Ever notice how AI agents can be eerily similar to humans when it comes to delegation? It’s almost like they’ve picked up workplace habits from the best (and worst) of us!
Picture this: Your boss gives you a task. You think, I got this! and dive right in. But as you get deeper, reality kicks in—it’s harder than expected. Now you’re stuck, too far in to quit, but unsure how to cleanly hand off the remaining work. Sound familiar?
I’ve seen plenty of highly technical managers fall into this trap. Instead of delegating efficiently, they take on tasks themselves, only to realize too late that they should have enlisted help from the start. Breaking a task into sub-tasks and effectively distributing them is an art—a skill that some humans (and AI agents) struggle to master.
Now, let’s look at how AI agents handle delegation. Suppose I give my automation platform the following task:
"Get my EKS clusters, find the EC2 instance IDs for each cluster node, determine their average utilization, and plot it. If utilization is below 10%, create a Jira task assigning it to XYZ, asking why these nodes are underutilized."
Sounds simple enough, right? Well, here’s how it actually unfolds:
So why did the AWS agent even try to do everything itself in the first place?
We made a design choice: attempt to solve problems earlier in the hierarchy to minimize excessive LLM calls. If we split tasks too aggressively, each subtask would trigger an additional call, slowing everything down.
But this approach also has its downsides. Just like an overconfident engineer who refuses to delegate, our AWS agent wastes time and resources before realizing it’s in over its head.
If this were a human, they’d (hopefully) learn from their mistakes. Over time, they’d recognize which tasks are beyond their capabilities and delegate them upfront instead of struggling first.
And that’s exactly how we’re improving our AI agents. By introducing memory, each agent remembers past successes and failures. Now, instead of blindly trying to handle every task, it makes a smarter decision:
Rather than brute-forcing its way to expertise, our AI agent is learning something more valuable: when to take action and when to call for backup.
As our AI agents evolve, they’re becoming more like specialists in a well-oiled machine, cooperating instead of competing. No single agent needs to be the lone wolf, tackling everything alone. Instead, they form a network of expertise, efficiently collaborating to get the job done.
Now, let’s just hope they don’t start getting jealous of each other! 😆
