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Hey there, I'm Joe Moa and I have
something very exciting to share with
you today. We're launching a new course,
design, develop, and deploy multi- aent
systems with Cai on a partnership with
deep learning AI. And you don't want to
miss on this one because we're going to
talk about everything there is to know
about AI agents. AI agents have so much
potential to reshape businesses from
streamlining fraud detection in finance
to automatically processing product
returns on retail to analyzing customer
behavior in communications. If you
really think about it, the genie is not
getting back into the bottom. There's
not going to be a word where people are
going to use less AI agents. If
anything, it's going to be even more.
So, this can have a direct impact in
your career and how you're going to
build the systems in the future. I'm the
CEO and founder of Crayi, and my goal
for Crayi was to share an open-source
framework that is loved by AI builders
and trust by AI leaders. So, this course
is meant for everyone who wants to learn
how to build multi-agent systems. It's
one of the most important skills on AI
today. No matter your spot on the path
to production, this course is for you.
It's not enough to just build something
fast. You want to make sure that
something that is trust worth it so that
you can manage and scale it. And in this
course, we're going to talk about all
the fundamentals from agents to tasks to
common communication patterns and even
how to configure these agents to use
tools, web search or MCP. Memory is a
key piece and we're going to dive deep
into how that works and how that helps
with your agents, including guard rails
and flows when you want to have that
low-level control. Finally, you make
your agents ready to serve thousands of
customers using sophisticated techniques
like tracing to track error sources or
hooks to set up and clean up executions
and evaluation to track progress over
time. You're going to hear directly from
people that are building the systems in
production today. I'm very excited to
invite some of friends and customers
that have been building with curi in
production to tell you all about it,
what works and what it doesn't and how
you should be thinking about AI agents.
I will share my personal experience on
thinking about this use cases and
actually building them. So this is a
unique opportunity. Make sure that you
enroll today and I hope to see you there
in a second.
Learn more: https://bit.ly/4ooiRUl Introducing "Design, Develop, and Deploy Multi-Agent Systems with CrewAI," taught by João Moura! AI agents leverage the power of Large Language Models (LLMs), but, as with all LLM-based tools, they struggle with reliability, coordination, and repeatability when deployed on complex workflows. AI agents build on these models to move from responding to prompts to acting autonomously, reasoning through tasks, and adapting to changing goals. Multi-agent systems extend this capability even further by distributing reasoning and responsibilities across specialized agents that can plan, collaborate, and improve together. While it’s never been faster to prototype a concept, many teams are still stuck at this prototype stage, where agents might run well at a small scale but fail under real-world conditions. In this course, you’ll bridge that gap by turning prototypes like an automated code reviewer, a meeting co-pilot, and a deep researcher into production-ready systems. You’ll use the CrewAI framework to apply methods that improve control, reliability, and scalability. Across four modules, you’ll: - Build AI agents using core the building blocks of memory, tools (including MCP servers), guardrails, and execution hooks. - Design and orchestrate multi-agent workflows using Flows and complex coordination strategies. In hands-on labs, create and refine crews for projects such as a deep researcher and a meeting co-pilot. - Add observability and evaluation through traces, testing with LLM-as-a-Judge techniques, and training with human feedback to monitor agent decisions, debug issues, and continuously improve performance. - Deploy and monitor agents safely in production, integrating zoom-in and zoom-out observability metrics, versioning your configurations, and scaling reliably with production-grade practices. By the end, you’ll know how to turn your agent ideas into scalable systems that are robust, observable, and ready for real-world use. Enroll now: https://bit.ly/4ooiRUl