“Agents are easy to demo. Hard to run at scale.”
Redeployed is a weekly newsletter that breaks down one important AI story at a time for leaders in technology. Every issue explains what the shift means for technology companies and how smart leaders can use it to get ahead.
A team builds its first AI agent to automate a workflow. It works in a demo. It handles a few tasks. It connects to one or two internal tools. For a moment, it feels like progress. Then the questions start…
Where does it run?
Who can access it?
What happens when it fails?
How do you monitor it?
How do you manage ten of them? Fifty?
That is where most teams slow down.
This week, Google made it clear where that problem is going.
From Tools to Infrastructure
At Google Cloud Next, the company introduced the Gemini Enterprise Agent Platform, a system designed not just to build agents, but to operate them. It combines model selection, agent development, orchestration, deployment, integration, and governance into one environment.
On the surface, it looks like another product launch.
In practice, it signals a shift.
Agents are no longer just features. They are becoming infrastructure.
What Actually Changed
For the past year, most teams approached agents the same way they approached early AI tools. They experimented. They built small workflows. They connected models to APIs and tested what was possible.
But those systems were fragile.
Each new agent required custom logic. Each integration had to be maintained manually. Governance was an afterthought. Scaling beyond a handful of use cases became difficult.
Google’s move is an attempt to standardize that layer.
Instead of asking how to build an agent, the question becomes how to run agents as part of a system. Where they live. How they are governed. How they interact with data and with each other.
That is a different level of problem.
The Build vs Platform Decision
This is where the real decision appears.
If agent infrastructure becomes standardized, companies have two options.
They can build their own systems. Control everything. Own the logic, the data, and the orchestration layer. That path offers flexibility, but it comes with complexity and cost.
Or they can build on top of platforms like Gemini. Move faster. Rely on managed infrastructure. Accept the constraints that come with it.
Neither option is obviously better.
But the decision matters more than most teams realize.
Because once agents are embedded into workflows, switching that infrastructure becomes difficult.
How Teams Are Starting to Respond
The teams moving fastest are not trying to solve everything at once.
They are testing agent workflows inside platforms first. Understanding where automation creates real value. Identifying which processes are stable enough to delegate to agents and which still require human oversight.
At the same time, they are starting to design products that assume agents will exist as part of the environment. Not as external tools, but as participants inside the system.
If you are building today, you are no longer deciding whether to use agents. You are deciding where they belong in your workflows and how much control you need over them.
That decision shapes your architecture early.
This issue of Redeployed is brought to you by Tecla: As agents move from experiments into infrastructure, the challenge is no longer building them. It is deciding where they run, how they are governed, and how they fit into real workflows. The teams moving fastest are not just shipping agents. They are building the systems around them, bringing in engineers who can integrate platforms, manage data flows, and operate AI in production environments. Tecla helps companies hire senior tech talent in the U.S. and nearshore who already work across these layers, so teams can scale agent-driven systems without losing visibility or control.
Where the Complexity Shows Up
Turning agents into infrastructure introduces a new kind of risk.
It becomes easier to deploy agents than to manage them.
Without clear boundaries, teams can create dozens of automated workflows that interact in unpredictable ways. Permissions become harder to track. Failures become harder to trace, especially when teams lack engineers who understand how different models behave in production and how to debug them effectively. Ownership becomes unclear.
There is also a strategic risk.
If too much of the agent layer is outsourced to a platform, companies may lose visibility into how critical decisions are made inside their systems.
Speed increases.
Control becomes more fragile.
What This Means for Teams and Hiring
This shift is already changing what teams need.
The demand is not for more people who can build simple agents. That capability is becoming widely accessible. The demand is for people who can design, operate, and govern systems where agents are part of the workflow.
That includes engineers who understand how to integrate AI into production systems, how to manage permissions and data flows, and how to monitor behavior over time.
It also includes people who can define workflows clearly enough for automation to work in the first place.
This is not just an engineering challenge. It is a systems and operations problem.
And it requires teams that can move across those layers.
What Smart Companies Will Do Next
The companies that benefit from this shift will not be the ones that deploy the most agents.
They will be the ones that decide where agents actually make sense.
They will invest in workflow design before automation. They will define ownership and governance early. And they will build systems that can evolve as the underlying platforms change.
Because once the agent layer becomes infrastructure, the advantage is no longer in having access to AI.
It is in knowing how to use it responsibly inside real systems.
And that is not a feature.
It is an operating capability.
Connect With Other Technology Leaders
If you want to connect with other technology leaders having real conversations about AI and how it is changing business, check out GILD Curated Circuit.
More to come…
Recommended Reads
✔️ Introducing Gemini Enterprise Agent Platform, powering the next wave of agents - Google Cloud Blog
✔️ Google expands Gemini Enterprise, consolidates Vertex AI services to simplify agent deployment- ITPro
– Gino Ferrand, Founder @ Tecla