Stop re-explaining your company to AI agents
Pydantic and Dosu on company knowledge, agent memory, and reliable internal workflows. Build internal agents that know how your company works: choosing a useful workflow, keeping their knowledge current, and using traces and evals to judge the results.
Pydantic and Dosu on company knowledge, agent memory, and reliable internal workflows.
Overview
- Live workshop
- Online event
- Free
Speakers
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Samuel Colvin Founder & CEO · Pydantic Creator of Pydantic, the most widely used data validation library for Python. Building developer tools for the Python and AI ecosystem. -
Douwe Maan Lead Developer, Pydantic AI · Pydantic Pydantic AI Lead Engineer.
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Devin Stein Founder & CEO · Dosu Founder and CEO of Dosu, which builds AI agents that answer engineering questions and keep project knowledge current. -
Taylor Dolezal Head of Open Source · Dosu Head of Open Source at Dosu, working on how maintainers keep project knowledge answerable. Previously Head of Ecosystem at the Cloud Native Computing Foundation. -
Laís Carvalho Developer Relations, host · Pydantic Growth marketing & Developer Relations at Pydantic. Passionate about Python, open source, and building great developer experiences.
About this event
Give your internal AI agents the knowledge they need to work inside your company. Whether they answer employee questions, investigate operational issues, or prepare finance reports, they need the right tools and current company knowledge. How do you provide that knowledge, keep it up to date, and measure whether it improves their work?
Join Pydantic and Dosu for a fireside chat about building internal agents for everyday work. Dosu builds agents with Pydantic AI and traces their runs with Pydantic Logfire. We will draw on that experience to discuss how teams build agent workflows, investigate failures, and maintain the company knowledge their agents use.
The conversation covers how to choose an internal workflow, where to put a human review step, and how to use traces to find the causes of failures. We will also get into what agents should remember between tasks and how to test whether that memory improves the results.
If you develop agents for your organization, or lead a team adopting them, join us to explore what to build, which knowledge to provide, and how to evaluate the work.
What you'll learn
- How to pick an internal workflow worth handing to an agent, from employee questions to operational investigations to finance reporting
- Ways to give an agent current company knowledge, and keep it current as the company changes
- Where a person should review the agent's work, and how to define that boundary
- Reading Logfire traces to find the cause of a failed agent run
- What an agent should remember between tasks, and how to test whether that memory improves results
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