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Agentic AI

11 articles tagged with Agentic AI.

  1. The Coordinator Agent Pattern

    When the shape of the request decides the shape of the work, let an LLM dispatch. An agent coordinator built with Google ADK that routes requests to sequential, parallel, and single sub-agent teams on the fly.

  2. A Local Managed Agent in Four Planes

    Anthropic decoupled their agent into brain, hands, and session. I built a local TypeScript version in roughly 2,400 lines to see what the architecture actually feels like when you type it out.

  3. The Loop Agent Pattern

    When one pass is not enough, let agents iterate. A playlist curator built with Google ADK that generates songs, verifies them against MusicBrainz, and refines until every track checks out.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. Routing: Building Step-by-Step AI Reasoning

    Explore how to intelligently route AI queries using schema-guided function calling and contextual categorisation.

  11. 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.