Agentic AI: The Future of Intelligent Systems

2025 was the year AI accelerated everything — code, decisions, delivery, and expectations.But acceleration came with lessons.In this episode, we reflect on what actually changed when generative and agentic AI entered real production systems — not demos, not labs, but software that teams had to run, maintain, and be accountable for.This conversation explores why prompting was never engineering, how autonomy without structure created fragility, why no-code didn’t remove complexity, and what it really means to design AI systems that behave reliably over time.2026 isn’t about using smarter models or moving faster.It’s about building AI like software — with constraints, resilience, domain intelligence, and accountability designed in from the start.If you’re building, deploying, or operating AI systems in the real world, this episode sets the tone for what comes next.

The experimentation phase of Agentic AI is over.In this first episode of 2026, the focus shifts from smarter models to more sensible systems. Rather than predictions or hype, this episode breaks down three practical skills that will define success with Agentic AI in the year ahead.The conversation explores why behavior design matters more than raw intelligence, how decision budgeting turns open-ended reasoning into controllable systems, and why failure literacy is becoming a critical capability for teams building agentic systems at scale.This episode sets the tone for 2026 — moving from impressive demos to systems that are reliable, predictable, and built to endure in real environments.

At the start of 2025, the AI story felt settled.Bigger models. More agents. Faster rollouts.By the end of the year, the conversation had changed.This episode reflects on what actually surfaced in production environments — behaviour over capability, failure modes over demos, trust over promises — and offers six grounded predictions for how AI will evolve in 2026.From why AI will finally be treated as just software, to why restraint becomes the most valuable skill, to why human judgment grows more important as automation scales, this episode closes the year with clarity rather than hype.This is the final episode of the year.Thank you for listening, sharing, and being part of the journey.Wishing you a calm holiday season — and a more deliberate, well-behaved AI future in 2026.

Agentic AI is moving fast. Models are changing. Tools are evolving. Standards are forming. But amid all this movement, organisations are facing a deeper question: where should they actually focus?In this episode, we move beyond model intelligence and talk about behaviour, discipline, and system design. Why intelligence is now a baseline, not a strategy. Why trust is built in the messy edge cases, not the perfect demos. And why production-grade agentic AI requires intent, lifecycle thinking, restraint, and predictable behaviour under change.A grounded conversation on how to think about agentic AI as an operating model, not a feature — and how organisations can navigate 2026 without chasing every new release.

In this episode, we step back and reflect on what really happened in the agentic AI space in 2025.The year was defined by rapid model releases, evolving frameworks, and a growing ecosystem of tools promising intelligent, autonomous systems. But beneath the momentum, organisations encountered a deeper challenge: building agentic systems on foundations that were still shifting.This episode explores how continuous model upgrades affected agent behaviour, why tooling emerged to stabilise execution rather than boost intelligence, and how coordination, interoperability, and structure became unavoidable concerns. From agent-to-agent communication and Google Antigravity to Model Context Protocol and the formation of the Agentic AI Foundation under the Linux Foundation, 2025 marked a shift toward standardisation and consolidation.Most importantly, the episode reinforces a central theme of this podcast: agentic AI systems are tools, not the work itself. Building production-grade agentic AI requires engineering discipline, behavioural testing, lifecycle thinking, and the ability to design for constant change.As we move into 2026, the key question is no longer which agent framework to adopt, but where organisations should focus when everything is still evolving. That is the question we take up in the next episode.