Useful Resources for AI Agents

Building effective AI agents requires understanding frameworks, best practices, and engineering principles. Here are two essential resources for anyone working with AI agents.

Anthropic Engineering Blog

The Anthropic Engineering Blog provides battle-tested insights from a team building production AI systems at scale. The blog covers topics ranging from agent architecture and tool use to security best practices and context engineering. Posts are technical, practical, and often include code examples and real-world case studies from systems used by millions.

DSPy

DSPy is a declarative framework that shifts AI development from brittle prompt strings to structured, modular programs (see my blog post on DSPy for a deeper dive). Instead of manually engineering prompts, DSPy enables you to define what you want (signatures) and automatically optimizes how to achieve it through its built-in optimizers. The framework includes powerful agent modules like ReAct and ChainOfThought, making it easy to build and optimize agent systems that are portable across different models.

MLflow GenAI

MLflow GenAI provides an end-to-end platform for tracking, evaluating, and optimizing GenAI applications and agent workflows. It offers comprehensive observability through OpenTelemetry-compatible tracing that captures your app’s entire execution, including prompts, retrievals, and tool calls. The platform includes LLM-as-a-judge metrics for assessing GenAI quality, a Prompt Registry for versioning and managing prompt templates, and agent versioning capabilities that complement Git for full lifecycle management. MLflow’s framework-agnostic design and open-source nature make it a flexible choice for production GenAI systems without vendor lock-in.