“Agents are not a feature. They are a product category.”
This issue of Redeployed is brought to you by Tecla: As OpenAI doubles down on AI agents and autonomous systems, more companies are looking for engineers who already know how to build with the OpenAI stack. Tecla helps U.S. tech teams hire experienced OpenAI developers from Latin America who can plug into teams quickly and ship production-ready AI features.
The move signals something important.
The race to build AI agents is no longer experimental. It is strategic.
For the past two years, most companies focused on models. Bigger models. Faster models. Better benchmarks.
Now the focus is shifting to what those models actually do.
From Models to Systems
AI agents represent the next layer in the stack. Instead of responding to prompts, agents can plan, execute tasks, and interact with other systems. In practice, that means coordinating actions across tools, APIs, and workflows without constant human prompting.
One agent retrieves information. Another analyzes it. A third writes the output or triggers an action.
This is what people mean when they talk about multi agent systems. Not one model answering questions, but a network of AI processes collaborating toward a goal.
And the companies that build these systems first will likely shape the next generation of software platforms.
Why This Matters for Product Teams
Agents change how software gets built and how it behaves once deployed.
Instead of static features, products begin to look like dynamic systems. A support tool becomes an autonomous troubleshooting agent. A research assistant becomes a system that gathers, validates, and summarizes information across sources.
The interface shifts from commands to outcomes.
You do not ask the software for one answer. You ask it to solve a problem.
That requires different engineering thinking. Systems orchestration. Reliability layers. Guardrails around autonomy. Observability into decisions made by machines.
In other words, the hard part is not the model. It is the architecture around it.
What this means for teams
Building agent systems is not just about experimenting with new models. It requires engineers who understand orchestration, APIs, distributed workflows, and how AI behaves in production.
That is where many teams are feeling the pressure. Companies are no longer just looking for developers who can integrate an API. They need builders who can design systems around AI.
More U.S. tech teams are responding by expanding their engineering capacity with senior developers who already work this way. Engineers who are comfortable working alongside AI tools, coordinating complex systems, and shipping fast. Many of those teams are turning to nearshore talent in Latin America, where experienced engineers can collaborate in real time and scale product development without slowing momentum.
Because in the agent era, speed and system thinking matter just as much as the models themselves.
The Competitive Layer
This is why OpenAI is investing in leadership around agents now. Google, Anthropic, and several infrastructure startups are pushing similar ideas.
Agents are becoming the battleground where models turn into products.
The companies that win this layer will control how software actually gets work done.
And if the hiring signals are right, the agent era is arriving faster than many teams expected.
More to come…
– Gino Ferrand, Founder @ Tecla