"The AI Agents course provided an outstanding introduction to automation using modern AI technologies. The trainer explained complex concepts clearly and demonstrated practical use cases that could be implemented within our organisation. The hands-on exercises helped us understand how AI agents can support business processes and improve operational efficiency. We left with a clear roadmap for future automation initiatives."
Daniel Foster
Solutions Architect
Technology Services Provider
What makes an agent different from a chatbot:
planning, memory, and tool use explained with live examples showing the difference in real output
API setup lab: keys, SDKs, environment configuration, and your first completion call running in under 30 minutes
Tool use deep dive: defining functions, passing schemas, and handling model-initiated calls with worked examples
Building the agent loop: how to parse model decisions and route to the right tool at each cycle of execution
Error handling workshop: timeouts, token limits, malformed responses, and fallback strategies tested against a live system
Data integration lab: agent reads from a database, calls an external API, and writes a structured result end to end
Microservice wiring: wrapping your agent behind a REST endpoint with authentication and rate limiting in place
Observability setup: structured logging, trace IDs, and a simple monitoring dashboard showing agent activity
Edge case testing: prompts designed to break your agent and practical techniques to harden against them
Deployment lab: containerise, push, and run your agent in a staging environment with health checks active
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Developers and Engineers |
"The AI Agents course provided an outstanding introduction to automation using modern AI technologies. The trainer explained complex concepts clearly and demonstrated practical use cases that could be implemented within our organisation. The hands-on exercises helped us understand how AI agents can support business processes and improve operational efficiency. We left with a clear roadmap for future automation initiatives."
Daniel Foster
Solutions Architect
Technology Services Provider
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This hands-on course teaches how to design and build intelligent AI agents that can plan, use tools, and interact with external systems.
Participants will learn what distinguishes agents from chatbots through real examples involving memory, planning, and tool execution.
The course covers setting up APIs, SDKs, and environments to quickly build and run functional agent-based applications.
Learners will implement tool use, function calling, and execution loops that allow agents to make decisions and perform actions.
Practical labs include integrating databases, external APIs, and building end-to-end workflows powered by agent reasoning.
The course also focuses on deploying agents as secure microservices with observability, logging, and error-handling in place.
By the end of the course, participants will be able to build, test, and deploy production-ready AI agents with robust real-world capabilities.
AI Agent Development is the process of creating AI-powered systems that can reason, make decisions, use tools, access data, and complete tasks with minimal human intervention. Unlike traditional software that follows predefined rules, AI agents use Large Language Models (LLMs) and external tools to analyse information, plan actions, and achieve goals.
Modern AI agents can search databases, access APIs, generate reports, automate workflows, and collaborate with users to solve complex business problems. AI Agent Development typically involves technologies such as OpenAI, Anthropic Claude, LangChain, Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and vector databases.
Traditional chatbots typically follow predefined rules and scripted conversation flows. They respond to user inputs based on patterns, keywords, or fixed decision trees.
AI agents are more autonomous and capable of reasoning, planning, and taking actions. They can use external tools, access business systems, retrieve information, and complete multi-step tasks to achieve specific objectives.
For example, a chatbot may answer questions about a company policy, while an AI agent could locate the policy, summarise it, update related records, notify stakeholders, and generate a report based on the outcome.
Agentic AI refers to artificial intelligence systems that can independently pursue goals, make decisions, and execute tasks with limited human supervision. These systems use reasoning, planning, memory, and tool usage to solve problems and adapt to changing circumstances.
Agentic AI is increasingly used in business automation, software development, customer service, research, and decision-support applications where tasks involve multiple steps and dynamic decision making.
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