In my last post, I talked about the potential risks AI could bring to humans in the not-too-distant future.
Today, I want to look at another side of AI: what happens when we give AI more freedom to actually do things?
Grok Bot is an interesting example.
Starting tomorrow, the Grok Bot Galaxy event will run a 72-hour demo, showing end-to-end workflows from different perspectives, including business, engineering, and marketing.
I'm particularly interested in watching how an AI agent handles these workflows from beginning to end.
From Chatbots to Agents
Everyone is familiar with the typical AI workflow:
We give AI a prompt → AI gives us a response → We do the rest.
For example, we ask AI to:
- Explain an unfamiliar API
- Debug an error
- Write a test
- Brainstorm an architecture
- Summarize some documentation
Then we take the response and continue working.
It's useful, but it's still quite passive and step-by-step.
So what happens when we change the workflow?
With an AI agent like Grok Bot, the idea becomes more like:
We give AI a goal → AI plans → AI uses tools → AI keeps working → We review and approve.
The AI isn't just answering us anymore.
It's taking action.
And that's a pretty significant change.
The Model Is Only Part of the Story
Think back to late 2023.
AI was already impressive, but the way we interacted with it was still relatively simple: type something, get a response, and continue from there.
Fast forward to today, and we're increasingly talking about AI agents, autonomous workflows, and eventually AGI.
But does this progress come only from making the underlying AI model smarter?
I don't think so.
The model is obviously important. Better reasoning gives AI the ability to understand more complicated problems.
But an AI system also becomes much more capable when we give it the ability to act on what it thinks.
- Give it web search.
- Give it a terminal.
- Give it the ability to run commands or call APIs.
- Give it external tools.
- Give it memory.
- Give it persistent state.
Now the system isn't simply generating a response.
It can observe something, make a decision, take an action, observe the result, and continue.
That's where AI agent architecture becomes really interesting.
The model provides the intelligence. The agent architecture gives that intelligence a way to act.
Imagine Grok Bot as a New Teammate
Now let's make this practical.
Imagine Grok Bot joining our SaaS engineering team as a new teammate.
What should we actually let it do?
For bugs, for example, sometimes we need a reliable reproduction before another engineer can verify a fix.
Instead of asking an AI:
"How can I reproduce this bug?"
We could give it a goal:
"Reproduce this bug, collect the relevant logs, document the steps, and create a Jira ticket."
The agent could potentially:
Open the application
Follow the required workflow
Reproduce the issue
Collect logs and relevant information
Document the reproduction steps
Create a Jira ticket
Wait for us to review it
That's very different from asking ChatGPT to simply tell us how to reproduce the bug.
The AI isn't replacing the engineer.
It's taking over a workflow that previously required an engineer to manually move between different tools.
And It Doesn't Stop at Engineering
This is where the Grok Bot Galaxy demo becomes interesting to me.
If an agent can operate across different tools and workflows, why stop at engineering?
Imagine giving it a business goal:
"Find out why our trial-to-paid conversion dropped this month."
Instead of only asking for an analysis, the agent could potentially gather data, inspect dashboards, compare historical results, identify possible causes, and prepare a report.
Or for marketing:"Prepare a campaign for our new feature."
The agent could research competitors, draft content, prepare different variations, and organize the campaign workflow for human review.
The important change isn't that AI can write these things.
AI has been able to write for a while.
The interesting part is that the AI can potentially connect the individual steps together.
But How Much Freedom Should We Give It?
This is also where my previous post about AI risks connects to today's topic.
The more capable an AI agent becomes, the more important permissions and boundaries become.
If we give an AI access to:
- Our codebase
- Production systems
- Customer data
- Company accounts
- Internal documents
- Payment systems
then "make the AI more autonomous" isn't automatically a good thing.
We need to ask:
What should it be allowed to do?
What should require human approval?
How do we know what it actually did?
What happens when it misunderstands the goal?
An AI teammate with too little access might not be very useful.
An AI teammate with too much access might become a serious security and operational risk.
The interesting engineering problem may therefore become not just building smarter agents, but designing the right boundaries around them.
From AI Assistant to AI Teammate
This is what I find most interesting about Grok Bot.
The biggest change may not be another improvement in how well AI answers questions.
It may be the transition from:
"Ask AI something."
to:
"Give AI something to accomplish."
That's a different mental model.
We're moving from AI as a tool that waits for instructions toward AI as a system that can take initiative within a defined scope.
I don't think this means developers suddenly become unnecessary.
If anything, the role of the developer may become more interesting.
We may spend less time manually executing every step and more time:
- Defining goals
- Designing workflows
- Choosing the right tools
- Setting permissions
- Reviewing AI decisions
- Handling edge cases
- Taking responsibility for the final result
The question may no longer be:
"Can AI do this task?"
but:
"How much of this workflow should we let AI do?"
That's what I want to explore while watching Grok Bot Galaxy.
We're still early.
But the direction is fascinating.
And perhaps the next generation of software won't just be software that we use.
It will be software that works alongside us.
I write about AI, software engineering, technology, and the things I'm learning along the way.
If you found this perspective interesting, you can read more of my work at:
👉 javaskr.com
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