{"id":5198,"date":"2025-12-17T01:26:34","date_gmt":"2025-12-17T01:26:34","guid":{"rendered":"https:\/\/imaworldwide.com\/?p=5198"},"modified":"2026-02-08T20:23:38","modified_gmt":"2026-02-08T20:23:38","slug":"who-watches-the-bots","status":"publish","type":"post","link":"https:\/\/imaworldwide.com\/who-watches-the-bots\/","title":{"rendered":"Who Watches the Bots?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"5198\" class=\"elementor elementor-5198\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8d2a80f e-flex e-con-boxed e-con e-parent\" data-id=\"8d2a80f\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1b34cb3 elementor-widget elementor-widget-html\" data-id=\"1b34cb3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t\t<style>\r\n\/* Scoped styling for this blog post only (NO font overrides) *\/\r\n.ph-blog {\r\n  max-width: 920px;\r\n  margin: 0 auto;\r\n  font-family: inherit;\r\n  line-height: 1.7;\r\n  color: #101211; \/* Obsidian Veil *\/\r\n}\r\n\r\n\/* Headings: inherit global font + global sizes (NO font-size here) *\/\r\n.ph-blog h1, .ph-blog h2, .ph-blog h3, .ph-blog h4 {\r\n  font-family: inherit;\r\n  line-height: 1.25;\r\n  margin: 1.35rem 0 0.75rem;\r\n}\r\n\r\n\/* Color-only + simple underline style (NO left border \/ sidebar) *\/\r\n.ph-blog h1 {\r\n  color: #2f4580; \/* Midnight Cascade *\/\r\n  margin-top: 0;\r\n}\r\n\r\n.ph-blog h2 {\r\n  color: #2f4580; \/* Midnight Cascade *\/\r\n  padding-bottom: 0.35rem;\r\n  border-bottom: 1px solid rgba(16,18,17,0.12);\r\n}\r\n\r\n.ph-blog h3 { color: #05554a; \/* Evergreen Horizon *\/ }\r\n.ph-blog h4 { color: #101211; }\r\n\r\n.ph-blog p { margin: 0.75rem 0; }\r\n\r\n.ph-blog a {\r\n  color: #05554a;\r\n  text-decoration: underline;\r\n  font-weight: 600;\r\n}\r\n\r\n.ph-blog .small {\r\n  font-size: 0.95rem;\r\n  color: rgba(16,18,17,0.78);\r\n}\r\n\r\n\/* Top blocks: \"colors behind\" look *\/\r\n.ph-blog .tldr {\r\n  margin: 1rem 0 1.25rem;\r\n  padding: 1.05rem 1.15rem;\r\n  border-radius: 12px;\r\n  border: 1px solid rgba(16,18,17,0.10);\r\n  background: rgba(205,198,198,0.28); \/* Desert Dusk tint *\/\r\n}\r\n\r\n.ph-blog .tldr strong { color: #2f4580; }\r\n\r\n.ph-blog .lede {\r\n  margin: 1rem 0 1.25rem;\r\n  padding: 1.05rem 1.15rem;\r\n  border-radius: 12px;\r\n  border: 1px solid rgba(16,18,17,0.10);\r\n  background: rgba(47,69,128,0.06); \/* Midnight tint *\/\r\n}\r\n\r\n.ph-blog .callout {\r\n  margin: 1.25rem 0;\r\n  padding: 1.05rem 1.15rem;\r\n  border-radius: 12px;\r\n  border: 1px solid rgba(16,18,17,0.10);\r\n  background: rgba(5,85,74,0.06); \/* Evergreen tint *\/\r\n}\r\n\r\n.ph-blog .callout strong { color: #05554a; }\r\n\r\n.ph-blog .quote {\r\n  margin: 1.25rem 0;\r\n  padding: 1.05rem 1.15rem;\r\n  border-radius: 12px;\r\n  background: rgba(205,198,198,0.35); \/* Desert Dusk tint *\/\r\n  border: 1px solid rgba(16,18,17,0.10);\r\n}\r\n\r\n.ph-blog .quote p { margin: 0; font-style: italic; }\r\n\r\n.ph-blog ul, .ph-blog ol { margin: 0.6rem 0 1rem 1.2rem; }\r\n.ph-blog li { margin: 0.35rem 0; }\r\n\r\n.ph-blog .divider {\r\n  margin: 2rem 0 1.25rem;\r\n  border: 0;\r\n  height: 1px;\r\n  background: rgba(16,18,17,0.12);\r\n}\r\n\r\n.ph-blog .image-note {\r\n  margin: 0.75rem 0 1.25rem;\r\n  padding: 0.9rem 1rem;\r\n  background: #f7f7f7;\r\n  border: 1px dashed rgba(16,18,17,0.18);\r\n  border-radius: 10px;\r\n  font-size: 0.95rem;\r\n  color: rgba(16,18,17,0.85);\r\n}\r\n\r\n\/* Button *\/\r\n.ph-blog .btn-wrap { text-align: center; margin: 0.75rem 0 0; }\r\n\r\n.ph-blog .btn {\r\n  display: inline-block;\r\n  padding: 12px 18px;\r\n  background: #2f4580;\r\n  color: #ffffff !important;\r\n  border-radius: 10px;\r\n  text-decoration: none !important;\r\n  font-weight: 700;\r\n}\r\n\r\n.ph-blog .btn:hover { opacity: 0.92; }\r\n\r\n\/* CTA block *\/\r\n.ph-blog .cta {\r\n  margin: 2rem 0 0;\r\n  padding: 1.15rem 1.15rem;\r\n  border-radius: 14px;\r\n  background: #2f4580;\r\n  color: #ffffff;\r\n}\r\n\r\n.ph-blog .cta h3 { margin: 0 0 0.5rem; color: #ffffff; }\r\n.ph-blog .cta p { margin: 0.5rem 0; }\r\n\r\n.ph-blog .cta a {\r\n  color: #ffffff !important;\r\n  text-decoration: underline;\r\n  font-weight: 700;\r\n}\r\n\r\n\/* Q&A styling *\/\r\n.ph-blog .qa-item { margin: 1.25rem 0; }\r\n.ph-blog .qa-item strong { color: #2f4580; }\r\n<\/style>\r\n\r\n<section class=\"ph-blog\">\r\n\r\n\r\n  <div class=\"tldr\">\r\n    <p><strong>TL;DR<\/strong><\/p>\r\n    <p>This blog revisits Juvenal's \"Who watches the watchmen?\" through the lens of AI, where agentic systems supervise other agents, creating recursive layers of oversight. It traces cultural echoes of the question, shows how modern organizations have amassed more watchers than doers, and explains why AI's tendency to guess fuels the urge to monitor. The core argument contrasts mere surveillance with true involvement: effective governance requires accountable humans who are in the work, not just adding layers. The 2025 answer is layered \u2014 agents do the work, an agentic layer monitors them, and humans actively oversee that layer.<\/p>\r\n  <\/div>\r\n\r\n  <h2>Quis Custodiet Ipsos Custodes?<\/h2>\r\n\r\n  <p>I was listening to the Everyday AI podcast \u2014 Episode 671, \"From Automation to Agents: Why Weak Data Makes AI Guess\" \u2014 when Ed Macosky, Chief Product and Technology Officer at Boomi, said something that made me laugh \u2014 then immediately made me frustrated.<\/p>\r\n\r\n  <p>He described using \"an agentic layer to oversee the agents.\" Bots watching bots. And I thought: We've been doing this with humans for decades. How's that working out?<\/p>\r\n\r\n  <p>Turns out, a Roman satirist asked the same question 2,000 years ago.<\/p>\r\n\r\n  <div class=\"quote\">\r\n    <p>\"Using another layer of an agentic layer to oversee the agents that are running so that if you see some anomaly behavior like, hey, agents have been doing a, b, and c, but this one looks like it may have stepped out of bounds. It will alert the business user that, hey, this agent did this. Or you might wanna check on this, or do you want me to stop this agent from behaving here.\"<\/p>\r\n  <\/div>\r\n\r\n  <p>An agentic layer to oversee the agents.<\/p>\r\n\r\n  <p>Bots watching bots.<\/p>\r\n\r\n  <p>And suddenly I'm thinking: who watches the watchers?<\/p>\r\n\r\n  <hr class=\"divider\" \/>\r\n\r\n  <h2>An Ancient Question<\/h2>\r\n\r\n  <p>So I asked Claude where this statement came from.<\/p>\r\n\r\n  <p>Around 100 CE, the Roman satirist Juvenal wrote a line in his Satires that has echoed through two millennia: \"Quis custodiet ipsos custodes?\" \u2014 Who will watch the watchmen?<\/p>\r\n\r\n  <p>Juvenal's original context was about the futility of posting guards. If the guards themselves can be corrupted, the whole system fails. Plato wrestled with the same question in The Republic: Who guards the guardians?<\/p>\r\n\r\n  <hr class=\"divider\" \/>\r\n\r\n  <h2>The Question That Won't Die<\/h2>\r\n\r\n  <p>Juvenal's question keeps showing up everywhere. Alan Moore built an entire graphic novel around it \u2014 Watchmen \u2014 where the phrase appears as graffiti throughout New York City, always partially obscured, never fully visible. (Moore didn't even know the phrase came from Juvenal until Harlan Ellison told him.)<\/p>\r\n\r\n  <p>Star Trek: The Next Generation has an episode called \"Who Watches the Watchers\" \u2014 but notably, it doesn't give an answer. It shows what happens when surveillance fails. Federation anthropologists are secretly observing a primitive civilization from a hidden outpost. The system breaks. A local sees Captain Picard and concludes he must be a god. Religious fervor spreads. They nearly sacrifice Troi. The episode demonstrates the problem; it doesn't solve it.<\/p>\r\n\r\n  <p>Terry Pratchett's Sam Vimes, commander of the Ankh-Morpork City Watch, gives maybe the best answer. When asked \"Who watches the watchmen?\" he replies: \"Me.\"<\/p>\r\n\r\n  <hr class=\"divider\" \/>\r\n\r\n  <h2>More Watchers Than Doers<\/h2>\r\n\r\n  <p>Here's what struck me listening to Macosky describe bots watching bots: we've been building toward this for decades with humans.<\/p>\r\n\r\n  <p>The U.S. Bureau of Labor Statistics reports that management occupations are projected to grow faster than the average for all occupations, with 1.1 million openings per year. The Project Management Institute says 25 million new project management professionals will be needed globally by 2030. PMI research shows 89% of companies now have a Project Management Office \u2014 a department whose job is to watch projects.<\/p>\r\n\r\n  <p>We've built organizations with layers upon layers of oversight. Managers watching teams. Project managers watching projects. PMOs watching project managers. Compliance officers watching processes. Quality assurance watching outputs. Internal audit watching everyone.<\/p>\r\n\r\n  <p>And now we're replicating this pattern with AI. Agents doing the work. An agentic layer watching the agents. A control tower watching the agentic layer. Dashboards watching the control tower.<\/p>\r\n\r\n  <div class=\"callout\">\r\n    <p><strong>The irony isn't lost on me:<\/strong> Gartner predicts that 80% of current project management tasks will be eliminated by 2030 as AI takes over. We're automating the watchers \u2014 and then building new watchers to watch the automated watchers.<\/p>\r\n  <\/div>\r\n\r\n  <hr class=\"divider\" \/>\r\n\r\n  <h2>Why We Need Watchers (Apparently)<\/h2>\r\n\r\n  <p>Earlier in the podcast, host Jordan Wilson made a point that explains the urgency. The old automation \u2014 deterministic, rule-based \u2014 would simply fail when something went wrong. No output. You knew there was a problem.<\/p>\r\n\r\n  <p>But these new AI agents don't fail gracefully. As Wilson put it: \"It might straight up lie or guess.\" The agent keeps producing outputs even when underlying data is incomplete. It fabricates. It confidently delivers wrong answers.<\/p>\r\n\r\n  <p>So you can't just let agents run unsupervised. You need something watching them. And when you have thousands of agents \u2014 which is where we're headed \u2014 you can't watch them manually. So you build a system to watch them for you.<\/p>\r\n\r\n  <p>Bots watching bots.<\/p>\r\n\r\n  <hr class=\"divider\" \/>\r\n\r\n  <h2>But Who Watches the Watchers?<\/h2>\r\n\r\n  <p>And this is where Juvenal's question gets recursive.<\/p>\r\n\r\n  <p>If agents can guess and fabricate, what about the agentic layer watching them? What happens when the control tower itself has incomplete data? What happens when the guardrails are misconfigured? What happens when the watcher steps out of bounds?<\/p>\r\n\r\n  <p>Plato's answer was that the guardians must guard themselves \u2014 through proper training and cultural formation. They needed to internalize values so deeply that external monitoring became secondary.<\/p>\r\n\r\n  <p>In enterprise AI, that translates to leadership. Automated control towers are necessary infrastructure. But somewhere in this stack of bots watching bots watching bots, there have to be humans. Humans who actively define acceptable behaviors. Humans who review the alerts. Humans who answer the question: do you want me to stop this agent?<\/p>\r\n\r\n  <div class=\"callout\">\r\n    <p><strong>The pattern I keep seeing:<\/strong> organizations deploy the governance platform, announce the policy, and assume the problem is solved. Then Shadow AI thrives anyway because no one is actually watching the watchers.<\/p>\r\n  <\/div>\r\n\r\n  <hr class=\"divider\" \/>\r\n\r\n  <h2>Wait, Why Can't They Just Work?<\/h2>\r\n\r\n  <p>I was talking with Claude about this article \u2014 yes, using AI to think through AI governance, because that's where we are now \u2014 and asked about the TV show references. Which episode? What season?<\/p>\r\n\r\n  <p>And as we went through the list, something else bubbled up. A different question. Less philosophical, more visceral:<\/p>\r\n\r\n  <p>Why don't all these watchers just\u2026 work?<\/p>\r\n\r\n  <p>Seriously. How many people have to stand around watching while someone else does the actual work? We've created entire organizational structures where the ratio of watchers to doers keeps climbing. And now we're automating that same pattern.<\/p>\r\n\r\n  <p>It gave me flashbacks to an argument everyone has had at least once: \"If you don't like the way I do the dishes, then you do them.\"<\/p>\r\n\r\n  <hr class=\"divider\" \/>\r\n\r\n  <h2>The Dishes Problem<\/h2>\r\n\r\n  <p>That dishes argument captures something essential that gets lost in all our talk of governance layers and control towers.<\/p>\r\n\r\n  <p>At some point, someone's doing the work. And three people are watching them do it. And one of them doesn't like how it's being done. But instead of doing it themselves \u2014 instead of getting their hands wet \u2014 they add another layer of oversight. A checklist. A review meeting. A dashboard. An agentic layer.<\/p>\r\n\r\n  <p>The person doing the dishes is standing there thinking: \"Either trust me to do this or take over, but stop hovering.\"<\/p>\r\n\r\n  <p>Don Harrison, founder of IMA's Accelerating Implementation Methodology (AIM), would recognize this immediately. His 40+ years of research draws a critical distinction between involvement and surveillance. Real leadership involvement means you're in the work \u2014 expressing the business case, modeling commitment, reinforcing behaviors in your direct reports. You're doing dishes alongside people, not standing behind them with a clipboard.<\/p>\r\n\r\n  <p>But what we've built instead? Layers of watchers who may or may not own the outcome, exist to catch problems rather than create success, and have every incentive to find fault \u2014 because that's their job.<\/p>\r\n\r\n  <p>The AI version just makes it more absurd. Bots watching bots watching bots \u2014 and somewhere at the bottom, one agent is actually trying to do something.<\/p>\r\n\r\n  <div class=\"quote\">\r\n    <p>The dishes metaphor exposes the core dysfunction: watching isn't helping. Watching isn't working. At some point, somebody has to wash the dish.<\/p>\r\n  <\/div>\r\n\r\n  <hr class=\"divider\" \/>\r\n\r\n  <h2>The Layered Answer<\/h2>\r\n\r\n  <p>Quis custodiet ipsos custodes?<\/p>\r\n\r\n  <p>In 2025, the answer is layered: Agents run the processes. An agentic layer watches the agents. And humans \u2014 with sustained attention, not occasional check-ins \u2014 watch the agentic layer.<\/p>\r\n\r\n  <p>We've spent decades adding layers of human oversight. Now we're automating the oversight and adding layers of automated oversight. The question Juvenal asked two thousand years ago isn't going away. It's just getting more recursive.<\/p>\r\n\r\n  <p>Or as Sam Vimes would say: \"Me.\"<\/p>\r\n\r\n  <p>Somebody still has to be the \"me.\" And increasingly, that \"me\" needs to be involved in the work \u2014 not just watching from a safe distance while the dishes pile up.<\/p>\r\n\r\n  <p>Or maybe we could all just do some dishes.<\/p>\r\n\r\n  <p class=\"small\">Thanks to Jordan Wilson and Ed Macosky for a podcast episode that connected enterprise AI governance to an ancient question \u2014 and to Claude for helping me realize that sometimes the best insight comes from asking \"wait, why can't they just work?\"<\/p>\r\n\r\n  <hr class=\"divider\" \/>\r\n\r\n  <h2>Q&A<\/h2>\r\n\r\n  <div class=\"qa-item\">\r\n    <p><strong>Question: What does \"bots watching bots\" actually mean?<\/strong><\/p>\r\n    <p>Short answer: It describes a layered setup where AI agents do the work, and a separate \"agentic layer\" monitors those agents for anomalies, policy breaches, or poor outcomes. As Ed Macosky put it, this overseer can alert a human \u2014 \"this agent did this; do you want me to stop it?\" \u2014 or even halt the offending agent. In practice, organizations often add a \"control tower\" and dashboards above that layer to summarize and escalate what the overseers see.<\/p>\r\n  <\/div>\r\n\r\n  <div class=\"qa-item\">\r\n    <p><strong>Question: Why do AI agents need this kind of oversight when old-school automation often didn't?<\/strong><\/p>\r\n    <p>Short answer: Traditional, rule-based automation typically failed visibly \u2014 no output meant you knew something broke. Modern AI agents, by contrast, can keep producing outputs even with weak or incomplete data; as the podcast noted, they might \"lie or guess.\" At scale, you can't manually check thousands of agents' outputs, so you need automated layers that watch for drift, fabrication, and out-of-bounds behavior.<\/p>\r\n  <\/div>\r\n\r\n  <div class=\"qa-item\">\r\n    <p><strong>Question: How does \"Who watches the watchmen?\" apply to enterprise AI?<\/strong><\/p>\r\n    <p>Short answer: The question becomes recursive: if agents can guess, the agentic layer that watches them can also err \u2014 misread signals, use incomplete data, or be misconfigured. Cultural touchstones (from Watchmen to Star Trek) dramatize this failure of surveillance. The essay's answer is that humans must ultimately watch the watchers \u2014 echoing Plato's call for well-formed guardians and Sam Vimes's \"Me.\" In concrete terms, accountable humans define acceptable behavior, review alerts, and decide when to stop or change an agent's actions.<\/p>\r\n  <\/div>\r\n\r\n  <div class=\"qa-item\">\r\n    <p><strong>Question: Aren't we already overloaded with \"watchers,\" and are we repeating that pattern with AI?<\/strong><\/p>\r\n    <p>Short answer: Yes. Organizations have long layered oversight on oversight: managers, project managers, PMOs (now in 89% of companies), compliance, QA, and internal audit. The BLS projects 1.1 million management openings per year; PMI forecasts 25 million new PM roles by 2030. Ironically, Gartner predicts 80% of current PM tasks will be automated by 2030 \u2014 so we're automating the watchers and then building new watchers to watch them. The risk is surveillance without ownership: lots of hovering, little accountability, and flourishing Shadow AI because no one is truly \"in\" the work.<\/p>\r\n  <\/div>\r\n\r\n  <div class=\"qa-item\">\r\n    <p><strong>Question: What does effective oversight look like in 2025 \u2014 beyond just adding dashboards?<\/strong><\/p>\r\n    <p>Short answer: A layered but engaged model: agents run processes; an agentic layer monitors agents; and humans provide sustained, accountable involvement over that layer. That means clearly defining guardrails, reviewing and acting on alerts, adjusting configurations, and stepping in to help \"do the dishes\" when needed. Drawing on Don Harrison's distinction, it's involvement (being in the work) rather than mere surveillance (standing back with a clipboard). Ultimately, somebody has to be the \"Me\" who owns the outcome.<\/p>\r\n  <\/div>\r\n\r\n<\/section>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>TL;DR This blog revisits Juvenal&#8217;s &#8220;Who watches the watchmen?&#8221; through the lens of AI, where agentic systems supervise other agents, creating recursive layers of oversight. It traces cultural echoes of the question, shows how modern organizations have amassed more watchers than doers, and explains why AI&#8217;s tendency to guess fuels the urge to monitor. The [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":5213,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_title":"AI Governance: Who Watches the Bots? | Peacock Hill Consulting","_yoast_wpseo_metadesc":"A podcast about AI agents made me think of a 2,000-year-old question. We keep adding watchers. But who watches the watchers?","_metasync_otto_title":"AI Governance: Who Watches the Bots? Oversight in Automation | IMA Worldwide","_metasync_otto_description":"AI Oversight: IMA Worldwide explores who watches the bots. Understand the need for accountability in automated decision-making.","_metasync_otto_keywords":"AI oversight, automation accountability, monitoring AI systems, ethical AI, philosophical implications of automation, risks of AI decision-making, automated processes management. IMA Worldwide","_metasync_otto_og_title":"Who Watches AI Bots? - IMA Worldwide","_metasync_otto_og_description":"Who oversees AI decision-making? Discover effective governance with IMA Worldwide.","_metasync_otto_twitter_title":"Who Oversees AI? IMA Worldwide #AIethics","_metasync_otto_twitter_description":"Who oversees AI automation? 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