registration!

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