Europe 2026
Skill issue: Lessons from skilling up coding agents to use Langfuse - Marc Klingen, Clickhouse
Overview
This talk explores the challenges and lessons learned in skilling up coding agents to effectively use tools like Langfuse. It highlights the evolution from complex workflows to more autonomous agents and emphasizes the role of formalized skills as shortcuts for reliability. The core thesis is that by leveraging tracing and structured skills, developers can significantly improve agent capabilities and streamline the integration of complex tools into AI applications.
Who should watch
- AI Engineers looking to integrate tools and libraries into agentic workflows.
- Product Managers seeking to understand how agents can simplify complex development tasks.
- Builders experimenting with agent capabilities and seeking best practices for tool usage.
- Developers facing challenges with outdated training data or non-optimal agent setups.
- Anyone interested in improving agent reliability and efficiency through formalized skills.
Key takeaways
- Skills act as formalized shortcuts, making agent interactions more reliable and efficient, akin to following a manual for a complex task.
- Tracing agent execution is crucial for identifying unexpected use cases and for improving existing skills by revealing runtime behavior.
- Integrating tools like Langfuse into agent workflows can be hampered by outdated training data and a lack of guidance on optimal setup.
- Exposing structured information, such as an agent sitemap, helps agents navigate documentation more effectively than broad web searches.
- A dedicated search endpoint for documentation allows for tracking user queries, identifying knowledge gaps, and improving documentation content.
- Even basic evaluation setups are beneficial for measuring agent performance, and these can be implemented using natural language statements evaluated by an LLM.
- Dynamic content used by skills should be directly referenced rather than cached to avoid staleness and duplication of information.
- Auto-research, guided by a well-defined target function, can significantly accelerate the exploration of agent improvements and new patterns.
Notable quotes
*Skills was like a great way once you get the manual, it's easy. You just need to follow the manual, and you can solve a Rubik's Cube.*
*Looking at traces still like gets you to like 80% of the detail.*
*The target function really matters.*
Unofficial community note. Prefer the recording for nuance.