In this episode, Sheamus McGovern sits down with João “Joe” Moura, founder and CEO of CrewAI, to discuss the rapid evolution of multi-agent AI systems and their real-world impact in enterprise environments. From his early experiments with agent-based automations to supporting clients like PwC, PepsiCo, and the U.S. Department of Defense, Joe shares how CrewAI is enabling companies to move beyond hype and flashy demos toward scalable, secure agent workflows. This conversation covers everything from open-source momentum to agent orchestration, governance, deployment challenges, and what it takes to go from prototype to production.

Key Topics Covered:

• João’s background in AI and the early days of CrewAI
• The problem CrewAI was originally built to solve—and how it scaled from a personal tool to a global platform
• Definition of a true agent: “Agency is the key. If there’s no decision-making, it’s not an agent.”
• The evolution to robust multi-agent enterprise systems
• CrewAI’s dual product strategy: open source for developers, enterprise-grade platform for production use
• Use cases driving the most traction: go-to-market workflows, internal GPTs, code automation at scale
• The MIT report on AI adoption: why many enterprises are failing to see ROI—and how CrewAI is addressing the gap
• Empowering non-technical users through CrewAI Studio
• Security, governance, and the importance of deterministic control in agent deployment
• Why waiting for the next-gen LLM is a mistake: “The models we have today are good enough for 99% of enterprise use cases.”
• CrewAI’s take on interoperability, agent-to-agent protocols, and why standards matter

Memorable Outtakes:

• “If you want an agent, you’ve got to have agency. Otherwise, it’s just a script.”
• “LLMs today are good enough for 99% of enterprise use cases.”
•  “We’re now running over 475 million agent automations a month.”

References & Resources:

João “Joe” Moura – LinkedIn https://www.linkedin.com/in/joaomdmoura/CrewAI https://www.crewai.com/

MIT Report on GenAI adoption (referenced in discussion) https://ide.mit.edu/research/95-of-generative-ai-projects-fail-how-to-make-yours-succeed

Humanity's Last Exam https://agi.safe.ai/

AgentBench⁠ https://github.com/THUDM/AgentBench⁠

HumanEval Benchmark⁠ https://github.com/openai/human-eval⁠

MCP – Model Coordination Protocolhttps://modelcontextprotocol.io/docs/getting-started/intro

Agent to Agent Communication https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/

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