The Sequential Agent Pattern
Chaining multiple specialised agents into a pipeline where each one builds on the last. Illustrated with a TypeScript CLI that fetches a quote, researches its author, and writes an inspiration card.
The Parallel Agent Pattern
When agents do not depend on each other, run them at the same time. Illustrated with a TypeScript translation pipeline built with Google ADK that translates a phrase into three languages simultaneously, then aggregates the results with Gemini.
The Single Agent Pattern
A look at the simplest agentic AI pattern: one model, one tool, zero orchestration. Illustrated with a tiny TypeScript agent built with the Google Agent Development Kit (ADK) that uses Gemini and Google Maps to review any place by name.
Agentic AI: Multi-Agent Systems and Task Handoff
The final article in the Agentic AI series explores multi-agent systems: how specialised agents collaborate through structured handoffs to complete complex user goals.
Understanding the Orchestrator-Worker Pattern
The orchestrator-worker pattern brings scalable structure to agentic AI workflows by cleanly separating high-level planning from specialised task execution. Through a practical trip planning example, this article demonstrates how LLMs can dynamically coordinate expert agents, grounded in schema-driven logic and real-world data.
Building with Reflection: A Practical Agentic AI Workflow
This article explores how to implement a reflection loop-an agentic AI pattern where a model generates, critiques, and iteratively improves its output - using image captioning as a practical example.
Routing: Building Step-by-Step AI Reasoning
Explore how to intelligently route AI queries using schema-guided function calling and contextual categorisation.
Prompt Chaining: Building Step-by-Step AI Reasoning
Learn how prompt chaining enables AI to tackle complex tasks through step-by-step reasoning, boosting both accuracy and interpretability.