Agentic Systems

Table of Contents Introduction From Chatbots to Agentic Systems Liquid Neural Networks: A Primer 3.1 Historical Context 3.2 Core Mechanics 3.3 Why “Liquid” Matters Open‑Source Landscape for Liquid Neural Networks Designing Agentic Workflows with Liquid NNs 5.1 Defining the Agentic Loop 5.2 State Representation & Memory 5.3 Action Generation & Execution Practical Example: Autonomous Data‑Enrichment Pipeline 6.1 Problem Statement 6.2 System Architecture 6.3 Implementation Walk‑through 6.4 Running the Pipeline Evaluation: Metrics and Benchmarks Operational Considerations 8.1 Scalability & Latency 8.2 Safety & Alignment 8.3 Monitoring & Observability Challenges, Limitations, and Future Directions Conclusion Resources Introduction Artificial intelligence has long been synonymous with chatbots—systems designed to converse with humans using natural language. While conversational agents remain valuable, the AI community is rapidly shifting toward agentic workflows, where autonomous agents not only talk but act in dynamic environments. These agents can plan, execute, and adapt without explicit human supervision, opening doors to applications ranging from automated DevOps to self‑optimizing recommendation engines. ...

Introduction The convergence of distributed intelligence, agentic systems, and mesh networking is reshaping how modern applications communicate, make decisions, and adapt to change. From autonomous vehicle fleets to industrial IoT (IIoT) deployments, thousands of intelligent agents now collaborate over dynamic, peer‑to‑peer topologies. While this architectural shift unlocks unprecedented scalability and resilience, it also expands the attack surface: each node becomes a potential entry point, and traditional perimeter‑based defenses quickly become obsolete. ...

Table of Contents Introduction From Chatbots to Agentic Systems What Are Large Action Models (LAMs)? 3.1 Definition and Core Idea 3.2 Architectural Foundations 3.3 Key Open‑Source Projects Core Components of an Agentic Workflow 4.1 Planner 4.2 Executor 4.3 Memory & State Management 4.4 Tool Integration Layer Hands‑On Example: Automated Ticket Triage 5.1 Problem Statement 5.2 Setting Up the Environment 5.3 Implementation Walk‑through Best Practices for Robust Agentic Systems 6.1 Prompt Engineering for Actionability 6.2 Safety, Alignment, and Guardrails 6.3 Observability & Monitoring Real‑World Deployments & Case Studies Challenges, Open Questions, and Future Directions Conclusion Resources Introduction The past few years have witnessed a seismic shift in how we think about conversational AI. Early chatbots—rule‑based or narrowly scoped language models—were primarily designed to answer questions or follow scripted dialogues. Today, a new generation of Large Action Models (LAMs) is emerging, enabling agentic workflows that can plan, act, and iterate autonomously across complex toolchains. ...

In the rapidly evolving world of LLM agent architectures, the Model Context Protocol (MCP) has emerged as a game-changing standard for enabling seamless, dynamic interactions between AI models and external tools. This comprehensive tutorial takes you from zero knowledge to hero-level implementation of MCP Tool Discovery—the mechanism that powers intelligent, scalable agentic systems. Whether you’re building production-grade AI agents, enhancing IDEs like VS Code, or creating Claude Desktop extensions, mastering tool discovery is essential for creating truly autonomous LLM workflows.[1][7] ...