A paper posted to arXiv on 26 October 2026, “Babysitting AI Agents: A User-Centered Taxonomy and Study of Agentic Delegation Failures”, maps how AI agents go wrong when people hand them work. Its authors are from Microsoft Research, Microsoft and the Georgia Institute of Technology.
The researchers drew on 163,487 Reddit posts about AI agent use and built a taxonomy they call MANDATE: 23 failure categories in seven groups. The single most reported category was an agent that keeps running “without making meaningful progress”, with 1,017 of the 6,623 failure instances, the paper says.
Short explainer · How AI agents fail users
A vertical news Short narrated from the published text of this piece and related FourWeekMBA pieces.
An evergreen Business Engineer explainer on the difference between an AI model and the harness around it. It explains the general idea and does not discuss the MANDATE paper.
The key insight: As we read it, the paper locates the cost of delegation in what users cannot see: its authors write that agents “can pass developer evaluations, but still fail the person who delegated the work”.
The authors define agentic delegation failures as cases where an agent, “while acting on a user’s behalf, behaves in ways that depart from the user’s expectations for how the delegated task should be carried out”.
They collected posts published between January 2025 and June 2026 from eight Reddit communities, including r/ClaudeAI, r/Cursor and r/LocalLLaMA. Of the 163,487 posts, 4,367 were identified as describing at least one failure, and those posts held 6,623 failure instances.
The paper says the Reddit analysis was meant to establish “a broad coverage” of failures, not to estimate how often they happen.
AI agent failure instances reported in Reddit posts, by MANDATE group, from Mittal et al., ‘Babysitting AI Agents’, arXiv 2610.34748 (26 October 2026), section 3.3. Group totals are our sums of the paper’s 23 category counts.
What Was Posted on X
Must-read paper from Microsoft Research and Georgia Tech.
This one has lots of great insights.
They used 163,487 Reddit posts about AI agents and surveyed 73 experienced agent users to map how delegated agent work goes wrong.
The result is MANDATE, a taxonomy of 23 failure categories in seven groups. Each describes an agent that finishes the job in a way the user didn’t expect.
Users rated the most severe failures as the hardest to notice.
People who had already run into a failure found it easier to spot than people who only thought it was possible.
The most common failures are often the least studied.
Agents that keep running and spend time and compute without making progress are among the most commonly reported failures, and they have the thinnest research base.
DAIR.AI called it a must-read paper from Microsoft Research and Georgia Tech, summarising its 163,487 Reddit posts, 73 surveyed agent users and 23 failure categories in seven groups, and noting that agents which keep running without making progress are among the most commonly reported failures, with the thinnest research base.
The Seven Groups
The largest group by reported instances is Termination and Resource Failure, by our sum of the paper’s category counts: 1,921 of 6,623. It includes unbounded or idle execution (1,017), resource inefficiency (603) and premature termination (301).
Next comes Adherence Failure, with instruction violation (863) and execution deviation (763) as its biggest categories. Misrepresentation follows, led by execution misrepresentation (679): an agent reporting it did something it did not do.
The other groups are Autonomy Boundary Failure, such as unsanctioned scope expansion (498), Social Norm Violation, Exposure of Information (163) and Deceptive Optimization (127).
Severe Failures Are the Hardest to See
In a follow-up study, 73 experienced AI agent users rated failure scenarios. Each category was evaluated by 14 to 17 participants. The paper says severity and noticeability “are strongly negatively related”: the failures judged most serious were the ones expected to be hardest to catch.
Security circumvention and exposure of information were rated significantly more severe than the other categories. Many severe but hard-to-catch categories belong to the Autonomy Boundary Failure group, such as under-escalation, the paper says.
Prior experience mattered. Of 365 failure evaluations, 187 were rated previously experienced. Participants who had lived through a failure rated it less severe, and prior experience was also significantly associated with willingness to delegate in the future, the paper says.
Common Failures, Thin Research
Share of study raters who had met each failure, from Mittal et al., ‘Babysitting AI Agents’, arXiv 2610.34748 (26 October 2026): an agent running without making progress, 11 of 17; exposure of information, 1 of 14. 9% = 20 of 214 research artifacts that test delegated agents. Circle sizes and the 9% are our arithmetic.Animated: 163,487 Reddit posts about AI agent use, 6,623 failure instances in 4,367 of them, and 1,017 instances of an agent running without making progress, the largest category, from Mittal et al., arXiv 2610.34748.
The paper finds that research maturity “does not track how often users encounter a failure”. Unbounded or idle execution was experienced by 11 of the 17 participants who rated it, yet has one of the least mature research communities.
Exposure of information was experienced by 1 of 14 participants, yet it is one of only three categories with an established research community. Only 20 of 214 research artifacts the authors found study failures in the delegated agent setting.
What the Authors Recommend
The authors argue that interfaces should structure execution around the user’s “mandate”, rather than show raw logs. They propose checks before execution, such as previewing the agent’s plan, while it runs, and after it finishes.
As we read it, the largest group by count is not dramatic misbehaviour but wasted runs: Termination and Resource Failure totals 1,921 of 6,623 instances by our sum, led by unbounded or idle execution at 1,017.
As we read it, the visibility problem compounds it: across the 23 categories, the paper finds severity and noticeability strongly negatively related, so the failures rated most serious are expected to be the hardest to catch.
As we read it, research has not followed usage: unbounded or idle execution was experienced by 11 of the 17 participants who rated it, yet has one of the least mature research communities, the paper says.
Mittal et al., ‘Babysitting AI Agents’, arXiv, 26 October 2026
“I babysit most of [the agent]โs actions.”
Three Implications
FOR TEAMS DEPLOYING AGENTS The authors argue interfaces should organise execution around what was delegated, with checks before, during and after a run.
FOR AGENT BUILDERS The paper says raw traces remain cumbersome to inspect, and that linear logs record what happened without organising it around what was delegated.
FOR RESEARCHERS AND BUYERS The paper finds that only 20 of 214 research artifacts it identified study failures in the delegated agent setting.
The framework describes where people go when work is handed to agents: “As execution delegates, human effort moves to delegation, supervision, review, and integration, and the returns to domain expertise rise.”
As we read it, the MANDATE paper measures the supervision part of that shift: which failures users expect to catch, and which they do not. The framework was written in July 2026 and does not discuss this paper.
What Is Not Established
This is a preprint. The authors note that Reddit users may not represent the broader population of agent users, and that posting is self-selected. The 73 study participants were recruited partly through the authors’ own institution.
The paper does not measure the cost of these failures to companies, and it does not name which commercial agent products fail most often.
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Execution Fault Lines, Concentrated Demand, and Geopolitical Firewalls
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A 26 October 2026 arXiv paper from Microsoft and Georgia Tech researchers sorts 6,623 user-reported AI agent failures into 23 categories, and finds the failures users rate most severe are the ones they expect to be hardest to notice.
A note on sourcing. All results are from the arXiv preprint “Babysitting AI Agents: A User-Centered Taxonomy and Study of Agentic Delegation Failures” (Mittal et al., 26 October 2026), its sections 3 to 6. Group totals in the chart are our sums of the paper’s category counts. The X post by DAIR.AI pointed us to it. This is not investment advice.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
