When to Delegate to Agents (and When Not To)

Not every task should be handled by an AI agent, and that’s not a limitation. It’s a strategy. In real software development, the question isn’t “Can an agent do this?” but “Should it?” After dozens of internal experiments at Nybble, one pattern became clear: the real intelligence doesn’t only exist in the model but in choosing when to use it.

As Nybble’s Director of Technology and Innovation, Leo Rodriguez, said during an internal session, the challenge isn’t adopting agents, but understanding where autonomy helps and where it creates friction: “We have to decide what we can delegate and what we can’t. Not blindly; that’s the mistake. We need to understand what level of autonomy still gives us a good result, and what level stops making sense. There are tasks you can delegate with control, and others where you’re only adding noise.”

This article is about that judgment: where agents accelerate development, where they slow you down, and how Nybble learned the difference through real, imperfect, practical work.

When Delegation Backfires: The Spring Migration

One of Nybble’s earliest agent-heavy experiments was a migration to a newer version of Spring: a repetitive job, lengthy, and mechanically predictable. On paper, a perfect match for automation.

And in execution? It moved fast. Very fast. But without proper context, planning and documentation, the initial speed turned into extra work later. The agent performed large chunks of the migration quickly, but each piece needed to be revisited, tested, corrected, and stabilized.

Leo recalls it clearly: “We didn’t plan. We didn’t prepare the context. We just started iterating with the agent. And yes, it moved fast, yes, but that speed became double the work. Fixing, checking, going back and forth. The model was good, but without structure we ended up doing many more iterations than necessary. Speed without planning is an illusion, because then it requires rework.”

Even a repetitive task needs boundaries. If an agent is working blind, velocity becomes a loop instead of a shortcut.

When Delegation Works Beautifully: The ArgoCD Cluster Project

Then came a different experiment, and a different outcome entirely.

For the ArgoCD cluster automation project, instead of rushing ahead, the team built context first. They documented how the cluster worked, how deployments flowed, what decisions had been previously made, and what the agent should (and shouldn’t) touch.

And the results were great.

The agent wasn’t guessing; it was navigating. Once the first application was deployed with this structure in place, the rest followed fast. Not because the model was smarter, but because the environment was clearer.

Leo describes why it worked:

“We documented everything before starting: how the cluster worked, how ArgoCD was configured for us, the decisions that had been made before. The agent wasn’t just coding blindly; it understood the environment. Once we created the first application and documented it well, every other application came out fast.”

This was the turning point: clear context transforms an agent from a generator of code into a collaborator that understands the system.

So, When Should You Delegate?

From these experiences, a rule emerged: delegation isn’t universal, it thrives under the right conditions.

Agents work best when the work is repetitive or mechanical (migrations, boilerplate generation, bulk code updates, internal dashboards) especially when documentation is strong and the cost of iteration is low. When the agent has a map, it can move confidently without needing constant correction. In low-risk environments, this creates acceleration instead of chaos, opening space for rapid learning and experimentation.

Delegation isn’t about offloading everything, it’s about offloading the right things. The parts where clarity is high, stakes are manageable, and iteration is cheap.

When Delegation Doesn’t Make Sense

The inverse is just as important. If the task involves architectural decisions, security-sensitive logic, or anything where failure carries real impact, handing control to an agent is more risk than reward. When the team doesn’t fully understand the problem yet, the agent can’t compensate — it amplifies uncertainty.

Automation doesn’t replace thinking. It accelerates what has already been understood. Agents assist best when humans lead: design, direction and clarity first. The moment responsibility becomes critical, delegation becomes oversight, not automation.

The Real Skill: Knowing What Can Run on Autopilot

Agentic development isn’t about using AI everywhere: it’s about using it intelligently. Teams gain the most when they draw a clear line between what can be automated and what should remain deliberately human-driven. Leo summarized it during a workshop in a way the whole room remembered:

“The goal isn’t to stop programming. It’s to understand that there’s another way of getting to the result; one where our contribution is more valuable in architecture, in planning, in how we prepare the system.  Agents can help us, but only if we guide them with the right structure.”

That’s the core of agentic work.

AI accelerates, but it does not decide. It executes, but it does not imagine. It shines, but only when we give it the room and the direction to do so.

Nybble has learned this not in theory, but in practice. Delegation is powerful when chosen carefully. And the companies that master that judgment will be the ones who build faster, smarter, and with more confidence in the results.

Because in the end, the real advantage is not using agents everywhere, but using them with intention. When teams choose wisely, when context is clear, and when autonomy is defined with purpose, the results stop feeling mechanical and start becoming truly remarkable.

That is the way we approach agentic development at Nybble: thoughtful, practical, and always focused on building solutions that matter.