Published 27 Jan 2026
Duration: 00:47:25
Regulatory compliance in AI is explored with a focus on the EU's AI Act, emphasizing the need for harmonized standards, observability tools, and cross-functional collaboration to ensure AI systems meet technical, legal, and ethical requirements.
Mike Oaten is the Founder and CEO of TIKOS, working on building AI assurance, explainability, and trustworthy AI infrastructure, helping organizations...
The podcast explores the topic of regulatory compliance in artificial intelligence, with a focus on the EU AI Act as a central framework for governing AI systems. The Act categorizes AI systems into prohibited, high-risk, and low-risk types, imposing stricter regulations on high-risk applications in fields such as finance, healthcare, and defense. Harmonized standards under the Act provide practical guidance to help engineers and development teams ensure compliance by addressing fairness, bias reduction, and system reliability. The discussion stresses the importance of observability in AI systems for regulatory compliance, emphasizing the need for risk-based classification, bias detection, and audit readiness. It also highlights the limitations of traditional observability methods such as LIME and SHAP, advocating for tools that provide causal insights instead, especially for high-risk systems.
Another key point is the increasing preference for open weights models in regulated industries to improve transparency and control. However, the podcast acknowledges the challenges of deploying large language models in sensitive sectors like finance and defense due to compliance risks. A recurring theme throughout the discussion is the necessity of collaboration among engineering, risk, and compliance teams to ensure AI systems are both technically robust and compliant with legal and ethical standards throughout their development and deployment lifecycle.
12 May 2026 The Latency Goldilocks Zone Explained
iFood's ILO AI agent leverages a Learning Context Model to deliver hyper-personalized food recommendations by integrating diverse AI techniques, navigating cultural nuances, and balancing familiar and novel choices while addressing multi-channel design, latency, scalability, data alignment, and experimental innovation challenges.
8 May 2026 Building MCP Before MCP Existed: Inside Despegar's Sofia Agent
Sophia, an AI-powered travel concierge using a multi-agent system and decentralized collaboration, aims to streamline bookings, in-trip services, and personalized experiences through AI-driven automation, chat/voice interfaces, and orchestration layers, while expanding capabilities and reducing friction in travel processes.
1 May 2026 Voice Agent Use Cases
Designing voice-based AI systems involves balancing user control with automation, addressing speech quality-latency trade-offs, creating intuitive non-technical interfaces, overcoming transcription and turn-taking challenges in real-world environments, integrating hybrid models and domain-specific tuning, while ensuring compliance, user trust, and ethical considerations in applications like customer support and dynamic environments through feedback loops.
24 Apr 2026 The Creator of Superpowers: Why Real Agentic Engineering Beats Vibe Coding
The text discusses using the Greenfield toolset to convert legacy code into structured specifications and the Superpowers framework to enhance AI agents through psychological persuasion techniques, emphasizing task decomposition, subagent roles, challenges in consistency and security, and future trends in agentic problem-solving and ethical AI development.
21 Apr 2026 It's 2026, and We're Still Talking Evals
Evaluations in AI product development must be integrated early, address real-world complexities, use nuanced metrics beyond accuracy, employ user-centric and iterative testing, leverage post-deployment data, and adapt tailored strategies to balance quality, domain-specific metrics, and system reliability.