Halilcan Soran

Tag: OpenAI Workspace Agents

  • OpenAI and Dell Are Bringing Codex On-Prem and That Is a Bigger Signal Than the Headline

    OpenAI and Dell Are Bringing Codex On-Prem and That Is a Bigger Signal Than the Headline

    OpenAI Dell Codex on-premise enterprise deployment diagram

    OpenAI and Dell announced a partnership to bring Codex into hybrid and on-premise enterprise environments. The headline reads like another infrastructure deal. It is not. The OpenAI Dell Codex on-premise story is an admission that the real ceiling on AI coding agents in regulated enterprises was never the model. It was data gravity and where the agent is allowed to execute.

    I have been waiting for one of the frontier labs to say this out loud.

    What it actually does

    The partnership packages Codex to run on Dell infrastructure inside an enterprise boundary. That means the coding agent can sit next to the source code repositories, the build systems, and the production data it needs to reason about, without that material crossing into a public cloud endpoint.

    Practically, you get Codex behavior with private deployment posture. The agent can read your repo, propose changes, run tests, and interact with internal systems while the model inference, the context window, and the working memory stay on infrastructure your security team already owns and audits.

    This is not a stripped-down Codex. It is the same agent capability, moved to a place where regulated industries can actually use it. Healthcare, defense, banking, public sector. The places where a senior engineer cannot paste a function into a public chat window without filing an incident report.

    Why the OpenAI Dell Codex on-premise move matters

    Every regulated enterprise I hear from has the same problem. They are not blocked on whether Claude or GPT or Gemini is the best coder this week. They are blocked on a simpler question. Can the agent see the code at all.

    If the answer is no, model quality is irrelevant. You end up with developers using public AI tools on personal laptops for snippets, manually retyping the output into the secured environment, and pretending this is a workflow. That is the actual state of AI coding adoption in a lot of large orgs right now. I am not exaggerating.

    Moving Codex on-prem changes the question from can we use this to how do we govern this. Those are different problems with different owners. The first one stalls for two years in legal review. The second one gets a project plan.

    There is a second signal here that matters more than the deal itself. OpenAI is conceding that the pure SaaS-only posture does not work for the highest-value enterprise segments. Anthropic has been hinting at the same thing with their enterprise services arm, and Microsoft has been quietly threading this through Azure for a while. The frontier labs are realizing that data gravity beats model gravity in the enterprise. The agent has to come to the data, not the other way around.

    The trade-off is real. On-prem deployments mean slower model updates, more operational burden on your platform team, and capacity you have to plan for instead of renting elastically. You give up the magic of always on latest. You get back the ability to actually use the thing.

    What I would do with it this week

    Even if you cannot deploy this tomorrow, you can get ready. The teams that move first will be the ones who did the boring preparation work before the procurement conversation started.

    Map where your code and pipeline data actually live. Not the architecture diagram version. The real version. Which repos are in GitHub Enterprise Cloud versus self-hosted. Which CI runners can reach which networks. Which production data the agent would need to read to be useful for debugging versus generation. If you cannot answer this in a meeting, the on-prem agent conversation is going to stall on day one.

    Write down the three coding tasks you would actually want an agent to do inside the secured environment. Not refactor our monolith. Specific things. Generate Power Platform solution components from a spec. Write the boring integration tests nobody wants to write. Triage failing pipelines. Pick three and put numbers on them. Hours saved, error rates, anything concrete. If you are thinking about where agents sit in a broader orchestration design, the distinction between single-agent and multi-agent architectures is worth settling before you scope the deployment.

    Talk to your security and compliance partners now, before the vendor demo. The fastest path to a stalled pilot is showing up with a tool and no answer to the data residency, audit logging, and model output review questions. I have seen this pattern repeat across orgs and the result is always the same. Six months of slideware, no deployment.

    Finally, watch what this does to the Copilot conversation. If Codex can run on-prem next to your code, the bar for what Microsoft has to offer inside the same boundary goes up. That is good for everyone building inside enterprise walls. The Anthropic acquisition of Stainless is worth watching in this context too, because the SDK layer that governs how these agents are called and integrated is quietly becoming as strategic as where the model runs.

    The era of AI coding agents being a public-cloud-only story is ending, and the orgs that prepared their data and governance posture early are going to look very smart in twelve months.

    This post was inspired by OpenAI and Dell partner to bring Codex to hybrid and on-premise enterprise environments via OpenAI.

  • Workspace Agents Are ChatGPT’s Answer to Power Automate and That Comparison Matters

    Workspace Agents Are ChatGPT’s Answer to Power Automate and That Comparison Matters

    OpenAI Workspace Agents compared to Power Automate flow diagrams

    I came across the OpenAI page on Workspace Agents and my first thought was blunt. This is Power Automate with a chat interface sitting in front of it. That is not a dig. The fact that OpenAI Workspace Agents land so close to what Microsoft has been building for years is the interesting part, because it tells you where the bar is moving for every automation builder in the enterprise.

    I have been building on Power Platform full time inside a large organisation for years. I am not worried about Workspace Agents replacing anything in my stack next week. I am thinking about what happens when the people I build for start using ChatGPT at home and walk into the office expecting the same feel.

    What OpenAI actually shipped and why it looks familiar

    Strip the marketing language and Workspace Agents are a way to let a user describe a repeatable task, connect some tools, and have the agent run it on a schedule or on demand. Triggers. Actions. Conditions. A reasoning layer that decides what to do next.

    If that sounds like a Power Automate flow with a Copilot Studio agent sitting on top, that is because functionally it overlaps a lot. The difference is not in what it does. It is in how you build it.

    Conversation-first automation versus flow-first automation

    Power Automate starts from a diagram. You pick a trigger, you add steps, you see the branches. Even when Copilot writes the flow for you, the output is still a visual graph you can inspect, test, and version.

    Workspace Agents start from a conversation. You tell the agent what you want. It figures out the steps. You refine by talking, not by dragging.

    Neither approach is better. They attract different builders and produce different kinds of automations. Flow-first builders think in terms of state, error paths, and what happens when step 4 fails. Conversation-first builders think in terms of outcomes and trust the model to fill in the middle.

    I have written before about what actually makes a workflow agentic, and the same rule applies here. If you can fully diagram the execution path before it runs, you built a flow with a chat skin. The interesting Workspace Agent use cases are the ones where the agent genuinely picks the path.

    What this means if you already run on Power Platform

    Workspace Agents are not going to displace Power Platform inside a large enterprise. Governance, DLP, environment strategy, audit, the whole compliance layer. None of that is solved by a chat interface on top of a model provider.

    But the comparison matters for two reasons.

    First, it shows what conversation-first building can feel like when it works. Power Automate with Copilot is moving in that direction, just slower and with more guard rails. If you want to understand where the platform is heading, watching how people actually use Workspace Agents is more useful than reading another Microsoft roadmap post.

    Second, it exposes the parts of Power Platform that still feel heavy. Creating a solution, picking an environment, sorting out connection references, publishing, sharing. A business user who just had a working agent in ChatGPT in four minutes is going to ask why the internal version takes four days. Part of that friction is unavoidable — as I explored in why Power Automate is still worth learning in 2026, the platform carries real enterprise weight that consumer tools simply do not have to.

    The expectation shift that is about to hit your intake queue

    This is the part people I talk to at other organisations are not ready for.

    The OpenAI Workspace Agents launch does not change what is technically possible inside your tenant. It changes what your users think should be easy. Someone who built an agent over the weekend to summarise emails and update a Google Sheet is going to file an intake ticket asking for the same thing against SharePoint and Outlook, and they will be confused when the answer is not “sure, by Friday.”

    The honest answer is that the internal version has to handle auth, permissions, data residency, retention, and the fact that the output will be read by someone who makes a decision based on it. That is not bureaucracy. That is the cost of operating in a regulated enterprise. But nobody wants to hear it when the external version just works.

    The teams that will handle this well are the ones that stop treating every request as a custom build and start shipping pre-approved agent templates with the governance already baked in. Citizen devs get conversation-first speed. The platform team keeps control of the risk surface. That is the only way the intake queue survives the next year. And it is worth remembering that who owns the decision inside these automations matters as much as how fast they run — shipping an agent template without settling that question just moves the risk downstream.

    I have opinions on how to structure that, and I will write about it soon. You can follow along on my LinkedIn if you want the next piece when it lands.

    Workspace Agents are not a threat. They are a preview of the conversation you are about to have with every business user who used ChatGPT over the weekend.

    Frequently Asked Questions

    What are OpenAI Workspace Agents?

    OpenAI Workspace Agents let users describe a repeatable task, connect tools, and have an agent run it on a schedule or on demand. They use a conversation-first approach, meaning you define what you want through chat rather than building a visual workflow diagram.

    How do OpenAI Workspace Agents compare to Power Automate?

    Both handle triggers, actions, and conditions to automate tasks, so they overlap significantly in what they can do. The key difference is how you build them: Power Automate starts from a visual flow diagram, while Workspace Agents start from a conversation with the model.

    When should I use Power Automate instead of a conversational agent?

    Power Automate is better suited when you need clear error handling, version control, and a fully inspectable execution path. Conversation-first tools like Workspace Agents work well when you want to define an outcome and let the model determine the steps.

    Why does the rise of OpenAI Workspace Agents matter for enterprise automation builders?

    As more people use conversational AI tools like ChatGPT in their personal lives, they will expect a similar experience in workplace tools. This raises the bar for how automation platforms present themselves, even if enterprise governance and compliance requirements still favour established platforms.

    This post was inspired by Workspace agents via OpenAI.