You have built your first AI agent.

What you have learned

You now have an honest, working understanding of:

• The definition of an AI agent and how it differs from a chatbot, a script, and a traditional automation.
• The think-act-observe loop that every modern agent runs, including the ReAct pattern published by Yao and colleagues in 2022.
• Tools and function-calling: what they are, how they work, and how to pick the right minimum set.
• Reactive versus planning agents, and the hybrid pattern most production systems use.
• Five common failure modes and the guardrails that mitigate them.

And you have done all of it hands-on. You ran the loop yourself. You gave an agent tools and watched it use them. You planned a project and watched the agent execute the plan. You wrote a governance plan for an agent of your own. That is a substantial bit of practitioner skill.

What this course did not cover

This is a deliberately small introduction. Below are the things we did not cover, which would take you from "I understand agents" to "I can deploy an agent to production".

• Real code. Everything was in the browser. To build a production agent you need Python or TypeScript, a framework like LangGraph or the Anthropic / OpenAI SDK, and an environment to run it in.
• Evaluation. How do you know if your agent is good? Evaluation of agents is its own specialty, with techniques like LLM-as-judge, gold-set replay, and adversarial probing.
• Memory. Real agents need to remember things across sessions and across long contexts. We did not touch the design space of agent memory: episodic, semantic, working, and procedural.
• Multi-agent systems. Several agents collaborating, each with its own role. This is increasingly how complex tasks are handled, and it has its own failure modes.
• Production engineering. Monitoring, alerting, cost management, security review, prompt versioning, A/B testing, rollback. The boring but essential parts of operating an agent.
• Domain-specific applications. Coding agents, research agents, customer service agents, sport analytics agents — each domain has its own patterns and pitfalls.

Where the Integrated AI Program takes this further

Romeo Advanced Academy runs an integrated, 180-ECTS programme in AI. The material in this free course corresponds to roughly the first three hours of a 150-hour journey. Below is where the relevant programme content sits.

• Tier 2 — C6: Generative AI and Foundation Models (6 ECTS). The technical depth on LLMs, prompting, in-context learning, fine-tuning, and the production stack around them.
• Tier 2 — C7: AI Ethics, Safety and Responsible AI (6 ECTS). The Lesson 5 material treated seriously: failure modes, mitigations, governance, audit trails, regulatory context.
• Tier 3 Path A — A2: Large Language Models (7.5 ECTS). Training, fine-tuning, retrieval-augmented generation, deployment patterns.
• Tier 3 Path A — A4: MLOps and Production ML Systems (7.5 ECTS). Evaluation, monitoring, cost management, A/B testing, the operational layer.
• Tier 3 Path A — A7: AI Security (6 ECTS). Adversarial ML, prompt injection, model security, tool security, the deeper version of Lesson 5.
• Tier 3 Path B — B6: AI Procurement, Vendor and Third-Party Risk (6 ECTS). For business and consulting practitioners — how to assess, buy, and govern agents you did not build yourself.

Most learners pursue this material as part of a Tier 2 + Tier 3 stack — Tier 2 for the foundations, Tier 3 for the specialisation that matches their work.

Stay in touch

If you found this useful, we would love to know who you are. The form below sends a short note to our admissions inbox; we will reply with more on the programme and, if you want, occasional updates on what Romeo Advanced Academy is publishing.

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What brought you to the course? I am exploring AI and wanted a hands-on intro I am a software engineer thinking about Path A I am a consultant or product person thinking about Path B I work in research and am interested in Path C I work in security and am interested in Path D I work in sport and am interested in Path E I am looking at this for my organisation, not myself Other Anything you want to tell us? (Optional)

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