A couple of months ago, I sat across from my nine-year-old daughter’s teachers at a parent-teacher conference. They were kind but concerned. She takes her time on assignments, they said—often deep in thought.
How would she do on timed tests next year? I told them I wasn’t worried. What they described as a problem is, to me, one of the most important things she can learn: the ability to take a hard problem and reason through it from beginning to end.
In a world optimized for efficiency, qualities like patience, perseverance, and attention to detail are not deficiencies. They are the foundation of sound judgment, which will become the skills we need most. The more time I spend working with AI, the more convinced I become: the question that matters for her future isn’t how quickly she can answer.
It’s whether she has the judgment to know when an answer can be trusted. I’ve spent decades at Microsoft watching this tension play out: first building tools for other developers, then working across AI as models moved from research curiosities to systems deployed at scale. Now we’re building Microsoft IQ , where we’re exploring how an organization’s collective intelligence can become its greatest advantage .
Through every one of those chapters, one thing has remained true: it’s never enough for a system to be powerful, it must also be trustworthy. Trust is what turns assistance into delegation. When we can trust an agent to do what we intend, within the limits we set, we can hand off the work we never wanted to spend our lives on: the repetitive tasks that drain attention, the mundane work that fills a day without moving anything meaningful forward, the dangerous work humans should not have to do, the work too vast for any individual or team.
Agents should take on that toil, extend our reach, and give us back our time for the work that calls for something only humans bring. My daughter doesn’t know any of this yet. But by the time she’s grown, most of the work that rewards speed and repetition will be work we delegate.
What will matter then is exactly what gave her teachers pause: the patience to stay with a hard problem, reason through it, and decide when she’s reached a conclusion she can trust. The very thing they feared might hold her back could be exactly what the next era prizes most. So no, I’m not worried about the timed test.
I hope she grows up in a world where software carries the toil and people are freed for the work that is unmistakably ours—to think, to judge, to create, to care for one another. That is the future I want agents to make real. But my hope is not evidence it will happen.
The future I just described turns on a single question: can we trust agents to do the work? Trust is earned one task at a time. So, I went looking for evidence of where it’s been earned, and where it hasn’t.
For the past year, the conversation around AI agents has circled the same promise: eliminate toil so people can focus on what matters. But I keep coming back to sharper questions. What, exactly, is toilsome?
Where does toil actually live in people’s work? What are the technical leaders closest to this shift willing to hand off—and what gives them the confidence to do it? To find out, we partnered with MIT Technology Review Insights on new research that draws directly from the people building this frontier.
Not the people talking about it, the people doing it. We surveyed 300 technical experts across AI, data, and cloud domains, spanning 12 industries and 4 regions of the world, asking them to rank their confidence across 101 of the top tasks. What we got back is the 2026 Agent Confidence Index , an honest map of where agents are delivering real value, so our community can see what’s working and move forward together with conviction.
Explore the 2026 Agent Confidence Index report Learn from where confidence is highest Across the 101 tasks measured, average confidence already lands at 64 out of 100 , and thirty tasks clear 70. The highest scores cluster on work that is both predictable and draining: the late nights, the interruptions, the low-value repetition. Automated report generation leads at 83.
- Boilerplate code generation for new features sits at 82. 5, the hours a developer no longer spends rewriting the same patterns, freed for the work that challenges them.
Certificate expiration monitoring and renewal, at 81. 5, ends the scramble that pulls engineers off high-stakes problems for something entirely routine. Real-time data stream monitoring follows at 80.
5, and release note generation from commit history at 79. 5, the manual end-of-sprint commit review, gone. This is where frontier teams are already delegating to agents, regularly.
The pattern holds across every discipline. In developer and AI workflows it extends to API client maintenance and code identification; in cloud operations, to ticket routing and cost optimization; in data, to anomaly detection. Wherever it sits in the stack, this is work technical teams now trust agents to own.
What matters most here isn’t what the data says about the tasks, it’s what it says about the people delegating them. When technical experts believe in something deeply enough to hand it real work, that belief ripples outward. It becomes the recommendation they make to their leadership, the solution they build for their customers, and the culture they create for their teams.
Even the toughest agent tasks are gaining traction Here’s what strikes me most: the tasks ranked lower on the index are still high in absolute terms. Service mesh configuration and troubleshooting sits at 37. 5, database schema migration scripting at 46.
5, memory leak detection at 48. 5. These sit at the very frontier, the interconnected, high-stakes work where investment and innovation are concentrated right now.
Consider what they demand. Service mesh configuration touches many systems at once.
Originally published at microsoft.com
