Highlights
- Understand agents versus simple completions
- Authenticate and call AI APIs
- Design multi-step agent loops
- Handle errors and rate limits
- Build a tool-use agent
- Chain multiple API calls
- Integrate agent into existing service
- Implement logging and observability
- Test against edge cases
- Deploy agent to staging environment
Course Details
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
Who should attend
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Developers and Engineers |
Feedback
4.8 out of 5 average
⭐⭐⭐⭐⭐ 5.0/5
"The course moved far beyond basic AI usage and demonstrated advanced techniques that can genuinely transform productivity. We learned how to structure complex prompts, automate documentation tasks, analyse information more effectively, and integrate AI into existing workflows. The practical nature of the course ensured that everyone could immediately apply what they learned."
David Wilson - Lead Systems Engineer - Engineering Consultancy