Bringing new tool use advancements to life: Claude Plays Pokemon

Introduction and Demo Start 00:06

• The presenter, David, introduces himself as the creator of Claude Plays Pokemon.
• The session is focused on advancements in tool use, particularly related to Claude's ability to play Pokemon.
• He launches a live demo of Claude playing Pokemon, engaging the audience in a countdown.

Advances in Tool Use and Agent Capabilities 01:13

• Highlights new capabilities in the models that enable improved tool use and agentic behavior.
• Extended thinking mode allows Claude to better plan and adapt, particularly evident during challenging scenarios like the Pokemon name entry screen.
• The model now builds comprehensive plans and can reconsider its assumptions between tool calls.
• Parallel tool calling allows the model to call multiple tools at once, improving efficiency compared to previous versions that could only call one tool at a time.
• Faster action-taking results from multiple simultaneous tool calls, making agents more efficient.

Evolution of Tool Use in AI Agents 03:13

• Tool use has shifted from simple calculators to enabling complex agentic behaviors.
• In agentic loops, the model plans an action, executes, learns from the outcome, and repeats until the goal is met.
• Practical Pokemon example: pressing buttons, reflecting on outcomes, and updating plans accordingly.
• Improved planning and acting in the new models are possible due to extended thinking and parallel tool calls.

Interleaved Thinking and Real-Time Adaptation 04:42

• Previously, models would draft inadequate initial plans and struggled with unexpected scenarios (e.g., cursor movement on the name screen).
• With extended thinking, Claude can catch and adapt to errors during tool use in real time (e.g., realizing how cursor wrapping works).
• The model's adaptability during execution is emphasized as crucial for building robust agents.

Efficiency Gains from Parallel Tool Calling 06:21

• Parallel tool calling mainly improves efficiency by reducing wait times between tool actions and updates.
• The model can now perform in-game actions and update knowledge bases concurrently, streamlining agent performance.
• For developers, this means quicker and more effective agents, enhancing end-user experience.

Training for Easier and Smarter Agent Use 07:13

• Ongoing improvements aim to make Claude smarter over long tasks and easier to use as an agent.
• Enthropic listens to developer feedback and iterates on model design, with a focus on practical features like parallel tool calling.
• Extended thinking and usability advancements are continually integrated based on user needs.

Q&A: Tool Hierarchies and Agent Design Patterns 08:09

• Discussion of high-level vs. low-level actions and how tools can be structured (flat vs. hierarchical).
• In practice, separating tool purposes and clearly defining scenarios for their use leads to better agent outcomes.
• Observing agent struggles informs better tool and prompt design.

Q&A: Tool Definitions and Prompting Strategies 10:04

• Addressing where best to define tool usage guidelines: prompt vs. tool description.
• Both locations are effective; clarity and detailed descriptions are most important.
• Having consistent formats (like JSON schema) can help, but main consideration is that the model understands and applies the tool as intended.

Claude's In-Game Performance and Planning Improvements 13:29

• Claude Opus shows significant improvements in planning and executing multi-step game objectives.
• Although some visual interpretation limitations persist (e.g., game screen navigation), task planning and sustained attention have greatly improved.
• Notable achievements include successfully accomplishing complex in-game quests over long timeframes.

Q&A: Parallel Tool Calling Implementation and Limitations 15:24

• Parallel tool calling is not entirely novel, but is a valuable addition for practical usage.
• The model may now return multiple tool calls in a single API call, which must then be handled by the developer's system.
• Discussion of how excessive or poorly timed parallel actions (e.g., spamming 'A' in dialogues) can lead to side effects that developers must anticipate and mitigate with careful prompting.

Q&A: Handling Large Toolsets and Instruction Consistency 18:34

• Opus models are more reliable at following complex instructions and handling large toolsets (50-100 tools or more).
• Precise and well-considered instructions are critical, as the model will closely follow whatever it's given—even if contradictory or unclear.
• Success with many tools depends on clear boundaries between tool definitions and accuracy in prompts; well-designed tools yield consistent results across a broad set.

Conclusion 19:54

• The session concludes with gratitude to attendees and a note that the discussion diverged from expectations but provided valuable insights into Claude's tool use and agentic capabilities.