Proposed Duration
3 Days.
Level
Advanced
Course Overview
AI Agent Architect is an advanced course that helps learners move beyond isolated prompting and linear automation into intelligent agent design. The course explains how agents differ from one-step prompts and fixed workflows by introducing role-based behavior, tool use, context handling, task progression, handoffs, and governance controls. It also includes a practical OpenClaw installation and setup module so participants can understand the operational requirements needed before agent experimentation or deployment. The outline is grounded in current public OpenAI agent documentation and current OpenClaw installation and getting-started documentation.
Pre-requisite
Participants should already understand prompting, AI-assisted task design, and workflow logic. Prior exposure to automation platforms is strongly recommended. Learners should be comfortable with technical conversations about architecture, tools, orchestration, boundaries, command-line steps, and environment setup. Familiarity with Node.js concepts is helpful for the installation segment, although deep software engineering experience is not mandatory.
Target Audience
This course is intended for advanced practitioners, solution architects, automation leads, AI champions, technical trainers, developers, and technically inclined professionals who want to design, evaluate, and begin implementing agent-based systems for business use. It is especially suitable for teams moving from productivity-oriented AI usage toward orchestrated, tool-using assistants.
Course Objective
By the end of the course, learners should be able to explain the defining characteristics of AI agents, design focused agent roles, define tool and context boundaries, understand when to use one agent or multiple agents, prepare an OpenClaw environment for use, and prototype an agent-based solution with stronger governance and execution logic. The broader objective is to move learners from interest in agents toward disciplined design and responsible implementation.
Module 1: The Evolution from Prompting to Agentic Systems
This opening module establishes the learning progression from basic prompting to workflow automation and finally to agent-based systems. Participants examine why agents matter, what additional capabilities they introduce, and where they fit in modern operational design.
Differentiate prompting, workflow automation, and agent-based execution in practical business terms.
Examine the role of autonomy, tool use, state awareness, and multi-step task progression.
Identify business scenarios where agents are more suitable than manual prompting or static workflows.
Recognize the capabilities and limits of current agent systems.
Module 2: Agent Design Foundations and Architecture
This module develops the core design discipline required for effective agents. Participants learn to define the purpose, scope, behavior, and operating constraints of an agent before considering deployment.
Define a clear role, mission, and responsibility boundary for an agent.
Design instructions, constraints, expected outputs, and escalation rules.
Assign tools intentionally and avoid unnecessary capability expansion.
Understand the difference between local context, workspace context, and model context where relevant.
Module 3: Tools, Context Handling, Handoffs, and Multi-Agent Patterns
This module examines the operating mechanics of agents once they begin performing real tasks. Participants explore how agents call tools, manage context, pass work between specialists, and stay aligned during multi-step execution.
Study patterns of tool calling and action sequencing.
Manage context windows and select only the information that should travel with the task.
Design handoffs between a generalist agent and specialist agents.
Recognize when one complex agent should be split into a smaller multi-agent structure.
Module 4: OpenClaw Installation and Environment Setup
This new module gives participants a practical installation pathway for OpenClaw so they can prepare a usable environment before implementation exercises. The emphasis is not just on running commands, but on understanding prerequisites, verification steps, and configuration readiness.
Review current documented system requirements, including Node 24 recommended or Node 22.14+ supported, and support for macOS, Linux, Windows, and WSL2.
Walk through the recommended installer script for macOS, Linux, or WSL2 and discuss the Windows PowerShell installation path.
Understand onboarding options, including default onboarding and installation without onboarding.
Verify installation with command checks such as openclaw --version, openclaw doctor, and openclaw gateway status.
Introduce setup initialization using openclaw setup and onboarding using openclaw setup --wizard where appropriate.
Discuss common setup issues such as PATH problems, Node availability, workspace location, and Gateway readiness.
Module 5: Governance, Reliability, and Human Control
This module addresses the operational realities of deploying agent-based systems. Participants examine review points, fallback logic, minimum-permission thinking, and guardrails so that agent solutions remain controlled and trustworthy.
Design human oversight checkpoints for sensitive or irreversible actions.
Handle uncertain outputs, unexpected tool results, and recovery paths.
Apply guardrails, boundaries, and approval logic in agent design.
Balance autonomy with accountability in business environments.
Module 6: OpenClaw-Oriented Practical Application
This module connects the conceptual and technical material into an OpenClaw-aligned use case. Participants work through how an agent design could be translated into a usable solution structure, including task flow, tool access, review logic, and operational clarity.
Frame an OpenClaw-oriented use case or architecture discussion.
Map agent roles to business tasks, channels, and tool access.
Design an execution flow with review, control, and exception handling.
Refine the proposed design for clarity, feasibility, and maintainability.
Module 7: Capstone Agent Design and Deployment Readiness Review
The course concludes with a capstone exercise where participants design a realistic agent-oriented solution and present its architecture, installation assumptions, operational controls, and next-step readiness. This module turns theory into disciplined solution design.
Select a realistic business scenario for agent deployment.
Design the agent or multi-agent structure and justify the decisions made.
Present orchestration logic, tool boundaries, installation assumptions, and control model.
Review strengths, risks, and recommended next steps for pilot deployment.
Learning Outcomes
Differentiate clearly between prompts, workflows, and AI agents in a practical and professional context.
Design focused agent roles with explicit instructions, tool access, operating boundaries, and escalation logic.
Explain how context, handoffs, orchestration, and multi-agent patterns affect agent behavior.
Prepare and verify a basic OpenClaw installation environment using current public setup guidance.
Prototype an OpenClaw-aligned agent scenario with governance, review, and reliability considerations.
Evaluate whether a proposed agent solution is ready for experimentation, pilot testing, or further technical development.
Programme scope, delivery arrangements and suitability are confirmed during enquiry.