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If you start mapping and designing your own agentic workflows today, you'll own the systems that will run your team tomorrow.
Jyoti Jaswani
Published on 25 February 2026
The subtle signal behind OpenAI's bold hire
For the past few years, using AI has felt like a conversation. You open a tab, type a prompt, and wait for a response: mails drafted, reports summarised, and content generated. It's useful and impressive, but fundamentally reactive. That phase is ending.
OpenAI's decision to hire Peter Steinberger, the creator of the OpenClaw agentic layer, marks more than a simple talent acquisition. By bringing in the architect of a system that allows AI to act autonomously, OpenAI is making a clear declaration of intent. The company is pivoting from conversational AI to operational AI, moving from systems that answer to systems that act.
From intelligence to execution
Most AI dialogue has focused on intelligence benchmarks: measuring how well models reason, handle context, or minimise hallucinations. But intelligence is becoming a baseline commodity. Now, the real value lies at the interface between thought and execution, not just in the model's internal logic.
While regulatory concerns centred on safety protocols and model weights, Steinberger was building tools that allowed Claude (Anthropic's model for clarity) and, now, GPT to access the file system. "Write me a follow-up email" has evolved into a delegated workflow: "When a new lead arrives, research their company, draft a tailored message, log the activity in the CRM, and notify me only if they engage."
Hiring an agentic engineer who captures the hopes of those desperate to realise long-promised agentic ambitions suggests that OpenAI understands the next competitive layer is about reliable orchestration, and not smarter LLMs. Intelligence is expected, whereas execution is the advantage.
Owning the orchestration layer
Chatbots live where users ask questions. Agents live where systems take action. It's a shift that fundamentally reorders the dynamics of software power.
Historically, tools dictated your workflow. You adapted to their dashboards, dropdowns, and rigid fields. In this agentic wave, workflows are responding to you. Natural language has become a programming interface, and whoever owns the orchestration layer sits between intent and execution.
Imagine a system that understands your specific operational nuances, integrates with your stack, and becomes the default conductor for your daily tasks. Since natural language is the interface, your core logic remains portable: if you can describe a process, any agent can enact it. Yet, the real lock-in lies in the integration. Replacing your 'conductor' isn't just about switching models; it would mean rewiring the underlying connections to your tech stack and losing the proprietary 'memory' of years of accumulated procedural context. By bringing Steinberger on board, OpenAI is moving beyond the role of a standalone tool; they are building the infrastructure to become the underlying operating system for business operations.
The strategic misstep
There's a subtext to this hire. The current identity of Steinberger's OpenClaw project was the result of a friction point with Anthropic over branding and naming rights. While Anthropic was finalising the branding strategy, OpenAI stepped in and appointed the person who was making Anthropic's own models more useful.
It is a signal shift from protecting the brand to winning the territory. OpenAI is buying the community's momentum rather than just a tool. They are signalling that the true frontier isn't just massive processing power any more. It's the messy, local, multi-agent reality where AI interacts with your hard drive and your proprietary data.
Skills become leverage
Building in technology has traditionally meant writing syntax. If you weren't technical, you were a spectator. Agents blur that boundary, but they don't erase it. They move the goalposts.
Building is now as much about spotting friction as it is about the code. Teachers see where students struggle, recruiters notice patterns in hiring, operators know where workflows break. If you can clearly describe what keeps breaking, what takes too long, and what the ideal outcome looks like, you can design an agent around it. The bottleneck is shifting from technical feasibility to problem clarity. It’s this contextual awareness that is the quiet revolution and your primary source of leverage.
Ecosystem gravity
There's something important that product leaders and founders shouldn't ignore. When OpenAI invests in agent builders and helps creators, it's also strengthening its own ecosystem. The more builders rely on OpenAI's tools, the more central OpenAI becomes. And it's not just OpenAI. Anthropic will build its own core agent systems. Microsoft will bake agents directly into enterprise software. Google will integrate them into Workspace, and Meta will experiment across its apps and devices.
So if the biggest platforms are making AI agents part of their infrastructure, your decision about adopting them is half the battle; the other half is where you sit relative to them. Will you build on top of these ecosystems? Alongside them? Or fully dependent inside them?
While owning orchestration provides leverage, operating downstream means your success is tied to another's roadmap.
The enduring signal
Operational AI is not a technical upgrade but a governance shift. When a system moves from suggesting an answer to executing an action, security is no longer an IT checkbox but a boardroom priority. The flashiest demo loses to the clearest boundary, and success will be defined by knowing exactly where the machine ends and human accountability begins.
Prompting is already a surface-level skill with a rapidly approaching shelf life. The enduring capability is orchestration literacy: systems thinking applied to autonomy, the ability to design workflows you own rather than inheriting a process designed by someone else.
Steinberger's hire is a signal that the phase of the consultant AI is ending. We have entered the era of the executor. Your role has evolved from manual tool use to the architecture of outcomes. In this new movement, you either design the workflow or you become a line item in someone else's workflow.