Data governance isn't sexy, but it's what makes or breaks your AI strategy. In this episode, Sam and Mac tackle the tactical reality of what happens inside companies trying to comply with AI regulations while keeping data governance practices intact.

What you'll learn:

• Why you can't have compliant AI without proper data governance
• Data lineage: tracking where your data came from, how it's processed, and where it ends up
• Real-world bias example: How historical hiring data can violate EU AI Act principles
• The challenge of GDPR's "right to be forgotten" when data is baked into neural networks
• Model governance across the entire lifecycle—from selection to deployment monitoring
• Why human oversight remains critical in high-risk systems like loan decisions
• How smaller companies can stay compliant without enterprise-level budgets

Key frameworks covered:

✓ Data lineage and chain of custody

✓ Audit trails throughout the AI lifecycle

✓ Model cards for documentation (used by Google, Microsoft, Meta, Amazon)

✓ Post-deployment monitoring: data drift, concept drift, and bias detection

✓ Human-in-the-loop requirements for consequential decisions

The unsexy truth: Compliance as a service companies are emerging to help startups navigate these requirements. Trust isn't just a nice-to-have—it's becoming a competitive advantage.

The EU AI Act became law in 2024, and even if you're not in Europe, it's going to affect how you build with AI. In this episode, Sam and Mac break down the world's first comprehensive AI regulation—from banned applications to high-risk use cases that require strict oversight.

What you'll learn:

• The four-tier risk framework: unacceptable, high, limited, and minimal risk
• Why this matters for your AI projects (hint: think GDPR's global impact)
• How enterprises balance innovation with compliance
• Practical implementation strategies from the frontlines
• What "the right to be forgotten" means when data is baked into neural networks

Whether you're building AI applications, leading data teams, or navigating enterprise AI governance, this episode gives you the framework to implement AI responsibly while maintaining innovation velocity.

Timeline rollout: Bans effective early 2025, general purpose AI requirements mid-2025, full high-risk compliance by mid-2026.

In this episode of Inside Assemble AI, hosts Sam Dey and Mac Goswami speak with Sam Bhagwat, Co-Founder & CEO of Maastra, about the rise of AI agents and the tools needed to build them for production.

Sam shares his journey from Gatsby to Maastra, the developer pain points that inspired the pivot, and why context engineering is critical for effective AI agents. The conversation explores Maastra’s core primitives, the impact of the Model Context Protocol (MCP), and the challenges of moving from prototype to production. The episode concludes with a forward-looking vision of AI development—smaller models, broader access, and a future with a billion developers by 2030.

🔗 Guest Links

• LinkedIn: https://www.linkedin.com/in/sambhagwat/

• Mastra: https://mastra.ai/

• Book(s): https://www.amazon.com/Principles-Building-Agents-Sam-Bhagwat/dp/B0DYH5GHDD