Agentic Workflows

Table of Contents Introduction From Rule‑Based Chatbots to Agentic Systems What Are Liquid Neural Networks? 3.1 Core Concepts: Continuous‑Time Dynamics 3.2 Liquid Time‑Constant (LTC) Cells Why Liquid Networks Enable Agentic Workflows Open‑Source Implementations Worth Knowing Designing an Agentic Workflow with Liquid NNs 6.1 Defining the Agentic Loop 6.2 State Representation & Memory 6.3 Action Generation & Execution Practical Example 1: Real‑Time Anomaly Detection in IoT Streams Practical Example 2: Adaptive Customer‑Support Assistant Deployment Considerations 9.1 Hardware Acceleration 9.2 Model Versioning & Monitoring Performance Benchmarking & Metrics Challenges, Pitfalls, and Future Directions Conclusion Resources Introduction The last decade has witnessed a dramatic shift in how we think about conversational AI. Early rule‑based chatbots gave way to large language models (LLMs) that can generate human‑like text, and today we stand on the cusp of the next evolution: agentic workflows—systems that not only converse but act autonomously in dynamic environments. ...

The era of the “chatbot” is rapidly evolving into the era of the “agent.” For the past two years, the world has been captivated by Large Language Models (LLMs) that can write essays, debug code, and hold witty conversations. However, the limitation of these models has always been their isolation; they could talk about the world, but they couldn’t do anything in it. Enter Large Action Models (LAMs) and Agentic Workflows. We are currently witnessing a seismic shift from passive text generation to active task execution. With the recent explosion of high-quality, open-source LAMs and agent frameworks, the power to build autonomous systems that navigate the web, manage software, and orchestrate complex business processes is no longer restricted to Big Tech labs. ...

Table of Contents Introduction Understanding Agentic Workflows: Core Concepts Setting Up Your Development Environment Building Your First Agent: The ReAct Pattern Tool Integration and Function Calling Memory Systems for Stateful Agents Multi-Agent Orchestration Patterns Error Handling and Reliability Patterns Observability and Debugging Agentic Systems Production Deployment Strategies Advanced Patterns: Graph-Based Workflows Security and Safety Considerations Performance Optimization Techniques Conclusion Top 10 Resources Introduction Agentic workflows represent the next evolution in AI application development. Unlike traditional request-response systems, agents autonomously plan, execute, and adapt their actions to achieve complex goals. In 2026, the landscape has matured significantly—LLM providers offer robust function calling, frameworks have standardized on proven patterns, and production deployments are increasingly common. ...

This tutorial explains how modern AI agents (like those in Cursor, IDE copilots, and autonomous coding tools) create, maintain, and execute to-do lists — and how you can build the same capability from scratch to production. This is not a UX trick. A to-do list is the core cognitive control structure that turns a language model from a chatty assistant into an agent that finishes work. 1. Why To-Do Lists Matter for Agents Large Language Models (LLMs) do not naturally: ...

Introduction Agentic workflows move AI beyond one-shot prompting into iterative, autonomous problem-solving by letting agents plan, act, observe, and refine—much like a human tackling a complex task. This shift yields more reliable, adaptable, and goal-directed systems for real-world, multi-step problems. In this article I explain the five core agentic workflow patterns (Reflection, Tool Use, ReAct, Planning, and Multi-Agent), show how they combine, give practical implementation guidance, example architectures, and discuss trade-offs and evaluation strategies. ...