Recent Blog Posts
Insights on AI Agents, Modern Web, and the Future of Engineering.
Moving Beyond Monolithic Prompts: A Guide to Graph Engineering with ADK
Learn how graph engineering with Google ADK breaks down complex AI workflows into testable, production-ready systems using nodes, edges, and powerful design patterns.
Flue: The Open Agent Framework for Building Durable AI Agents
Explore Flue 2.0, the open TypeScript agent framework from the creators of Astro. Learn how Agent Hooks, durability, and a React-like API make building autonomous AI agents intuitive and powerful.
Building Modern Terminal UIs in Dart: dart_tui vs. commander_ui
A comprehensive guide to building interactive terminal interfaces in Dart using dart_tui and commander_ui frameworks
Why You Should Still Learn to Code in the Age of AI
With AI coding assistants on the rise, Stanford professor Chris Piech explains why learning to code remains essential for developing problem-solving skills and creative thinking.
Sharing State Across Agent Boundaries: Context Contracts
Define type-safe context contracts with PydanticAI to share validated state safely across multi-agent handoffs and boundary transitions.
Orchestrator-Worker Decomposition: Splitting Complex Tasks Across Agents
Decompose complex multi-step goals into structured worker sub-tasks using the Google Agent Development Kit (ADK) Orchestrator-Worker pattern.
Event-Driven Orchestration: Coordinating Agents over Message Buses
Build asynchronous, event-driven multi-agent systems using message buses like Kafka or RabbitMQ with LlamaIndex Workflows.
Resilience for Orchestrators: Retries, Timeouts, and Circuit Breakers
Build enterprise-grade resilience into multi-agent orchestrators using retries, per-task timeouts, and circuit breakers with the Microsoft Agent Framework.
Fan-Out/Fan-In: Parallel Execution in Multi-Agent Systems
Master the Fan-Out/Fan-In orchestration pattern in LangGraph to run concurrent worker agents in parallel and aggregate multi-agent insights.
Using MCP Tools with the OpenAI Agents SDK
Connect Model Context Protocol (MCP) servers seamlessly into the OpenAI Agents SDK for Python to power autonomous agent workflows with standardized tools.
Testing MCP Servers: Mock Clients and Schema Validation
Build robust unit and integration test suites for Model Context Protocol (MCP) servers using in-memory mock clients, JSON Schema validation, and pytest.
Versioning MCP Servers: Backward-Compatible Tool Interfaces
Maintain backward compatibility and manage API evolution when updating Model Context Protocol (MCP) tool schemas across multi-agent fleets.