Agentic AI: What Exactly Is It?
“Agentic AI” might sound like another big tech buzzword.
In reality, it’s simple - and it’s already here.
At its core, Agentic AI means you describe an outcome, and the AI executes the steps to make it happen.
Traditional AI assistants take your prompt and give you an answer. They help you think or plan, but they stop there. Agentic AI acts on your behalf. It interprets your goal, executes a sequence of steps to achieve it, and keeps going until it’s done - or until you step in to guide it.
If you’ve used GitHub Copilot, ChatGPT, or any code assistant, you already know the basics.
This is simply the next step - from autocomplete to auto-execute, from assistant to collaborator.
There are already several excellent articles out there, that go deeper into explaining and comparing agentic systems in the broader sense.12
They outline the growing number of tools, models, and products exploring this new category.
My own recent experience comes mainly from using GitHub Copilot and its agentic features, experimenting with different underlying models and observing how they perform differently in practice. I also experimented with OpenAI Codex in VScode.
This post focuses on what it feels like to integrate agentic AI into my daily work, and why it matters.
Agentic AI for Developers - It Can Do the Work (When You Guide It)
For developers, AI agents offer a new way to interact with daily work.
Instead of typing code line by line (even with autocomplete), you can now describe outcomes.
For example, when extending one of my SaaS web apps, I can simply say:
“Implement the items listed for version 1.1 in TODO.md, follow all style and quality conventions from the project context, and ask me to review once you’re finished.”
An agentic AI can take it from there: generate the required code, write tests, fix failing tests, create ADRs, update documentation and TODOs, and hand back a ready-to-review implementation.
It is similar to pair programming - just that you stay in the navigator seat full-time.
You give direction, supervise, and review. The AI handles the busywork, context switches, and repetitive commands.
To me, this workflow inversion was striking.
A short, clear paragraph of instruction produced dozens of coherent changes and artifacts - work that would normally take hours or days. While it still needed review and correction, the agent planned, built, tested, and refined continuously.
You can guide, interrupt, or let it run - it adapts, and that adaptability feels genuinely new.
Agents, like humans, need context.
There are different approaches to sharing conventions and expectations - for example, using files like .agent-config or .prompt-context that describe coding standards, naming conventions, and quality rules.
These become shared references the agent reads and follows during execution.
Yes, Agents Make Mistakes
Skepticism is healthy here. Agentic AI isn’t perfect.
Agents still make plenty of mistakes - broken builds, misread requirements, even security oversights - and they always need supervision.
They can get stuck in retry loops or wander off solving the wrong problem.
They sometimes introduce subtle bugs or inefficient solutions.
They can also be expensive if you run complex tasks frequently.
You cannot just walk away and expect production-ready code.
But here’s the thing: I make those same mistakes too.
When an agent produces broken code, I catch it in review - the same way I’d catch a mistake in my own work or a teammate’s PR.
The difference is that the agent has already handled the boilerplate, the CLI commands, the documentation-diving, and the tedious setup. I’m reviewing the logic and architecture, not fighting syntax or shell quirks.
Small aside:
Using different models inside GitHub Copilot, the gap in reliability becomes clear.
Some models maintain context and reasoning across longer execution chains, while others lose direction after a few steps.
That difference turns out to be the line between “nice idea” and “actually useful.”
(And to be clear, this isn’t a sponsorship or product review - just where I happened to experience the biggest shift.)
Programming Became Overwhelming
Modern software development has grown into a complex ecosystem that few people fully grasp anymore.
A typical backend developer today uses multiple languages and frameworks at once - often a mix of Python, Java, Go, Ruby, plus SQL for databases, YAML and JSON for configuration, Bash for scripting, and maybe TypeScript for tooling or front-end integration.
On top of that comes a growing stack of infrastructure and automation systems: Docker, Kubernetes, CI/CD pipelines, Terraform or Ansible for provisioning, and an endless list of supporting CLIs.
Even when IDEs abstract parts of this, the daily workflow still means juggling dozens of tools and switching constantly between them.
Write code, run tests, commit, lint, reformat, deploy, monitor, debug - each step through a different command or interface.
Research confirms this: developers now spend more time managing environments, pipelines, and dependencies than writing business logic itself.3
It’s no longer just about learning a language; it’s about carrying the mental model of an entire ecosystem.
That complexity creates enormous cognitive load.
Every task requires remembering not just what to do, but where and how to do it.
We open the terminal, run one command, fix an error, change a config, push again, check logs, start over.
Each context switch drains a bit of attention, and with enough of them, it feels less like building software and more like operating machinery.
The actual act of creating - of thinking clearly about a problem - gets buried under all the operational overhead.
Tool Frustration
To me, this is not only exhausting; it’s frustrating.
Because much of that mental effort goes into friction, not creation.
The tools themselves often fight back.
Remembering a curl flag sequence just to make a simple request.
Running find or sed and discovering they behave differently on macOS and Linux.
Dealing with git’s inconsistent naming, or docker’s unpredictable rebuilds and version quirks.
These are small annoyances on their own, but together they form the background noise of a developer’s day.
Surveys like Warp’s State of the CLI 2023 show that about 70% of developers struggle to remember command syntax and flags4, and DX research from 2024 estimates that time lost to tool troubleshooting equals nearly twenty working days per year5.
It’s a quiet tax on our attention. A steady erosion of focus and flow.
How Agents Can Help
Agentic AI can change this dynamic.
It can handle the repetitive command chains, retry on failure, interpret errors, and glue these tools together reliably.
It doesn’t make the ecosystem simpler - but it can carry more of its weight.
And when that happens, the experience changes too: less stress, fewer interruptions, and a small piece of clarity returning to the craft.
Agents Need Guidance - Like Any Good Teammate
Even the most capable agents need direction and context - just like we do.
They can do a lot, but they still rely on us for intent, structure, and clarity.
The best results come when you set expectations clearly.
Create an agent-config or context file describing your project’s architecture, naming conventions, dependencies, and coding standards.
Then lead with something like:
“Implement feature X according to the agent-config file and follow all established rules.”
That’s your shared playbook. It helps the agent make consistent decisions - and keeps your project coherent.
When the agent gets stuck (and it will, occasionally), treat it the same way you’d treat any teammate - or even yourself - when debugging: pause, step back, simplify.
Sometimes the common advice still applies:
Clear expectations, context, and instructions turn agentic AI from a simple bot into a dependable teammate.
This partnership works because it is built on collaboration.
I provide context, goals, and guidance while the agent handles scale, detail, and execution speed.
Together, we build better software - and I can actually enjoy doing it.
Why This Matters
What agentic systems change isn’t just efficiency - it’s how we relate to the work itself.
For years, development has quietly drifted toward a state of exhaustion.
We built faster pipelines and smarter editors, yet the work itself kept feeling heavier and more fragmented.
Agentic AI flips that relationship.
It gives us a chance to work at a more natural level of abstraction - to focus on intent, not orchestration.
It doesn’t make us better programmers in the academic sense; it just removes the layers of friction that have been dulling our focus.
In a world where almost every developer spends part of their day fighting the toolchain, that’s not a small change.
For me, that shift has made programming enjoyable again.
Not easier, not perfect — just closer to what I always liked about software development:
Building things, exploring ideas, seeing progress without wading through endless glue code and command-line trivia.
That’s why this moment feels relevant.
Because the discussion about AI in software shouldn’t only be about capability or risk.
It should also be about experience - what it’s like to build again with clarity and calm.
Whatever the broader change becomes, one thing already seems clear: AI agents will change how we work and how the work feels.
They offer a rare opportunity to reduce frustration and friction, and to make our daily work a bit more humane. That alone is something worth embracing.