Building Production-Ready AI Agents: The 4Ds for Scalable Python Systems
Most AI agent tutorials end with a working demo, but production systems require fundamentally different design patterns. This hands-on workshop teaches the 4Ds framework for building AI agents that actually scale: Distributed, mostly Deterministic, Durable, and Debuggable.
You'll start by running a typical "demo-ware" document classifier that breaks under real-world conditions—rate limits, network failures, memory constraints, and concurrent requests. Then we'll systematically rebuild it using production patterns.
Distributed: Decompose AI workflows into parallelizable units using Hatchet's task orchestration. Transform sequential document processing into concurrent operations that scale horizontally.
mostly Deterministic: Constrain LLM outputs using Pydantic schemas and structured prompts. Build predictable fallback mechanisms for low-confidence results, turning non-deterministic AI into reliable system components.
Durable: Implement automatic retry logic and stateful checkpoints that survive failures without losing progress. Your workflows recover gracefully from API timeouts, service restarts, and infrastructure issues.
Debuggable: Add comprehensive observability through structured logging, distributed tracing, and real-time metrics. Watch your AI agents execute with full visibility into performance bottlenecks and failure patterns.
Each principle builds on practical Python code you'll run locally against cloud infrastructure. By the workshop's end, you'll have a complete production-ready document classification system processing hundreds of documents in parallel, with automatic recovery and full observability.
This is an infrastructure-focused training for Python developers who need to deploy AI agents that actually work in production. You'll leave with patterns immediately applicable to context/RAG systems, document processing pipelines, and multi-step AI workflows.
Prerequisites: Python 3.11+, basic asyncio knowledge, laptop with internet access.