The AI Native Dev - from Copilot today to AI Native Software Development tomorrow Building an AI Agent in 100 Lines of Code | Yaniv Aknin
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Dec 16, 2025 Yaniv Aknin, a founding engineer at Tessl, shares his insights on enhancing AI agents through effective design and context. He explains the significance of built-in instructions that influence agent behavior and performance. The conversation dives into the surprising effectiveness of a 100-line Python nano agent and contrasts the tool philosophies of Codex and Claude. Yaniv also discusses how subagents improve flexibility and proposes innovative evaluation methods for agent performance. Prepare for a deep dive into the future of AI development!
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Small Agent, Big Results
- You can build a working coding agent in under 100 lines of Python that completes real tasks like a to-do app.
- Minimal agents perform respectably on benchmarks, showing tooling and context aren't the only drivers of capability.
Align Your Context With The Agent
- Understand the built-in system prompt and tool descriptions before adding your own context.
- Make your external context compatible with the agent's internal instructions to avoid conflicts.
System Prompts Are Substantial
- Flagship agents embed large system prompts: roughly 10–15 KB each for Codex, Claude, and Gemini.
- These built-in prompts shape agent behavior significantly before any user input arrives.