Delegation Versus Dumping in the Age of Agentic AI

There’s a leadership pattern I’ve seen for decades: the leaders who practice real delegation build capacity and resiliency; the ones who “dump” work create thrash. In 2025, the divide is stark in how teams and individuals use AI. Strategic delegators are already compounding output with agentic systems. Dumpers are stuck, confused by inconsistent results.  

Delegation is intentional. You assign outcomes, transfer context, define constraints, set decision rights and coach toward success. Dumping is offloading a problem without the information, authority or support required to succeed. 

The distinction isn’t new. What changes in 2025 is the span of control. In addition to orchestrating resources from within the company and outsourcing, leaders now orchestrate work handed to AI services and agentic systems.  

Agentic AI is goaldriven and plandriven 

Agentic systems reason, plan and act. They do well when you specify goals, subtasks, tools, constraints and evaluation, and they degrade when the ask is vague. In a recent blog, NVIDIA explains it simply: agents handle multistep problems using iterative planning and action. OpenAI’s recent agent tooling leans into that structure: explicit goals, tool use, orchestration and observability. If you don’t delegate with that structure, your agent flails.  

A lot of agent projects get scrapped for unclear business value and immature scoping. (In fact, Gartner estimates over 40% of agentic AI projects will be canceled by 2027 for these reasons and others.) That’s just “dumping” with fancier APIs. 

An example of dumping is telling an agent or designer, “Make a slide image.” There’s no context or clear explanation. Delegation to a designer or an AI agent looks more like this:  

• Objective: Visual for a conference slide that communicates “Threats move at machine speed; you need machinespeed defense.” 

• Audience: CISOs; enterprise security architects. 

• Format: 16:9 PNG, 1920×1080, dark theme, accessible contrast. 

• Constraints: Brand palette; no stock shield icons. 

• References: Two example slides we like; mood board link. 

• Definition of Done: Two concepts; one round of revision; ship by Friday 5 p.m. ET. 

When you can hand that spec to a contractor and get a bid, you can hand it to an agent and get a plan. That’s the tell. 

Why prompts matter (and why AI doesn’t tolerate dumping) 

The top vendor guides consistently repeat the same advice: be specific, provide structure, offer examples, and define success. That’s delegation. You wouldn’t delegate something to your team without the specifics; the same goes for AI.  

If your input is vague, the model will improvise, and you’ll call it “hallucination.” It’s a classic “garbage in, garbage out” scenario.  

However, if you upgrade your prompts into mini-specs – role + objective + constraints + canonical examples + evaluation criteria – you’ll immediately see the difference. 

Closing the loop: Make delegation your AI superpower 

Delegation is timeless, but agentic AI raises the stakes. If you can articulate the outcome, constraints and definition of done, you can multiply your team with software. If you can’t, AI will mirror your ambiguity fast. 

Go through these five quick checks to determine whether delegation or dumping is taking place: 

1. Can this be priced? Have you provided enough detail that a contractor could scope the work and give you a fixed price or a nottoexceed estimate? If yes, you’re delegating. If no, you’re dumping. Public Statement of Work (SOW) guidance exists for a reason. 
2. Is “Done” testable by someone who isn’t you? Could a third party say whether the work meets the Definition of Done without reading your mind? If not, tighten the acceptance criteria.
3. Are decision rights explicit? What can the assignee (human or agent) decide without you? What requires a checkpoint? Lack of decision rights converts delegation into dependency.
4. Are constraints and resources attached? Attach security permissions, data access, examples, brand rules and service level agreements (SLAs) up front. Designfirst/APIfirst is a good muscle to build here.
5. Is there a coaching/feedback loop? Delegation without coaching is abdication. Even Harvard Business Review’s decades of delegation advice ultimately center on context, autonomy and coaching. 

These are the four specdriven practices to adopt now to supercharge your delegation skills: 

• Apply the Definition of Done everywhere, not just engineering.

• Write acceptance criteria in a structured, testable format whenever it makes sense so that they can be understood by everyone and potentially automated as part of the testing process. 

• Designfirst specs before build.  Develop a template that includes all the normal things you would declare when describing the work to be done to someone like a contractor. 

• Constraints & Boundaries: All non-negotiable limitations. 

• Performance Goals/service level objectives (SLOs): Specific, measurable targets for speed and efficiency. 

• Key capabilities(Non-Functional Requirements): Availability, Scalability, Security, Maintainability/Serviceability, to name a few 

Anything you want to make certain, take the time to define it for the AI.  This is not the time for the AI to get creative.  

Delegation is the new oil 

I expect 2026 to create more jobs for machines than for humans – specialized agent roles across every department. The winning formula will be to keep headcount flat and 5x output by pairing people with agents and delegating well. Studies like this one from McKinsey show the productivity upside is real if we redeploy time effectively and structure work; the teams that master specfirst delegation will capture the gains first.  

And a final caution: don’t confuse hype for capability. Agentic AI is accelerating fast, but value comes from clear tasks, clear specs and clear evaluation. Do that, and your five people really can produce like fifteen. Skip it, and you’ll just scale the chaos. Delegation creates clarity; dumping creates gravity. In the age of agentic AI, clarity compounds.