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agents-towards-production

by NirDiamant

Code-first tutorials and blueprints for building production-grade GenAI agents

Jupyter Notebook
Updated Feb 3, 2026
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Overview

Teaches end-to-end, code-first patterns for taking GenAI agents from prototype to production through guided notebooks and reusable blueprints. Walks through each layer—design, orchestration, testing, deployment and observability—with runnable examples and proven practices. Includes practical patterns for agent delegation, tool integration, and failure-mode handling that you can adapt to real systems. Hierarchical Multi-Agent Pattern and MCP Pattern provide structural templates for coordinating complex agent activity and tool usage.

Key Benefits

As teams build multi-agent systems, having concrete, production-proven patterns reduces risky guesswork and brittle architectures. These tutorials surface evaluation and operational patterns (testing, logging, delegation strategies) that are essential for assessing agent reliability and building agent track records. Until teams adopt consistent production practices, agent-to-agent evaluation and trust remain ad-hoc—these blueprints make those practices accessible. A2A Protocol Pattern

Ideal For

Engineers and ML/Ops teams who need practical, runnable guidance to move multi-agent prototypes into production safely. Defense in Depth Pattern

Real-World Examples

  • Implementing delegation and orchestration patterns for multi-agent workflows
  • Adopting repeatable pre-production testing and evaluation practices for agents
  • Building observability and failure-mode handling into agent runtimes
Works With
langchainopenaihuggingface
Topics
agentagent-frameworkagentsai-agentsgenaigenerative-aillmllmsmlopsmulti-agent+3 more
Similar Tools
autogenlangchain
Keywords
multi-agent orchestrationmulti-agent trustagent-to-agent evaluationproduction agents