What a Good AI Agents Workshop Should Cover
A workshop worth your team's time covers the full path from a first agent to one you would trust in production. Concretely, that means five areas: agent architecture (what an agent is and how to structure one), tools and function calling (how the agent acts on the world), memory and retrieval (how it stays grounded and remembers), orchestration (how multiple steps or agents coordinate), and evals and guardrails (how you know it works and keep it safe). Just as important as the topics is the delivery: every area should be hands-on in your own stack, not slides, so the team leaves with working code rather than notes.
I'm Mahmoud Zalt, an AI systems architect. When I design a workshop through Sista AI, this is the ground I make sure a team covers before they take agents anywhere near production.
The Five Areas, and Why Each Matters
Skipping any one of these is where teams get burned later. Here is what each covers and the failure it prevents.
- Agent architecture. The shape of an agent: the loop, the model, the tools, the state. Without this the team builds something that works once and cannot explain why it broke.
- Tools and function calling. How an agent does anything beyond talk. This is where most real bugs live, because the model calls a tool with input the tool did not expect. A workshop should drill the patterns that make tool use reliable.
- Memory and retrieval. How the agent stays grounded in real data and remembers across a task without overflowing its context. This is where retrieval, often called RAG, earns its place, and where teams learn when they do not need it.
- Orchestration. How steps chain and how multiple agents divide work without stepping on each other. The difference between a demo and a system.
- Evals and guardrails. How to measure whether the agent actually does its job, and how to stop it doing harm. A team that cannot evaluate an agent cannot responsibly ship one.
What Separates a Real Workshop From a Talk
The topics above are only half of it. The delivery decides whether they stick.
- Hands-on working sessions. The team builds each concept, hits the real walls, and clears them with a senior facilitator in the room. This is the part a video cannot replace.
- Your own stack. Building against your tools and constraints means the lessons transfer directly. A neutral sandbox teaches the idea; your codebase teaches the job.
- A custom curriculum. A team already running agents needs different depth than one starting out. The syllabus should bend to the team, not the reverse.
- A reference repo the team keeps. A known-good example built during the session, so the workshop lives on as something to copy from.
- A follow-up window. The best questions surface a week later, when people apply the material to real work. A workshop that closes the door the moment it ends leaves value on the table.
Frequently Asked Questions
What topics should an AI agents workshop cover?
Agent architecture, tools and function calling, memory and retrieval, orchestration, and evals and guardrails. Together these span the path from a first agent to one you can trust in production, and each should be taught hands-on rather than through slides.
Should a workshop cover RAG and memory?
Yes. Memory and retrieval, often called RAG, is one of the five core areas. A good workshop covers how an agent stays grounded and remembers across a task, and just as usefully, when a team does not need retrieval at all.
How technical should an AI agents workshop be?
For an engineering team, technical enough to build. The value is in hands-on working sessions in your own stack, so the team writes real code, hits real failure modes, and keeps a reference repo, rather than watching a demo.
How is the curriculum decided?
It is custom. The syllabus is shaped to your team's level and goals, so a group already running agents goes deeper on orchestration and evals, while a team starting out spends more time on architecture and tools.
Building the Right Workshop for Your Team
A strong AI agents workshop covers architecture, tools, memory and retrieval, orchestration, and evals and guardrails, and it teaches all of it hands-on in your own stack with a reference repo the team keeps. Miss an area, and it tends to resurface as a production incident.
If you want a session built around exactly these areas for your team, my Workshop and Training service designs a custom curriculum with a senior facilitator, delivered remote, on-site, or hybrid, with a follow-up window. Tell me your team's level, and I will map the syllabus to it.
