Thomas Martinsen
Global Principal Advisor @ Twoday | Microsoft Regional Director | Microsoft AI MVP | Startup Mentor | Developer by Heart
Copenhagen, Denmark
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Thomas Martinsen is Global Principal Advisor at Twoday, Microsoft Regional Director, and Microsoft MVP in AI and Developer Tools. With more than 30 years of experience in software engineering, technology leadership, and strategic advisory, he helps organizations understand and adopt emerging technologies such as AI, cloud, and next-generation software engineering practices.
Thomas works across the Nordic region advising customers, partners, and leadership teams on technology strategy, innovation, and business transformation. He is passionate about bridging the gap between emerging technology and real-world business value.
Area of Expertise
Topics
Autonomous AI Agents
Most AI solutions today assist humans. The next generation of AI agents will execute work independently.
We are entering a time where AI is no longer just answer questions, generate content, or assist users through chat. Autonomous AI agents can pursue goals, reason about what needs to happen, discover and evaluate information, and take action across digital environments.
In this talk, we will explore how autonomous agents move beyond traditional automation and predefined workflows. Instead of being told every step to follow, agents can identify what information is missing, determine where to find it, evaluate the quality of available data, and continuously adapt their approach as conditions change.
We will examine the core concepts behind autonomous agents, including reasoning, planning, tool use, knowledge building, orchestration, and safe execution. The talk will also cover what organizations need to consider when introducing agents into real business processes, including trust, governance, observability, security, and maintaining control over autonomous behavior.
This session is for anyone who wants to understand where AI is heading beyond copilots and chatbots. The future of AI is not simply better interfaces—it is autonomous digital workers capable of turning objectives into outcomes.
Delivery Options
- Duration: 30–60 minutes (flexible)
- Language: Danish or English
- Level: Inspiration (200)
Engineering Autonomous AI Agents
Most AI solutions today assist humans. The next generation of systems will execute work independently.
Autonomous AI agents represent a fundamental shift in how we think about AI-enabled systems. Instead of simply responding to prompts or assisting users through chat interfaces, agents can reason, plan, gather information, evaluate options, and take action in pursuit of a goal.
In this talk, we will explore what autonomous AI agents are, why they matter, and how they differ from traditional automation, copilots, and low-code AI workflows. We will look at how agents can discover information, work across multiple data sources, build knowledge, identify gaps, and dynamically determine the next best step instead of following a predefined process.
The session will also address what it takes to move from impressive demos to enterprise-ready agentic systems. We will discuss key architectural patterns, orchestration models, operational concerns, and the safety, governance, and control mechanisms required when AI systems are allowed to act on behalf of users and organizations.
Attendees will leave with a clear understanding of the opportunities and challenges of autonomous AI agents, how they can transform business processes, and why the future of AI is not just smarter chat interfaces—but autonomous digital workers capable of turning objectives into outcomes.
Delivery Options
- Duration: 30–60 minutes (flexible)
- Language: Danish or English
- Level: Inspiration (300)
The quantum advantage gap (for developers)
What software teams need to understand before quantum becomes practical.
Quantum computing sounds like a future breakthrough, but for software teams the important question is more practical: what will it take before quantum becomes something we can actually build with? Today, the gap is not just about hardware. It is about programming models, tooling, architecture, resource estimation, hybrid execution, and knowing which problems are even worth trying.
In this talk, we explore that gap from an engineering perspective. We will look at how quantum workloads differ from classical computing, why hybrid systems will matter, and how quantum may connect with AI, HPC, cloud platforms, and existing software architectures.
Attendees will leave with a grounded understanding of what quantum advantage really means, why it is hard to achieve, and how technical teams can start preparing without pretending quantum is already production-ready.
Delivery Options:
- Duration: 30–60 minutes (flexible)
- Language: English
- Level: Inspiration (300)
Audience:
The talk can be adjusted to target either developers or technical leaders.
The quantum advantage gap (for leaders)
Quantum computing is no longer just a research ambition — but it is not yet a mainstream business tool either. The real opportunity lies in the gap between the two.
This talk explores how business and technology leaders can understand that gap, prepare for what is coming, and identify where quantum may create advantage before the market fully catches up. We will connect quantum with AI, cloud, cybersecurity, and software engineering — not as distant theory, but as an emerging strategic capability.
Delivery Options:
- Duration: 30–60 minutes (flexible)
- Language: Danish or English
- Level: Inspiration (300)
Audience:
Business and tech leaders.
Using the right intelligence for the job
AI is already part of the engineering toolbox. The next challenge is using it deliberately.
In this highly interactive session, we will explore what it means to treat AI usage as an engineering discipline. That starts with choosing the right tool, model, and level of reasoning for the task instead of automatically reaching for the most capable option.
But good decisions require visibility. We will demonstrate a token usage dashboard and a token counter tool we have built to capture usage across our engineering tools, and explore what the data can tell us about model choice, cost, efficiency, and engineering behavior.
The goal is not to use less AI. It is to understand what we use, why we use it, and whether we are using the right intelligence for the job.
Delivery Options:
- Duration: 20-45 minutes (flexible)
- Language: English
- Level: Inspiration (100-200)
Audience:
The talk can be adjusted to target either developers or managers.
Who decides what happens next?
Engineering truly autonomous AI agents
Most AI agents still wait for us. We give them a task, they reason, call a tool, return a result — and stop. But what happens when we remove the human from deciding what happens next?
In this session, we will go beyond tool-calling agents and explore what it takes to engineer systems that can operate autonomously: pursuing goals, observing their environment, reasoning about what is happening, planning their next actions, delegating work to other agents, acting through tools, and continuously deciding what to do next.
Through a live multi-agent system connected to a content platform, we will see how content can become more than data an agent reads and writes. Changes in the platform can become signals that agents observe, reason about, and act upon — triggering collaboration between specialized agents without waiting for another human prompt.
Along the way, we will explore the engineering challenges that emerge as we increase autonomy: coordination, state, memory, permissions, guardrails, human approval, and knowing when an autonomous system should stop.
The goal isn't to build agents that can do everything on their own. It's to understand how to engineer systems that can decide what happens next — and where we still want humans to make that decision.
Delivery Options
- Duration: 30–60 minutes (flexible)
- Language: Danish or English
- Level: Inspiration (300)
Engineering Agentic Loops and Graphs
How do you make an agent keep working until the job is actually done? How do you make it verify its own progress, recover when something fails, and know when to stop? And when is one agent no longer enough?
In this session, we will go deep into loop and graph engineering — two powerful patterns for building agents that can operate autonomously and reliably.
We will build agents that work towards verifiable goals, iterate until specific conditions are met, and operate within clear boundaries. Then we will take the next step and compose agents into graphs where specialized agents divide the work, run in parallel, verify each other, and hand work between them.
Along the way, we will explore how to design effective finish lines, build verification into the loop, prevent agents from running forever, decide when work should be split across multiple agents, and control what flows between them.
You will leave with practical patterns for designing loops and graphs that make your agents more autonomous, predictable, and effective — patterns you can start using in your own agentic systems right away.
Delivery Options
- Duration: 30–60 minutes (flexible)
- Language: Danish or English
- Level: Experienced (300)
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Thomas Martinsen
Global Principal Advisor @ Twoday | Microsoft Regional Director | Microsoft AI MVP | Startup Mentor | Developer by Heart
Copenhagen, Denmark
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