After nearly 10 years in classical machine learning — I
now find myself working on agentic AI systems.
It’s an exciting space, no doubt.
And yet, I feel… bored at times.
This isn’t something many people openly admit,
especially when working with “cutting-edge” technologies. But if you’ve made a
similar transition, you might recognise this feeling too. This isn’t about the
technology being less powerful. It’s about how the nature of the work has
changed.
From Craft to Assembly
Classical machine learning felt like a craft.
You worked closely with data — engineering features,
tuning models, and understanding behaviour through clear cause and effect. The
satisfaction came from building something that you could reason about deeply.
With agentic AI, the work has shifted.
Instead of building intelligence, we often orchestrate
it:
· Designing
prompts
· Integrating
APIs
· Managing
workflows across tools and agents
It can feel less like engineering from first principles,
and more like assembling systems from pre-built components. The intellectual
depth hasn’t disappeared — but it has shifted away from where many of us
derived satisfaction.
Abstraction and Loss of Control
Modern AI systems are extremely powerful — but
also highly abstracted.
In classical ML, when something failed, you could trace
it back — data leakage, model choice, tuning gaps, bias/variance
trade-offs.
In agentic systems, failures are often opaque:
· Is
it the prompt?
· Context
ambiguity?
· The
model’s internal reasoning?
You lose the tight feedback loop between hypothesis and outcome, and that can make the work feel less intellectually satisfying.
The Rise of “Glue Work”
A significant part of agentic AI today involves:
· Prompt
iteration
· Output
formatting
· Guardrails
and validation
· Tool
integration
This “glue engineering” is necessary, but often
repetitive. The challenge shifts from solving domain problems to making systems
behave reliably.
Determinism vs. Probabilistic Behaviour
Another shift is from relative determinism to
probabilistic behaviour.
Traditional ML systems were not perfectly predictable,
but they were understandable within known bounds.
Agentic systems are more variable:
· The
same input may produce different outputs at invocations
· Small
prompt changes can have large effects
Instead of debugging logic, you spend time managing
uncertainty. And that can be frustrating.
Expectation vs. Reality
Agentic AI is often described as autonomous and
self-directed.
In practice, much of the work involves:
· Adding
constraints
· Designing
safeguards
· Keeping
humans in the loop
Much of the work becomes about limiting the system
rather than unleashing it — which can feel like a step back from the original
promise.
So Why Does It Feel Less Engaging?
Putting it all together, it isn’t about the technology
being trivial — it’s about a mismatch in where the intellectual challenges lie.
In classical ML, the focus was on: Understanding
and modelling the problem.
In agentic AI, the challenge is increasingly: Managing
reliability, orchestration, and behaviour of black-box intelligence.
If you were energised by depth, optimisation, and
mathematical clarity, the current landscape can feel like it lacks those same
hooks.
Where the Real Challenge Is Moving
The depth hasn’t disappeared — it has relocated.
Areas that feel genuinely challenging include:
· Evaluation
of LLM and agent outputs
· Building
reliable and observable systems
· Designing
hybrid architectures combining ML and agents
This is where prior experience in classical ML becomes
valuable again.
A Personal Reflection
Maybe
this isn’t boredom — maybe it’s transition.
I’ve moved from being a builder of models to
a designer of intelligent systems. And somewhere along the way,
I’ve lost that familiar sense of control and mastery I built over years in
classical machine learning — that shift feels noticeable, even though it isn’t
about Agentic AI being less interesting.
If you come from a world of deep modelling and
mathematical clarity, it’s natural to feel like something is missing at first.
Perhaps the real shift is this: to deliberately seek out
the new depth in this space — rather than expecting it to appear where it once
did.
