In the news
What Is Eval-Driven Development? How to Improve AI Agents
LangChain · Published · 3 min read
In 30 seconds
- What happened
- LangChain describes eval-driven development, a methodology for building AI agents by defining success metrics upfront and iteratively comparing experimental changes against consistent benchmarks.
- Why it matters
- Engineers building AI agents need this when manual testing becomes unreliable due to non-deterministic outputs and multiple valid answers, especially as agent complexity grows.
- Watch out
- Overfitting to evaluation datasets is a real risk; teams must maintain held-out test examples separate from tuning examples to verify genuine behavior improvement.
- agent
- eval
The patterns behind this
- Eval-Driven Development (Agent CI)
- Agentic Context Engineering (Evolving Playbook)
- 12-Factor Agent Methodology
Each one covers how the technique works, when it earns its cost, and where it breaks.
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