A platform engineer described an order from management on Reddit (exhibit 127). Everyone on the team had to create a new agent within 2 weeks. Afterwards, the same management questioned the token spend. Tokens are the units of text that the model vendor charges for. The author says shell scripts would do the same job. The top answer was to wrap the script in a tool to comply.
The demand is there. BCG surveyed 1,330 executives in 2026 (exhibit 135). Almost half of their companies say they already get value from AI. They expect agents to deliver about 40% of that value by 2030. Deloitte surveyed 3,235 leaders (exhibit 90). 74% expect to use agents at least moderately by 2027. If the demand is real, waiting has a cost too. These are consultancy surveys, and they measure expectations.
about 40%
Share of AI value they expect from agents by 2030. Exhibit 135
An agent that takes actions is not reckless either. In dotnet/runtime, the coding agent opens pull requests, which are real changes to the code (exhibit 68). Of the merged changes, only 0.6% were reverted. And a narrow agent can do a lot with little risk. A commenter on Reddit described one that sorts about 5,000 faxes a day (exhibit 48). That agent has no power to do harm.
First, quantity. IBM surveyed 2,000 CEOs in 2026 (exhibit 95). An article in Business Today gives figures the press release does not include (exhibit 96). Only 10% say advanced or agentic AI is what drives their growth the most. Agentic AI is AI that acts through agents. Two years earlier, 49% expected generative AI to be that driver. And 53% still have piloting and experimenting as their main goal.
Quantity targets produce what they measure. We already saw the agent per person in 2 weeks. Another Reddit thread describes a company that measures how many tokens each employee uses (exhibit 89). Some people inflated their usage with pointless queries. In a product thread, the proposal was to measure the user’s outcome (exhibit 92). If the agent does not pay for itself and for the team that maintains it, you shut it down.
The more agents a company deploys, the further along it is with AI.
Verdict: No: counting agents or tokens measures activity, not results. Only 10% of CEOs see advanced or agentic AI as the main driver of their growth. Companies expect agents to deliver about 40% of AI's value by 2030. But that is an expectation. In two threads, one company tracks tokens per employee. Another asked for one agent per person in 2 weeks. The response was to inflate usage or wrap a script.
Rule
Measure progress by problems solved, not by agents deployed, and set no quantity targets. At every review, show which metric each agent moved.
Why medium confidence. The surveys are executive self-reports, run by companies that sell AI or consulting. The IBM figure was read in the press. The same study says success depends more on people adopting the tool than on the technology. The cases of quantity targets and of mandates with no problem to solve come from Reddit. But they recur across several subreddits.
Show the 7 exhibits in detailHide the detail1 for, 4 against, 2 qualify
The 2026 edition of BCG's study says the gap narrowed: almost half of companies already get value from AI. Many expect to give agents real autonomy, but almost none have the controls to do so today.
Numbers
about 48.5%Companies that generate value: 7.5% leaders (5% in 2025) and 41% scaling; out of 1,330 executives.
42% and 5%Expect to give agents real autonomy by 2030, and have all the controls today.
about 40%Share of AI value they expect from agents by 2030.
Limitation An announcement by a consultancy that sells AI services; self-report, with no detail on how it classifies companies. Automated download now returns 403, so it carries no verbatim quote.
The article reports figures from IBM's 2026 study that the announcement does not include. About seven in ten CEOs say AI is already transforming their core business. But only one in ten says advanced or agentic AI is what most drives their growth. More than half say their main goal with AI is still running pilots and experimenting.
Numbers
10% (two years earlier, 49% expected generative AI to be)CEOs who say advanced or agentic AI is what most drives growth.
53 %Say their main goal with AI is piloting and experimenting.
Limitation A press article about the IBM study; the figures were not checked against the full report. It is CEO opinion, not a return measurement.
The company measures how many tokens (units of text that the model processes) each employee uses, as a sign of AI adoption. The author admits that the measure is meaningless. He also says others were caught inflating usage with useless queries. The comments compare it to measuring a programmer by lines of code or a help desk by tickets closed.
Numbers
291 points and 291 commentsSize of the thread according to the archive.
Limitation One person's story and opinions from peers. It was read in the Arctic Shift archive (full post and 100 comments) because reddit.com blocks automated reading. The quote was compared letter by letter with the archive.
“meaningless way to measure an employee's usefulness and AI skillset”
Management requires every person on the platform team to build a new agent in 2 weeks. But then it questions the token spend. The author says shell scripts can do the same. The most upvoted reply proposes to wrap the script in a tool or in MCP to comply. Another proposes to use AI to write the pending scripts, with guidance and review, as with a very new team member.
Numbers
1 agent per person in 2 weeksWhat management asked for.
147 points and 90 commentsSize of the thread according to the archive. The quoted comment has 59 points.
Limitation One person's story and opinions from peers. It was read in the public Arctic Shift archive because reddit.com blocks automated reading. The quote was compared letter by letter with the original.
“they get their agent as basically a more expensive unix pipe”
The company hires a consultancy to mandate AI use and raise developer productivity. The author's manager admits productivity was not low, but his hands are tied. It is a mandate with no problem to solve.
Numbers
237 points and 120 commentsSize of the thread according to the archive.
Limitation One person's story. The post was read in the Arctic Shift archive; the comments were not.
IBM Institute for Business Value, Oxford Economics
The 2026 edition of IBM's CEO study talks about redesigning leadership and adding AI chiefs. It also talks about leaving more operating decisions to AI. It says success depends more on people adopting the tool than on the technology.
Numbers
76% (26% in 2025)Companies with an AI chief, out of 2,000 CEOs in 33 geographies (February to April 2026).
25% today and 48% expected by 2030Operating decisions made by AI with no human involvement.
83 %Say success depends more on people's adoption than on the technology.
Limitation An announcement by a company that sells AI and consulting; an opinion survey. It does not repeat the return figures of the 2025 edition.
A thread asks how to measure the value of an agent. One comment asks to go back to the product's main metric. If the agent does not pay for itself and for the team that maintains it, the comment asks to shut it down. Another asks to measure the user's outcome rather than the number of agent actions. For example, did the user act on the answer, or send it without editing.
Numbers
3 times its costRevenue an agent product should generate, according to the rule one commenter proposes.
Limitation A low-scoring thread (25 points and 32 comments in the archive). The 3 times rule is an opinion, not data. It was read in the public Arctic Shift archive because reddit.com blocks automated reading. The quote was compared letter by letter with the original.
“an agentic product should generate 3x revenue for the cost of the agent”
Quote checked against the original
Read at the source. Checked on Nov 2, 2026.
What about actions from day one? The cases in the base that lasted started by reading. The base is the set of sources on this site. A platform team with about 20 services tried agents for a year (exhibit 120). It described on Reddit what was left. It dropped AI-generated infrastructure, because someone had to read every line. It kept agents that read and summarize. Its alert correlation groups related alerts and looks for the likely cause. It is right 60% of the time, against 0% for the previous system.
How often alert correlation gets the probable cause right, vs the old system.Exhibit 120
Times it gets the probable cause right, in %
With the agent60%
Previous system0%
0100%
The IT triage from chapter 10 also started with read-only access (exhibit 117). Its next step is automation, with a person approving from a chat. The support team from chapter 11 reviewed 100% of the answers at the start (exhibit 99). It did so for the first 30 days.
One study surveyed 86 practitioners with agents in production (exhibit 29). 68% let the agent run at most 10 steps before a person steps in. In an IT thread, the top advice was to get something in writing before giving actions (exhibit 48). That document says the business accepts the risk.
For an agent to be worth it, you have to give it actions from day one.
Verdict: It is not necessary. In the cases in the evidence base that lasted, teams started by reading. A platform team, after a year, kept agents that read and summarize. An IT triage flow, which sorts and routes tickets, started with read-only access. It leaves actions for later, with a person approving from a chat. The ones that act successfully do it under review. A support team reviewed 100% of answers for the first 30 days.
Rule
Start with an agent that reads and proposes, and review everything it produces at first. Add actions one at a time, each with a written risk acceptance from whoever is accountable for the business.
Why medium confidence. The study of production teams is a primary source with a small sample. In it, 68% let the agent take at most 10 steps before a person steps in. The one-year, support and triage cases are Reddit accounts with no independent measurement. The supporting data shows an agent that acts well, but with expert review of every change.
Show the 6 exhibits in detailHide the detail1 for, 3 against, 2 qualify
Ten months of data on the Copilot coding agent in the dotnet/runtime repository. More than half of its pull requests were merged and almost none were reverted, because each one went through expert review. It did best on mechanical, well-scoped tasks (cleanup, tests) and worst on those that need judgment (performance). An instructions file and build setup almost doubled success. The bottleneck moved from writing code to reviewing it.
Numbers
878 PRs, 67.9% successAgent pull requests and success rate (87.1% for PRs by Microsoft staff; populations not comparable).
0.6% vs 0.8%Merged PRs later reverted: the agent's vs the rest.
from 38.1% to 69%Success before and after adding instructions and setting up the build.
86.2% vs 55.1%Success with and without commits from a person in the PR.
5 to 9 hoursReview work created by nine PRs the author opened in a short time.
Limitation Written by a Microsoft engineer about a Microsoft tool. It is a repository with very expert reviewers. The author notes that the agent and the people do not do the same kind of tasks.
“we cannot compromise on review quality, on expert and experienced eyes validating the changes”
A platform team with about 20 services reports what stayed after a year of trying agents. It dropped AI-generated Terraform, because every line had to be read. It also dropped an assistant that answered wrongly with confidence: someone almost ran a command it suggested in production. It kept what reads and summarizes: automatic changelogs, PR review and alert correlation. The rule is that agents read everything, but do not touch infrastructure.
Numbers
60% vs 0%How often alert correlation gets the probable cause right, vs the old system.
289 points and 43 commentsSize of the thread according to the archive.
Limitation One team's story, without independent measurement. A commenter suspects the post is an ad for a review tool it mentions. It was read in the public Arctic Shift archive because reddit.com blocks automated reading. The quote was compared letter by letter with the original.
“agents can read and summarize anything but they dont get to touch infra”
A mid-size SaaS describes how it built a support triage agent, which sorts requests and decides who handles them. It started with passwords and billing. The agent only answers if it can cite an article or a fact from the database. People reviewed 100% of answers for the first 30 days. Refunds, cancellations, large customers and low-confidence answers go to a person. The first version without review made up a feature that did not exist. The team did not shrink: it moved to the hard cases.
Numbers
47% and 22%Tickets resolved with no person, and tickets drafted by the agent and sent by a person, at 90 days.
USD 0.18 vs about 3.50Cost per ticket with the agent vs the previous human cost.
0.85Confidence threshold: below it, the answer goes to a person.
Limitation Self-reported numbers in a small thread (3 points and 7 comments in the archive), without independent verification. The original post text was read in the public Arctic Shift archive because reddit.com blocks automated reading.
A thread asks which IT tasks were really automated with AI, and the most upvoted comment answers “none”. On the plus side, one lead built a triage flow (sorting of requests) with read-only access. The flow reads device management, the directory, two-factor authentication, the network and ticket history. The next step is to automate with a person approving from a chat. He says the goal is to grow, not to cut people.
Numbers
30 minutes per ticketSaving on complex tickets, according to the person who built the triage.
67 points and 146 commentsSize of the thread according to the archive. The quoted comment has 47 points and the “none” comment, 134.
Limitation Self-reported saving, without measurement. It was read in the public Arctic Shift archive because reddit.com blocks automated reading. The quote was compared letter by letter with the original.
“On more complicated stuff this is saving EASILY 30 minutes per ticket.”
A case study and survey of practitioners with agents in production. Most evaluate with people rather than benchmarks (standard tests), and let the agent take few steps before someone steps in. Reliability, meaning that the agent behaves correctly over time, remains the main challenge. Teams tackle it with system design.
Numbers
20 cases and 86 practitionersBasis of the study: 20 in-depth interviews and a survey of practitioners across 26 domains.
74%Rely mainly on human evaluation.
68%Run at most 10 steps before a person steps in.
70%Prompt off-the-shelf models instead of tuning their weights.
Limitation A small sample chosen by the authors; it does not represent all companies. Preprint.
“Reliability (consistent correct behavior over time) remains the top development challenge”
This is the same author as the thread on connecting a model to internal data. Leadership now wants agents that trigger workflows and send emails. The most upvoted advice: get the business or legal to accept the risk in writing before giving the agent actions. In contrast, another describes a narrow agent that routes about 5,000 faxes a day to the right person, with no power to do harm.
Numbers
about 5,000 faxes a dayVolume a narrow agent routes, according to one commenter.
338 points and 129 commentsSize of the thread according to the archive. The quoted comment has 376 points.
Limitation Experience from people who run IT, without measurement. It was read in the public Arctic Shift archive because reddit.com blocks automated reading. The quote was compared letter by letter with the original.
“get it in writing from legal that you're not liable”
Quote checked against the original
Read at the source. Checked on Nov 2, 2026.
Buy or build? Menlo Ventures is a fund that invests in AI companies. Its survey shows where the spending goes (exhibit 32). In 2025, companies bought 76% of their AI, against 53% a year earlier. That says companies buy more, not that buying works better. Menlo says, without giving a figure, that bought solutions reach production sooner. No source in the base compares the results of the two paths.
The same survey gives another figure, whether bought or built. Only 16% of what large companies deploy as AI is a real agent. The trap in buying is the audit. In a buyers’ thread, the conclusion was that auditing the agent falls on whoever buys it (exhibit 45). One comment put it this way: “there are no frameworks for doing any kind of an audit of model behavior.”
It is better to build agents in-house than to buy them.
Verdict: There is no data on which is better. In 2025, companies bought 76% of their AI instead of building it, up from 53% a year earlier. That says more is bought, not that it works better. Menlo adds, without a figure, that bought solutions reach production sooner. Whether bought or built, only 16% of what large companies deploy is a real agent.
Rule
If you buy, test the agent on your own tasks before signing, and ask to see what it does step by step. If you build, start with the simplest solution. Add complexity only when it improves the result.
Why low confidence. The survey is run by a fund that invests in AI companies. It shows what gets bought, not what works. The problem of auditing vendors comes from a Reddit thread with a very low score. No source compares buying and building on results. None argues with data for building in-house.
Show the 2 exhibits in detailHide the detail0 for, 1 against, 1 qualifies
Tully, T., Redfern, J., Das, D., Xiao, D. Menlo Ventures
Of what companies deploy as AI, only a small part qualifies as a real agent. Companies buy more than they build (76%). Menlo says, without a figure, that bought solutions reach production sooner. About half of AI deals reach production, well above traditional software.
Numbers
16% and 27%Deployments at large companies and at startups that qualify as real agents.
76% boughtShare of AI that was bought in 2025 instead of built (53% in 2024).
47% versus 25%AI deals that reach production, versus traditional software.
37 billion dollarsEnterprise spending on generative AI in 2025 (11.5 billion in 2024).
Limitation A survey of 495 US company decision makers (November 7 to 25, 2025) plus market data. Menlo is a fund that invests in AI companies. It does not say buying works better. It says more is bought and that bought solutions reach production sooner.
“companies spent $37 billion on generative AI in 2025”
In proofs of concept, agent vendors hand over an API key and say “test it yourself”. The author asks why auditing falls on the buyer. The replies say that asking for proof that the model does not hallucinate is asking to prove a negative. Also, SOC 2 does not cover model behavior. The practical answer is for the buyer to build their own evaluation set.
Numbers
4 points and 26 commentsSize of the thread according to the archive.
Limitation A very low-scoring thread: it is useful as an idea, not as consensus. It was read in the public Arctic Shift archive because reddit.com blocks automated reading. The quote was compared letter by letter with the original.
“there are no frameworks for doing any kind of an audit of model behavior.”
Quote checked against the original
Read at the source. Checked on Nov 2, 2026.
Can you leave an agent alone once it works? The fax one, yes. For the rest, several IT threads describe the same problem. One sums it up in a sentence: “Then the person who created the agents leaves.” (exhibit 73). Another asks what happens to the agents of a person who leaves the company (exhibit 146). The employee offboarding checklist does not include them. The agent keeps running on a key nobody owns anymore.
In a forum for IT directors, someone proposed treating agents like service accounts (exhibit 138). A service account is an account for a system, not for a person. That calls for three things. An inventory of what each agent reads and writes. Credentials under a role. And a review every three months.
The formal sources point the same way. A NIST draft proposes tying each agent to whoever delegates to it and logging its actions (exhibit 50). According to BCG, only 5% of companies have every control in place today to give agents real autonomy. According to Deloitte, just 21% of leaders have mature governance for agentic AI. Governance means rules, roles and controls.
Limits also drift. A commenter described what happens within about three weeks (exhibit 104). Operators move about 40% of actions from confirm to automatic. It is a figure with no method behind it, but it describes a change you can watch for.
Once the agent works, it can be left running on its own.
Verdict: No. A narrow agent carries little risk, like one that sorts about 5,000 faxes a day with no power to do harm. The rest need an owner. When the creator leaves, the agent keeps running on a key that no longer belongs to anyone. A NIST draft proposes tying each agent to the person who delegates to it and logging its actions. And only 5% of the companies surveyed have all the controls today to give agents real autonomy.
Rule
Give every agent a named owner and a backup. Add an inventory of what it reads and writes, and a review every three months. Do not create the agent's key or account without an owner field. When someone leaves, review the agents they launched.
Why medium confidence. The cases come from Reddit threads, several with low scores, and the supporting side is a single comment. Both surveys are self-reported and come from consultancies. In the other survey, only 21% of leaders have mature governance for agentic AI. The NIST draft is a consultation, not a measurement. The sources agree, but nobody has measured how often an ownerless agent fails. One commenter adds that limits drift over time.
Show the 8 exhibits in detailHide the detail1 for, 6 against, 1 qualifies
This is the same author as the thread on connecting a model to internal data. Leadership now wants agents that trigger workflows and send emails. The most upvoted advice: get the business or legal to accept the risk in writing before giving the agent actions. In contrast, another describes a narrow agent that routes about 5,000 faxes a day to the right person, with no power to do harm.
Numbers
about 5,000 faxes a dayVolume a narrow agent routes, according to one commenter.
338 points and 129 commentsSize of the thread according to the archive. The quoted comment has 376 points.
Limitation Experience from people who run IT, without measurement. It was read in the public Arctic Shift archive because reddit.com blocks automated reading. The quote was compared letter by letter with the original.
“get it in writing from legal that you're not liable”
Booth, H., Fisher, B., Galluzzo, R., Roberts, J. NIST, NCCoE
It is a NIST draft for a demonstration project that applies identity standards to agents. It proposes to identify the agent as distinct from a person, and to authenticate and authorize it with OAuth 2.0 and policies. It also proposes to bind each agent to the person who delegates to it, and to log its actions. It asks how to know the sensitivity of the data the agent gathers, and whether the user may see the combined answer. It also asks how to set least privilege (only the permissions needed) when the agent's actions are not predictable.
Numbers
6 groups of questionsGeneral, identification, authentication, authorization, auditing and prompt injection. Comments from February 5 to April 2, 2026.
Limitation It is a draft for consultation, with questions rather than controls. It leaves RAG out of scope. The PDF (11 pages) was read; the project page returned 403.
“How do we establish "least privilege" for an agent”
The 2026 edition of BCG's study says the gap narrowed: almost half of companies already get value from AI. Many expect to give agents real autonomy, but almost none have the controls to do so today.
Numbers
about 48.5%Companies that generate value: 7.5% leaders (5% in 2025) and 41% scaling; out of 1,330 executives.
42% and 5%Expect to give agents real autonomy by 2030, and have all the controls today.
about 40%Share of AI value they expect from agents by 2030.
Limitation An announcement by a consultancy that sells AI services; self-report, with no detail on how it classifies companies. Automated download now returns 403, so it carries no verbatim quote.
It is a survey of IT and business leaders involved in AI programs. Most expect to use agents soon, but few have a mature governance model to control them. Intention moves faster than control.
Numbers
3,235 leaders in 24 countriesSurvey sample (January 2026 report).
74%Expect to use agents at least moderately by 2027.
21%Have a mature governance model for agentic AI.
Limitation Self-reported intention, not measured use. Other figures that circulate about this report (for example, how many pilots reached production) were only seen in coverage and are not used.
A thread on who owns an agent six months after the person who built it leaves. One comment compares it to the old Microsoft Access databases left loose in companies. Another says agents belong to teams, not to people. The most concrete proposal is to treat them as service accounts, which are accounts for programs. That needs an inventory of what they read and write, credentials under a role and a review every three months.
Numbers
26 points and 33 commentsSize of the thread according to the archive. The quoted comment has 5 points.
Limitation A small subreddit with some vendor comments; it is useful as an idea, not as consensus. The same idea appears in other threads on r/sysadmin, r/ITManagers and r/devops. It was read in the public Arctic Shift archive because reddit.com blocks automated reading. The quote was compared letter by letter with the original.
“nothing goes live without a named owner, a backup owner, and a review date”
The author asks what the employee offboarding checklist does with the agents that person launched. The agent keeps running on a service account or key that nobody owns anymore. One comment proposes making the owner a required field when someone creates the key or account. It also proposes rotating the key (replacing it with a new one) before shutting the agent down, to see who depends on it.
Numbers
6 points and 7 commentsSize of the thread according to the archive.
Limitation A very low-scoring thread: it is useful as an idea, not as consensus. It was read in the public Arctic Shift archive because reddit.com blocks automated reading. The quote is in the post text and was compared letter by letter.
“it keeps going on a service account or key nobody owns anymore”
Reporting from a security conference, the author says agents are being sold there as “virtual employees”. The comments bring it down to earth. An agent is a model call in a loop with tools, and its “memory” is files or a database. Another warns that afterwards there is no way to analyze why it decided something. A third describes the sprawl of agents that are orphaned when the person who created them leaves.
Numbers
356 points and 261 commentsSize of the thread according to the archive. The quoted comment has 86 points.
Limitation Opinions from people who run IT, without measurement. It was read in the public Arctic Shift archive because reddit.com blocks automated reading. The quote was compared letter by letter with the original.
A thread about who answers for what an agent does. One comment proposes three action levels: automatic, confirmed by a person and forbidden (for example, paying or deleting data). It also says the thresholds drift. After about three weeks, operators move about 40% of actions from confirm to automatic. That drift must be watched. Another comment asks to log why the agent chose each action.
Numbers
40% in about three weeksActions that move from confirm to automatic, according to one commenter.
Limitation A thread with a very low score (0 points and 16 comments in the archive). The 40% figure comes from one comment, without a method. It was read in the public Arctic Shift archive because reddit.com blocks automated reading. The quote was compared letter by letter with the original.
“Capability without visibility into why it chose action X”
Quote checked against the original
Read at the source. Checked on Nov 2, 2026.
That leaves shadow AI: technology people use without IT controlling it. The risk is real. An admin said on Reddit that leadership asked to connect a model to internal data (exhibit 37). The assistant showed sensitive documents to people who should not see them. The permissions were already wrong before the model.
Banning it looks like the obvious answer, but the demand is still there: that is Deloitte’s 74%. In a forum for IT directors, one proposal split the problem into two tracks (exhibit 140). The first is strict governance for anything released company-wide. The second is monitoring every agent: who creates it, what it does and how many tokens it spends. Another person described a rule that stops most cases. Whoever builds an AI app also has to maintain it.
The way to control agents people build on their own is to ban them.
Verdict: Banning does not solve the underlying problem, because the demand is there. 74% of surveyed leaders expect to use agents at least moderately by 2027. The risk is real. Connecting an assistant to internal data showed sensitive documents to people who should not see them. The permissions were already wrong. An IT thread proposes splitting into two lanes. One is strict governance for anything published company-wide. The other is monitoring of every agent.
Rule
Keep a register of every agent, with who created it, what it does and how many tokens it spends. Apply full governance to whatever is published company-wide, and ask whoever builds an agent to maintain it too.
Why low confidence. Both sides come from Reddit threads. The two-lane proposal comes from a small subreddit. The survey measures intent, not actual use. Nobody has measured whether banning or monitoring works better.
Show the 3 exhibits in detailHide the detail1 for, 1 against, 1 qualifies
Leadership asked to connect a language model to internal data. The assistant began showing sensitive documents to people who should not see them, because nobody had fixed permissions that were already wrong. The most upvoted comment says most organizations have weak data governance in SharePoint, Teams and OneDrive. Another proposes a data readiness and governance project before turning on the assistant.
Numbers
1,351 points and 198 commentsSize of the thread according to the archive. The quoted comment has 500 points.
Limitation Experience from people who run IT, without measurement. It was read in the public Arctic Shift archive because reddit.com blocks automated reading. The quote was compared letter by letter with the original.
“the LLM tool has access to what you give it/your people access to”
A thread on AI as the new shadow IT problem, meaning technology people use without IT control. One proposal separates two tracks. The first is hard governance for what is released company-wide. The second is monitoring of every agent: who creates it, what it does, traffic and tokens. Another says that asking whoever built an AI app to maintain it stops most cases.
Numbers
42 points and 24 commentsSize of the thread according to the archive. The quoted comment has 9 points.
Limitation A small subreddit; it is useful as an idea, not as consensus. It was read in the public Arctic Shift archive because reddit.com blocks automated reading. The quote was compared letter by letter with the original.
It is a survey of IT and business leaders involved in AI programs. Most expect to use agents soon, but few have a mature governance model to control them. Intention moves faster than control.
Numbers
3,235 leaders in 24 countriesSurvey sample (January 2026 report).
74%Expect to use agents at least moderately by 2027.
21%Have a mature governance model for agentic AI.
Limitation Self-reported intention, not measured use. Other figures that circulate about this report (for example, how many pilots reached production) were only seen in coverage and are not used.
None of the five ideas in the opening scene passes the trial as stated:
Counting agents or tokens measures activity, not results.
The cases that lasted started by reading and added actions with review.
Building is not the safe choice, and buying requires testing before you sign.
An agent with no owner keeps running when nobody is watching.
A ban hides the demand instead of removing it.
This evidence carries little weight. Two of the five claims have low confidence and the other three, medium. Almost everything comes from Reddit threads and self-reported surveys by consultancies. The framework below helps you decide with what is known today. It gets corrected when better evidence arrives.
The framework is a sequence of seven steps. Any step can end in “you do not need an agent”. That outcome is as valid as the others.
Decide whether you need an agent. If you can write the steps in advance, use a fixed workflow. If there are few cases and a mistake is costly, use a person.
Write down which business number it has to move, by how much and by when.
Look at the risk before building: what it reads, what data it touches and what it can do externally.
If you buy, test the agent on your own tasks before signing and ask to see what it does step by step. If you build, start with the simplest option. No source shows that one path performs better, so pick the one that lets you test first on your own tasks.
Start with an agent that reads and proposes. Add actions one at a time, with a written risk acceptance.
Give it a named owner and a backup. Record it in a register of every agent.
Measure problems solved, not agents deployed.
The evaluator handles the first step with ten questions. It recommends one of five outcomes: fixed workflow, bounded agent, agent with human approval, person, or not yet. The ADR puts the decision in writing. An ADR is a record of an architecture decision. The design review requires the other templates as prerequisites.
Design security into what the agent sees and does. In one session, at most two of three: untrusted content, sensitive data, external output. If it needs all three, human approval for what goes out. Filters are an alarm, not a lock.
Use the standards to avoid vendor lock-in and design security on top of them. Fix permissions before connecting internal data. Treat each MCP server as third-party code, with an owner, a pinned version and tools enabled one by one.
Put the person where their attention changes the outcome. Isolate first and enforce the three levels outside the model. Keep human approval for a few irreversible actions, tied to the exact action.
Do not promise what you have not measured on your own tasks. Build 20 to 50 from real failures. If there is no test first, measure whether it gets every repetition right. Recalibrate the model judge with every new model.
Measure what each successful task costs, per workflow and with review included. Budget for a price that doubles. Set a spending cap that stops the agent and alerts a person.
Design for the strictest rule already in force. Disclose that it is an AI in the first message. Treat every agent with personal data as data processing.
Each rule comes from the claims in its chapter. If new evidence changes a verdict, the claim changes and the rule changes with it. To propose a source or challenge a claim, read the methodology.