> ## Content Index > Fetch the complete content index at: https://www.praxiscto.com/llms.txt > Use this file to discover other available public pages before exploring further. # Managing Agents Is Like Managing People - URL: https://www.praxiscto.com/managing-agents-is-like-managing-people/ - Published: 2026-10-08T12:00:00.000Z - Updated: 2026-10-08T12:00:00.000Z - Description: Micromanaging AI agents comes from a reasonable place, but it defeats the purpose. Effective agent management looks a lot like effective delegation to a person. - Author: Matt Shirel - Tags: AI & Automation, Managing Agents Like People *This is Part 1 of the* [*Managing Agents Like People*](https://www.praxiscto.com/tag/managing-agents-like-people/) *series, applying people-management discipline to AI agent oversight.* ## The Instinct That Undermines the Point A common pattern when companies first [start using AI agents for real work](https://www.praxiscto.com/what-agentic-ai-actually-means/): enthusiasm for the potential, followed almost immediately by an instinct to review every single step the agent takes before trusting any of it. Every intermediate action gets checked. Every output gets second-guessed from scratch, as if the agent hadn't done any of the work. This instinct comes from a reasonable place — the technology is new, the failure modes aren't fully understood yet, and the stakes of a bad output feel high. But taken to its natural conclusion, it defeats the entire purpose of delegating the work to an agent in the first place. If a human has to carefully verify every intermediate step, the agent isn't actually saving meaningful time — it's just producing a longer, more elaborate first draft that still requires the same close supervision the human would have needed to do the work directly. ## The Parallel That Actually Helps The useful mental model isn't "how do I supervise a piece of software." It's "how do I delegate to a person I've recently hired and don't yet have a long track record with." That's a problem every manager has already solved, repeatedly, and the same instincts largely transfer. A manager doesn't supervise a new hire's every keystroke. They give the new hire clearly scoped work, they check in at specific points — not randomly, and not on everything — and they verify the actual outcome rather than re-doing the work themselves to confirm it. Over time, as the new hire builds a track record, the checkpoints get lighter and the scope of what they're trusted with grows. Nobody treats this as reckless; it's just how competent delegation works, because the alternative — permanently supervising every detail — doesn't scale and doesn't actually build a more capable team. Agents deserve the same basic approach: clear scope, checkpoints placed at the moments that actually matter, and verification of outcomes rather than a re-litigation of every step along the way. ## Where the Parallel Breaks Down — and Why That Matters The analogy is useful, not perfect, and the places it breaks down matter for how oversight should actually be designed. An agent doesn't get tired, doesn't have a bad day, and doesn't get defensive when a piece of its work gets questioned — all real advantages over managing people, and part of why agents are genuinely useful for a lot of repetitive or high-volume work. But an agent also doesn't have the kind of broad, contextual judgment a reasonably experienced person has about when something is unusual enough to be worth flagging on its own initiative, unless that judgment has been explicitly built into how the agent is scoped and instructed. A new employee who encounters something genuinely strange in the course of routine work — a customer request that doesn't fit any normal pattern, a number that looks obviously wrong — will usually pause and flag it, even without being told to, because they have general-purpose judgment about what "looks off." An agent generally won't, unless "flag anything that deviates from this expected pattern" was explicitly part of its instructions. This isn't a flaw to be embarrassed about — it's a structural fact about how these systems work, and it means the checkpoints and escalation rules have to be more explicit and more deliberately designed than they would need to be for a person with general judgment already built in. ## Sizing Oversight to Stakes and Reversibility The right level of oversight for any given task — whether delegated to a person or an agent — is a function of two things: how high the stakes are if it goes wrong, and how easily that mistake can be caught and reversed before it causes real damage. Low-stakes, easily reversible work — drafting a first version of an internal document, categorizing incoming support tickets, generating a summary that a human will review before acting on it — can run with light oversight, because the cost of an occasional mistake is low and the mistake is easy to catch and fix. High-stakes, hard-to-reverse work — anything that sends something external-facing, commits money, or touches sensitive data without further review — needs a human checkpoint before the consequential action happens, not just spot-checking after the fact. This is exactly the same calculation a manager makes when deciding how closely to supervise a person's work on a specific task, and it should be made the same way for agents: task by task, based on actual stakes, not as a blanket policy of either full autonomy or full supervision applied uniformly regardless of what's actually at risk. ## Building Trust Incrementally, the Same Way You Would With a Person ![Trust built like a new hire's: scope widens as checkpoints thin out.](https://storage.ghost.io/c/eb/09/eb09d71a-7468-4462-94d3-73cc9c2df39d/content/images/2026/10/blog-5-managing-agents-x-1200x675-v2.png) Start narrow with real oversight, then widen the scope as the track record grows. Trust in an agent, like trust in a new employee, is something that should be built up based on demonstrated track record in a bounded scope — not granted all at once on day one, and not permanently withheld out of caution that never gets revisited either. A practical pattern: start an agent on a narrow, low-stakes piece of a workflow with real oversight, let it build a track record over real volume, and only then expand its scope or lighten the checkpoints — the same progression a manager would use with a promising but unproven new hire. This is different from either extreme that companies tend to default to: full autonomy from day one because the demo looked impressive, or permanent heavy supervision because the technology feels unfamiliar and the caution never gets revisited once the track record actually justifies loosening it. ## What Good Oversight Actually Looks Like None of this argues for blind trust in agents, any more than good management argues for blind trust in new employees. It argues for oversight that's actually designed around where the real risk is, rather than oversight that's uniformly heavy because the technology is unfamiliar, or oversight that's absent because the technology is exciting. The companies getting real, sustained value out of agentic AI right now aren't the ones supervising every step, and they aren't the ones granting full autonomy on day one either. They're the ones applying the same delegation discipline to agents that a good manager already applies to people — clear scope, checkpoints where they matter, and trust that's earned incrementally rather than assumed or permanently withheld. --- [*Subscribe*](#/portal/signup) *for more like this.* --- *In the spirit of this site: this article was drafted with AI assistance and edited by Matt before publishing — the same kind of human-in-the-loop review this piece argues for.* **About the Author:** Matt Shirel writes Praxis CTO, exploring what real technology leadership looks like for growing companies that have outgrown ad hoc decision-making. He brings over 20 years in enterprise IT — spanning infrastructure architecture, cloud strategy, and technology budget ownership — and works hands-on in applied AI, building multi-agent and RAG workflows with tools like n8n and AI coding agents — work that's made the parallel between managing people and managing agents hard to ignore.