Tereza Tizkova works on startup growth and teaches how coding agents execute software. Her work includes growth at Factory and E2B, a DeepLearning.AI course, and a public directory of AI agents.[1][2]
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Tizkova’s portfolio lists growth work at Factory and E2B alongside developer events, writing and teaching. Her essays and talks cover how computer-use agents work and how software development changes when agents can carry out more of the process. She also writes about mathematics, including topology and Gödel’s incompleteness theorems.[1]
Contributions
Agents that write and run code
With Francesco Zuppichini, she teaches Building Coding Agents with Tool Execution. The course moves beyond selecting predefined functions: an agent writes Python, executes it, observes errors and revises its next step. Students compare local execution, containers and isolated cloud sandboxes before building a data analyst and a full-stack application agent. Separating the execution environment from the model helps make the risks and capabilities of generated code explicit.[2]
A map of agent projects
Her portfolio links Awesome AI Agents, an open-source directory maintained under E2B. It collects examples of agent systems rather than presenting one universal agent architecture. The directory complements her teaching by giving builders concrete projects to inspect after learning how an agent can use an execution environment.[1][3]
Tools, writing & talks
Writing, research & talks
Start here
Building Coding Agents with Tool Executiondeeplearning.aiA practical course covering code execution, feedback loops and isolated environments for agents.
Take the course if you know Python and have used a language model, but want to understand the execution loop inside a coding agent. Start with the execution-environment lesson before running generated code against your own files.
Co-instructor[2]Factory's Tereza Tížková defines a software factory as the full autonomous lifecycle, not a swarm of agents, detailing sequential worker agents with fresh context and a deferred context engine cutting token use over 50%.
Catalog workOriginal source
Tools
- Awesome AI Agents
github.comAn open-source collection of agent projects linked from her portfolio.