In this episode, we explore what happens when AI leaves the lab and enters real-world production. We examine why most AI projects fail at deployment, how production systems differ fundamentally from research models, and what it takes to operate large language models reliably at scale.

The discussion focuses on the engineering, organizational, and governance challenges of deploying probabilistic systems, along with the emerging architectures that turn LLMs into agents capable of planning, tool use, and autonomous action.

This episode covers:

• Why most AI projects fail in production
• Research vs. production AI: reliability, consistency, and scale
• Build vs. buy trade-offs for LLMs
• Hidden costs: prompt drift, prompt engineering, and inference
• Evaluation, monitoring, and governance in real systems
• Agent architectures and AI as infrastructure

This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

Additional references and extended material are available at:

https://adapticx.co.uk

Season 7 begins at a turning point. AI is no longer confined to research papers and demos—it is deployed, operational, and shaping real-world systems at scale. This season focuses on what changes when models move from experiments to production infrastructure.

We explore how organizations build, monitor, and maintain AI systems whose behavior is probabilistic rather than deterministic. What reliability means when models can adapt, fail in unexpected ways, and influence high-stakes decisions. And how engineering practices evolve when AI is treated not as a tool, but as a collaborator embedded in workflows.

The season also looks ahead to the next frontier: reasoning models, planning systems, and autonomous agents capable of using tools, coordinating tasks, and acting toward goals. Alongside these capabilities come urgent questions of safety, governance, and control—how risks are identified, how responsibility is enforced, and how oversight scales with capability.

Finally, we examine one of the defining debates of this era: open versus closed models. Who should control powerful AI systems, how transparency affects innovation and safety, and what these choices mean for the long-term trajectory toward AGI.

Season 7 is about AI in the world—how it behaves in production, how it is governed, and how today’s decisions shape what comes next.

This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

Additional references and extended material are available at:

https://adapticx.co.uk

In this episode, we explore how large language models evolved from passive text generators into agentic systems that can use tools, take actions, collaborate, and operate inside dynamic environments. We explain the shift from “knowing” to “doing,” and why this transition marks one of the most significant changes since the Transformer.

We break down what defines agentic AI, how agents plan and act through tool use, and why multi-agent systems outperform single models on complex, real-world tasks. The episode also covers the emerging agent frameworks, real business impact, and the safety and governance challenges that come with autonomy.

This episode covers:

• The gap between text generation and real-world action
• What defines agentic AI: autonomy, reactivity, proactivity, learning
• Tool use as the bridge from reasoning to execution
• Agent lifecycles: planning, action, observation, refinement
• Single-agent limits and multi-agent systems (MAS)
• Popular agent frameworks (LangChain, LangGraph, AutoGen, CrewAI)
• Enterprise, science, and productivity impacts
• Safety, latency, memory, and responsibility challenges

This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

Additional references and extended material are available at:

https://adapticx.co.uk