Fusion Claw Is Arriving in 26D. Here Is What We Are Doing Before "Turning it On".

Fusion Claw Is Arriving in 26D. Here Is Why We Are Not Turning It On Just Yet.

Visible is not the same as approved. The governance conversation to have before your first Claw run.

Oracle associated Fusion Claw with 26D. Customers in the early access cohort should see it first, and everyone else gets it with their cohort's release.

One practical detail: Oracle told us some of the capability did not make the initial general availability build, so an environment on 26D may still need a weekly CWB update. Oracle offered to look at our environment with us and confirm whether the required build is present. I would recommend asking for the same.

The model question

Fusion Claw requires frontier models. Oracle told us plainly that GPT-OSS, the model it hosts at a zero multiplier, is not capable enough for it. And if you use it, it will call a frontier model hosted by OpenAI.

If your organization approved Fusion AI on the basis of Oracle-hosted models, Fusion Claw is a new conversation, not a footnote to the old one.

There was a useful nuance here that I suspect many people have not considered. Even GPT-OSS runs outside your Fusion tenancy. Oracle hosts it in OCI data centers in Chicago and Frankfurt, and Fusion calls out to it. So the real question was never inside the tenancy versus outside it. It is Oracle-operated versus third-party operated.

For many organizations, there is precedent. Plenty of us already use frontier models hosted by third parties through other tools. Precedent makes the review easier. It does not make it unnecessary.

Oracle has documentation describing how data flows to LLMs and told us it has used it to help other customers, including defense and government customers, work through the same concerns. Get it into your review early.

What you can point to in that review

The good news is that Oracle has given security and architecture teams quite a lot to work with. Based on what was described to us:

·        User-token enforcement. All data access and actions use the invoking user's token and functional rights.

·        Layered roles. Users need the role for each agent and for the agentic app. App access does not grant missing agent access.

·        Capability scoping. Authors decide which agents and capabilities a Claw can use.

·        No credential custody. The Claw plans and orchestrates. Agents execute.

·        Private runtime. Generated code runs in a private container hosted by Oracle in OCI.

·        Configurable retention. Session state is kept for up to 90 days, and you can set that period.

·        Tenant-specific learning. Self-learning stays within your tenant and is tied to the specific outcome.

·        Budgets and a kill switch. Agent Studio in 26D supports budgets at environment, app, and agent level, plus a kill-all control.

What we are still asking

We sent Oracle a written list of follow-up questions. The ones I expect any review board to ask:

·        Can an administrator disable Claw outcomes at the tenant, environment, or role level, rather than relying on instruction alone?

·        How is the user's identity handled across sessions that run for hours or days, and what happens if their roles change mid-run?

·        What does the Outcome Receipt described in the announcement contain, how long is it kept, and can it be exported for audit?

·        Is code generated by the Claw logged and reviewable?

·        Where is business data retrieved into the container held, how is it encrypted, and how is it purged?

·        Which models are available, and can a specific model and version be pinned?

I will share what we learn.

Where to experiment instead

One of our architects asked whether we could get a separate Fusion demo environment for this kind of work. It has historically been difficult to get one. Oracle told us there is now a SKU for it.

That matters more than it sounds. Even an internal sandbox with elevated access is still constrained by your own data and your own approvals. For a capability like this, a greenfield environment is the right place to learn how it behaves before it ever touches yours.

The sequence we landed on

1.      Confirm with Oracle whether your environment has the required build, and treat visibility as information rather than permission.

2.      Get Oracle's LLM data-flow documentation in front of your security and architecture reviewers.

3.      Decide who is allowed to author outcomes and who is allowed to run them, and design the roles to match.

4.      Set budgets in Agent Studio before the first run.

5.      Experiment in a separate demo environment.

6.      Only then move toward a pilot with your own data.

If you are heading to Oracle AI World in Las Vegas, October 25 to 28, Oracle pointed us to the keynotes, a roadmap session from the product team, and the Fusion Claw Meet the Experts session. We will be there with a lot of questions.

Based on a working session between our team and Oracle's Fusion AI product management group in October 2026, combined with Oracle's public announcement of Fusion Claw on September 29, 2026 and Oracle's public documentation. Statements about availability, hosting, and controls reflect that conversation and are not a substitute for Oracle's published guidance. Confirm specifics against your own environment and your Oracle account team. Views are my own.

Fusion Claw and AI Units: A New Rate Card, and Your Agents Still Bill Separately

Fusion Claw and AI Units: A New Rate Card, and Your Agents Still Bill Separately

What changes in the cost model when a Claw runs, what does not, and the budget controls you can use today.

In August I wrote about making sense of Oracle AI units. The insight that cleared things up for me then was simple: models Oracle hosts carry a zero multiplier, and models Oracle has to call out to are where charging happens.

Fusion Claw adds a new wrinkle to that picture, and one of our team members asked about it during our session with Oracle's Fusion AI product management team. Here is what we learned.

Same meter, new rate

The meter does not change. An AI unit still represents the tokens processed combined with the complexity of the type of action. Today, nearly everything is a general action, which is the cheapest category.

What is changing is the set of rates. Oracle is publishing new rates for new capabilities: one for document generation, one for audio and video generation, and a new rate card it calls Frontier for Claw work.

The Frontier multiplier was not confirmed in our session, so I am not going to guess at one here. Watch for it on the rate card.

What the Frontier rate covers

For a Claw, consumption is mostly the orchestration work: the frontier model doing the planning and reasoning. It is based on the tokens processed, and it is independent of the agents.

Your agents still bill

This is the part worth getting right in a forecast.

When a Claw asks an agent to execute something, that agent is still doing its own work, using its own model, under the rate for the type of action it performs. The Claw does not absorb or replace that consumption.

So the cost of a Claw run is two things added together: the Claw's own orchestration at the Frontier rate, plus whatever each underlying agent consumes along the way.

That is consistent with what Oracle told us in August about agentic apps: same meter, more moving parts. A Claw is one more moving part, on a different rate.

Why the hosted model escape hatch does not apply

In my earlier post I suggested starting any Fusion AI cost model by working out which use cases genuinely need a premium model, and assuming the rest can run on Oracle-hosted inference at no incremental cost.

That framing still works for agents. It does not work for the Claw itself. Oracle told us that GPT-OSS, the hosted model with the zero multiplier, is not capable enough for Fusion Claw, and that frontier models are required.

Fusion Claw is inherently premium. The question shifts from which model a Claw should use to whether a given outcome justifies a Claw at all, or whether a standard agentic app interaction will do.

What you need to have

Fusion Claw is accessed through agentic apps, so you need the agentic app SKU. That SKU includes an allotment of AI unit credits, and if your usage grows beyond it, you buy additional AI units for that consumption.

As before, I am deliberately not quoting figures. Have that conversation with your account team early.

Budget controls you can use in 26D

One of our architects asked the question every platform owner is thinking: what happens when someone kicks off something ambitious and it runs for nine hours straight?

The answer was partial but useful. In Agent Studio in 26D you can set AI unit budgets at three levels:

·        An entire environment

·        An individual agentic app, which includes its Claw usage

·        An individual agent

You can also tell it what to do when a budget is reached: stop execution, or let the current work finish. And there is a kill-all button. As it was put to us, kill -9 for the Unix users.

What is not there yet:

·        User-level budgets. You can see what individual users are consuming, but you cannot set a budget per user.

·        Events or APIs for budgets. These controls live in Agent Studio today.

Oracle described work in progress on both fronts: exposing operational observability data through something like an OpenTelemetry stream so you can monitor it in your own systems, and APIs to set budgets, stop agents, and manage user access from the outside. Until those land, budget management happens in Agent Studio.

What I would do

Set budgets before the first Claw run, not after the first surprise. At minimum, put an app-level budget on every Claw-powered agentic app you enable, and an environment-level budget on anything that is not production.

And apply the same discipline I argued for in August, one level up. Before reaching for a Claw, be clear on why the outcome needs long-running, frontier-model reasoning rather than a point-in-time agentic app interaction.

What I am still watching

·        The published Frontier multiplier.

·        Whether a single Claw run can be broken down into its Frontier consumption and its underlying agent consumption, which is what a finance conversation will ask for.

·        Whether the ROI dashboard in Agent Studio reports on Claw outcomes the way it does for agents.

Based on a working session between our team and Oracle's Fusion AI product management group in October 2026, combined with Oracle's public announcement of Fusion Claw on September 29, 2026 and Oracle's public documentation. I am reporting a conversation, not a contract. Product details and pricing mechanics change between releases. Confirm specifics against your own environment, rate card, and Oracle account team. Views are my own.

What Actually Happens When a Fusion Claw Runs

What Actually Happens When a Fusion Claw Runs

The harness, the models, the private container, the memory, and the boundaries that hold it all in.

The word Claw comes with baggage. The open agent harnesses that inspired the name earned a reputation for broad reach: a shell, a file system, a repository, and whatever else you hand them.

So the first thing I wanted to understand about Fusion Claw was not what it can do. It was what it cannot do, and what is actually running underneath it.

Oracle's Fusion AI product management team walked us through it in some detail. Here is the picture as it was described to us.

The runtime

When a Claw runs, Oracle sets up a virtual command line environment and gives the agent harness a controlled set of tools inside it.

·        The primary harness is based on the open source Codex harness.

·        Frontier models from OpenAI provide the planning, analysis, and deep research capability.

·        Skills live inside the virtual file system. Oracle provides some, and authors can add their own to describe how a particular outcome should operate.

Oracle's announcement also names Gemini alongside OpenAI, with support for additional frontier models planned over time. In our session, the discussion centered on OpenAI.

The private container

Each Claw runs in a private virtual container that Oracle hosts in OCI, specifically within its Fusion Spectra service, and that only your company and your users can access.

The lifecycle is sensible:

·        The container is live while the work is running.

·        When the session pauses or completes, Oracle stores the session state and shuts the container down.

·        If you resume, the container is rehydrated from that stored state.

Session information is retained for up to 90 days, and that retention period is something you can set yourself. Keeping it around is what lets you restart a session, continue it after the Claw has finished, or take it down a different path with the same context.

This is also where generated code runs. When a Claw writes code to analyze a large data set, that code executes inside the container, not directly in Fusion.

What it can reach

The Claw's scope is set by the agentic app it belongs to. Authors decide which parts of the app are exposed to the Claw through a capability layer, effectively a subset of the app, along with the skills and guardrails that govern how it pursues the outcome.

Oracle gave two concrete examples of what that means in practice. A Claw will not go set up a REST call outside the domain of its agentic app. And unlike general coding assistants, where you might give the agent access to a Git repository, Fusion Claw does not get that kind of open-ended access. It is limited to the agents and capabilities you make available to that particular outcome.

Whose identity it uses

All data access and all actions happen with the invoking user's token. The Claw only fetches data that user can see, and only takes actions that user has the functional rights to perform.

Combined with the point I made in my previous post, that the Claw plans and orchestrates while the agents execute, this means the Claw itself never holds credentials.

Roles, in layers

One of our architects asked the question every security team will ask: if I can use a Claw, do I inherit access to every agent it can call?

No. Access is layered:

·        A user needs the role for an individual workflow agent to invoke it, or for it to be invoked on their behalf.

·        The agentic app has its own role access, and that is where access to the Claw is set, because the outcome is tied to the app.

·        If a user has access to the app but not to one of its agents, that agent simply does not participate on that user's behalf.

App access does not paper over missing agent access. That is the answer I was hoping for.

Memory and learning

As a Claw works through a long-running session, it keeps notes to manage memory and context. Across runs, it applies what Oracle described as procedural or functional learning, so later runs of the same outcome can make fewer LLM calls, use fewer tokens, and produce better results.

Another of our architects asked where that learning lives. The answer was direct: it is tenant-centric and specific to the Claw outcome. It is not a shared, Oracle-wide learning layer.

Why it took this long

I said on the call that the open Claw concept has been around for a while, and that the real challenge was never the concept. It was constraining it.

Oracle agreed. Their point was that no customer wants a Claw going wild in their environment and picking up permissions it is not supposed to have, and that this is a deliberately constrained implementation built to work within Fusion security. Having heard the details, I believe them.

What we did not cover

A few things I still want to understand, and which are now with Oracle as written follow-up questions:

·        What the Outcome Receipt described in the announcement contains in practice, where it is stored, and whether it can be exported to the systems we already use for audit.

·        Whether code generated by the Claw is logged and reviewable after the fact.

·        How the user's identity is handled across sessions that run for hours or days, including what happens if that user's roles change mid-run.

·        Where business data retrieved into the container is held during the retention period, and how it is purged.

None of those are criticisms. They are the questions an audit or architecture review board will ask, and I would rather have them answered before anyone needs them and I'll report back with what we find!

Based on a working session between our team and Oracle's Fusion AI product management group in October 2026, combined with Oracle's public announcement of Fusion Claw on September 29, 2026 and Oracle's public documentation. Product details and pricing mechanics change between releases. Confirm specifics against your own environment, rate card, and Oracle account team. Views are my own.

Fusion Claw Does Not Replace Agentic Apps. It Gives Them a Longer Reach.

Fusion Claw Does Not Replace Agentic Apps. It Gives Them a Longer Reach.

Where Oracle's newest Fusion AI capability actually sits relative to Agent Studio, workflow agents, and agentic apps.

In August I wrote about sorting out where AI Agent Studio ends and agentic apps begin. A few weeks later, on September 29, Oracle announced Fusion Claw, and my feed has had something new about it nearly every day since.

The reaction on my team was the same one we had last time: what is this, and what does it do to everything we just learned?

So we went back to Oracle's Fusion AI product management team and asked exactly that.

The short answer

Fusion Claw is not a new place to build agents, and it is not a replacement for agentic apps. It is a governed runtime that an agentic app can use for a specific class of work: long-running, outcome-driven, and often data-heavy.

When I summarized it back on the call as a governed agentic execution runtime that agentic apps can leverage, rather than something doing work on its own, Oracle agreed with the framing. They added one refinement that turns out to matter a great deal, and I will get to it in a moment.

Oracle also confirmed what the name suggests. The architecture and concept are inspired by OpenClaw, NemoClaw, and similar approaches. What they built around that concept is considerably more governed, which I will cover in a separate post.

The hierarchy, restated

The cleanest way to understand Fusion Claw is as a third layer on top of the two we already had.

·        Workflow agents perform a specific, well-defined task. The example used in our session was taking a purchase order from an email and turning it into an invoice.

·        Agentic apps combine those agents into a team with shared context and memory, coordinated by an orchestrator, working toward a broader business outcome in near real time.

·        Claw outcomes are presented inside an agentic app as an explicitly defined business goal, described in natural language and powered by a skill that tells the Claw how it may use the agents and capabilities the app exposes.

One of our architects pushed back during the call and asked whether we had the direction backwards. The clarification was helpful. Agents are designed to work independently. The agentic app combines them into a team. The Claw is then presented through that app as an outcome. It can orchestrate against the team, but its scope is the app.

The refinement: the Claw plans, the agents execute

This is the part I would underline.

The Claw does the planning and orchestration. When it is time to actually do something in Fusion, it calls back through the agentic app to the agents to perform the execution. The Claw itself does not hold authentication credentials and does not directly perform authenticated business transactions.

That separation shapes how you should think about governance, and it is why I keep describing this as a runtime rather than a new kind of agent.

What it adds on top of an agentic app

One of our architects asked the obvious question: agentic apps already orchestrate agents, so what does a Claw add? Oracle's answer came down to four things.

1.      An explicit business goal. In an agentic app, the capability is implicit in the agents you selected and the tasks users ask of them. With a Claw, the author or user states a specific outcome, and progress is tracked toward completing it.

2.      Long-running sessions. A Claw session has a heartbeat. It can wake up, continue working, and make progress toward the outcome over minutes, hours, or days, incorporating feedback from the user where needed. Oracle compared it to deep research tools where you hand over a large ask and it works through a plan.

3.      Large data and generated code. The Claw can retrieve authorized business object data, potentially millions of records, and write code behind the scenes to analyze it. That code runs in a private container rather than directly in Fusion. The example offered was evaluating supply chain routes ahead of an upcoming event or a supplier shortage. Oracle was clear that this is not something an agentic app does out of the box.

4.      Self-learning. The runtime keeps notes and memory as it works, so subsequent runs of the same outcome can use fewer LLM calls and fewer tokens while improving quality. That learning is tenant-specific and tied to the particular outcome.

This connects directly to something I flagged in August. I wrote then that agentic apps enforce a 60 second response limit and that it is a real design constraint. Oracle described the agentic app interaction as point in time: to see updated state, you invoke the app, and then you tell it what to do next. When I asked whether long-running reasoning beyond that point-in-time interaction was the real value of the Claw, the answer was yes. Fusion Claw is what Oracle built for work that does not fit that shape.

Where you will actually see it

There is no separate Claw interface. Inside an agentic app there is an outcomes section, and available Claws are presented there as business goals. An authorized user picks one and starts it.

Behind that, the author defines the outcome: the skills, the capabilities it can use, the security constraints, and the guardrails. That package is what gets instantiated into a container when an authorized end user kicks it off. Authors decide what the Claw can do. Users with the right roles get to run it.

One of our team members asked specifically whether this created a separate path in. It does not. The agentic app is the way in.

What Oracle shipped

Oracle's announcement introduced 25 Claw-powered agentic applications, part of a portfolio of 75 agentic applications. The examples it called out span the suite:

·        Ledger for close readiness and accounting exceptions

·        Workforce Staffing for staffing plans that balance skills, availability, labor rules, and cost

·        Shipping Consolidation for modeling consolidation alternatives in supply chain

·        Account Territory Growth Plan for sales territory and opportunity allocation scenarios

The announcement describes each outcome as starting with a frontier model that reasons and plans, with deterministic enterprise computation handling execution at scale. It also describes a range of automation levels, from quick assistance to governed full auto within explicitly delegated authority.

Where this leaves your investment

My advice from August holds, and if anything it is stronger now.

A Claw is only as capable as the agents and capabilities its agentic app exposes. Well-built workflow agents matter more with Fusion Claw, not less. Agent Studio remains the foundation.

Build workflow agents first. Assemble them into agentic apps once you have components worth assembling. Then look at which of your outcomes genuinely need long-running, data-heavy work, and those are your Claw candidates.

What I am still watching

·        How much outcome authoring is open to customers on their own agentic apps compared with the Oracle-delivered Claw-powered apps, and how customer changes to delivered apps carry forward through quarterly updates.

·        How the governance terms in the announcement, the Enterprise Operating Envelope, the Outcome Trust Harness, and the Outcome Receipt, map to what you actually configure. We discussed the outcome definition in detail. We did not get into the Outcome Receipt.

Both are on the list of follow-up questions we sent to Oracle. I will write about the answers when I have them.

Based on a working session between our team and Oracle's Fusion AI product management group in October 2026, combined with Oracle's public announcement of Fusion Claw on September 29, 2026 and Oracle's public documentation. Product details and pricing mechanics change between releases. Confirm specifics against your own environment, rate card, and Oracle account team. Views are my own.

AI Configurator Is Deprecated in 26C. Here Is What It Means for Prompts You Already Changed.

AI Configurator Is Deprecated in 26C. Here Is What It Means for Prompts You Already Changed.

The notification tells HCM customers to stop using it. It is far less clear about the work you have already done.

If you run Oracle Fusion HCM and anyone on your team has ever edited a prompt behind an embedded AI feature, you probably received a notification recently. Starting in Update 26C, AI Configurator is deprecated and replaced by AI Agent Studio. The immediate action listed is to stop using AI Configurator and make sure prompts are not propagated to production.

Clear enough on its own terms. The question it left our team with was a different one. What happens to the prompts we already modified? Do they stop working at 26C? Do we have to redo that work, and if so, by when?

The notice does not say. We had time scheduled with Oracle's AI product management team, so I asked. Between that conversation and the readiness documentation, here is the fuller picture.

What the documentation actually says

The 26C readiness note is worth reading directly rather than relying on the summary in the notification email. Several things in it matter.

AI Configurator is described as the built-in tool inside Oracle HCM Experience Design Studio, used for locally editing prompts for embedded AI features. It is deprecated in 26C.

If you previously configured prompts with it, the documentation is direct about what you owe. You must re-create and revalidate those configurations in AI Agent Studio. That is not framed as optional.

It also states that you retain access to AI Configurator so you can copy your prompts and reuse them in Agent Studio. This is not a case of the tool vanishing and taking your work with it.

What the documentation does not say is when. That is the part I wanted answered.

What Oracle told us about timing

Existing configurations do not stop working when 26C lands. That was stated plainly. There is no date on which your modified prompts revert to Oracle defaults and your users notice a difference.

The trigger for action is more specific than a release number. Today some of these embedded use cases call a completion endpoint. Oracle is progressively moving each use case over to the invoke agent and invoke sync APIs instead. When a use case you modified makes that move, your prompt change needs to already exist in Agent Studio, in the relevant LLM node, for it to keep applying.

So the re-creation the documentation asks for is real work you need to plan. The sequencing follows Oracle's migration of each individual use case rather than a single cutover date.

From an end user perspective, none of this is visible. Same experience, different plumbing underneath.

A short note on what AI Configurator was

Worth a detour, because plenty of people received this notification without a clear sense of what the tool did.

Fusion has embedded AI features throughout the interface, the ones sitting behind an AI Assist button. Goal setting is the example most people recognize. You click it and the system drafts goals in the SMART format, specific and measurable and the rest.

That formatting instruction lives in a prompt. Some organizations wanted a different structure, different language, or different emphasis for reasons of their own. AI Configurator existed so you could modify that prompt without involving Oracle.

If nobody at your organization ever opened it, this deprecation does not create work for you.

The practical sequence

This is the order we landed on, and I think it generalizes to most HCM customers in the same position.

1.        Build an inventory of what you actually modified. Which use cases, what changed, and why. This is the step people skip and it is the one that matters most.

2.        Copy the prompts out of AI Configurator. Put them somewhere you control rather than leaving them inside a tool being retired.

3.        If something cannot be recovered, ask Oracle. They confirmed they can retrieve prompts from the underlying tables if needed.

4.        Re-create and revalidate in Agent Studio, sequenced against Oracle moving each use case to the agent framework.

On the first step, the notification identified the environments where prompts had been configured, which is a helpful pointer, but it does not tell you which specific use cases were touched or what the change was. That inventory is yours to build.

I would also involve whoever asked for the original change rather than treating this as a purely technical exercise. Someone modified that prompt for a business reason. Migrating the text without carrying the reasoning forward means you cannot revalidate it properly, and revalidation is explicitly part of what Oracle is asking for.

The access requirement people will trip over

The readiness note contains one line that is easy to skim past. You must have access to use AI Agent Studio.

That access is not automatic. AI Configurator lived inside HCM Experience Design Studio. Agent Studio is a separate tool with its own access requirements. There is a reasonable chance the person who made your original prompt changes cannot get into Agent Studio today.

Sort that out before you reach the migration work rather than during it.

On cost

I asked directly whether maintaining these configurations after the move generates any additional charge. The answer was no.

The reasoning holds up. These embedded use cases are simple calls, summarization and short text generation, comfortably within what a hosted model handles. Oracle hosts a model for this category of work and treats calls to it differently from models it has to source externally. This kind of usage did not generate a bill before and does not after.

I have written separately about how that metering works, because it is the most misunderstood part of Fusion AI right now.

Why the destination is better than the origin

I will be honest that a deprecation notice with an unclear impact statement is irritating, and I said as much on the call. That said, where this lands is genuinely better, and the readiness documentation is specific about why.

·        Prompt configuration and agent configuration stop living in two separate tools.

·        Access control improves. Prompts inherit the same security model as agents, so an HCM administrator cannot configure prompts belonging to other product families. If you care about governance, that is substantive rather than cosmetic.

·        You get a broader range of models to choose from.

·        You can evaluate a prompt before publishing it. Editing prompt text in isolation and hoping for the best is how most of us have worked until now.

·        You get the full set of workflow nodes for editing, plus the ability to add and remove variables, which AI Configurator did not offer.

What I would still like to see

Two things, offered as the feedback I would want if our positions were reversed.

Clearer impact statements in deprecation communications. The notice told customers to stop using a tool without addressing the status of work already done with it. That single gap produced more internal speculation on our side than the migration itself will produce work. One sentence would have covered it.

Published sequencing for the underlying migration. Because the practical trigger is Oracle moving each use case to the agent framework, knowing which use cases move in which update would let customers plan instead of react. That timing is not visible from outside today.

Neither of these is a reason to be worried about the change. Both are the kind of thing that would make the next one land better because this change is actually a huge improvement and something to be excited about!

Where to read more

Oracle's 26C readiness note covering the deprecation and transition is at docs.oracle.com under the HCM 26C common technologies what's new, titled Deprecation of AI Configurator and Transition to AI Agent Studio.

If you received the notification and have not started, begin with the inventory. Everything else depends on knowing what you actually changed.

Based on Oracle's 26C readiness documentation and customer notification, combined with a working session between our team and Oracle's Fusion AI product management group in August 2026. Statements about migration timing and cost reflect that conversation and are not a substitute for Oracle's published guidance. Confirm specifics against your own environment and your Oracle account team. Views are my own.

Making Sense of Oracle AI Units

Making Sense of Oracle AI Units

What you actually pay for when agents run in Fusion, and the pricing change a lot of people missed.

Every conversation I have about Fusion AI eventually arrives at the same question, usually about twenty minutes in. Someone asks what this is going to cost. Not the license. The running cost, once agents are actually doing work.

It is a fair question, and until recently I did not have a clean answer for it. We recently spent time with Oracle's AI product management team going through the mechanics, and I want to write down what I learned. The model is more reasonable than most people assume, and I think the way it gets explained, depending on who you talk to, is part of why people assume otherwise.

What an AI Unit actually measures

An AI Unit captures two things at once: the type of action an agent performs, and the number of tokens that action consumes.

The token side works in boundaries of roughly 10,000. Your consumption gets grouped into those boundaries, and then a multiplier is applied based on what the agent was doing. A reasoning action, where an agent takes a prompt and some context and comes back with a plan, falls into what Oracle calls a basic or general action.

In the example we were walked through, a general reasoning action came out to 5 AI units, which is roughly 5 cents.

The distinction that matters most

Here is the part that reframed the whole thing for me. The multiplier depends on whether Oracle hosts the model or has to call out to someone else's.

Oracle hosts GPT OSS, the 120 billion parameter open model. Because they run that infrastructure and absorb the cost, calls to it carry a zero multiplier. In practical terms, free.

Premium models are the ones Oracle reaches out to other providers for. OpenAI's models, and more recently Google's Gemini models. Oracle pays those providers, and that cost passes through to you.

The way it was put to us was simple: any model we host, we will continue to offer at a zero multiplier. Any model we have to call out to is where charging happens. Once I understood that, most of my confusion about Fusion AI cost went away.

The embedded features you already use are not about to start costing you

If you have been clicking the AI Assist buttons scattered around Fusion, goal setting being the one most people know, those are simple LLM calls. Summarization and text generation, that category of work. The hosted model is more than capable of handling them.

Those were free when they ran on Cohere, and they stay free as they migrate over to Agent Studio. If you were bracing for a bill when that migration lands, you can stop.

The pricing change a lot of people missed

This is the part I think deserves far more attention than it has received.

The original pricing model triggered billing based on customization. Build a custom agent in Agent Studio, that triggered billing. Install one from the marketplace, same result. This applied even when the underlying model was the free hosted one.

That model is gone. Oracle told us plainly that it generated a lot of customer feedback and that it was confusing. The underlying problem was definitional. Drawing a clean line between a custom agent and a configured Oracle-delivered agent turned out to be cumbersome in practice. They started strict, tried loosening the definition, and eventually concluded the whole approach was the wrong shape.

So they moved to usage. You are charged for what you consume, not for whether someone classifies your agent as custom.

Per-user licensing still exists if it suits your procurement better, but most customers are choosing usage, mainly because it lets you start small and grow into it.

I want to give credit here without laying it on too thick. Retiring a pricing model because customers found it confusing is not a small thing, and it is the kind of decision that tends to surface only in conversations like this one. That is part of why I am writing it down.

Agentic apps use the same meter

One of our architects asked whether agentic apps are metered differently from agents built in Agent Studio. They are not.

An agentic app is a collection of agents, so what gets captured is the orchestrator performing its reasoning and delegation, plus the activities of each workflow agent underneath it. Same units, same mechanics, more moving parts.

Where I would spend premium model budget

You choose the model. Oracle's own recommendation, and I agree with it, is to use a premium model for the orchestration layer at minimum. If an orchestrator is deciding which agents to invoke and in what order, weak reasoning at that layer degrades everything downstream of it.

The workflow agents doing narrower, more deterministic work are a different story. That is where hosted inference earns its keep.

The advice that came attached to that recommendation is the part I would underline: run your own evaluation against your own use cases. Model quality on a vendor benchmark is not the same as model quality in your configured environment, with your data and your prompts. That gap is where most disappointment lives.

What is not settled

Models that generate audio or video are considerably more expensive to run and will carry a higher multiplier. Those rates were not on the rate card when we spoke.

I also want to be careful about what this post is. I am reporting a conversation, not a contract. The mechanics held up well under questioning, but the interaction between action type and model tier is exactly the sort of detail worth confirming in writing. Before you build a forecast on any of it, verify against your own rate card and your Oracle account team.

The short version

If you are trying to build a cost model for Fusion AI, start here: work out which of your use cases genuinely require a premium model, and assume the rest can run on hosted inference at no incremental cost.

That framing got us considerably further than trying to price every individual interaction, and it turns the cost conversation into a design and ROI conversation, which is a much better conversation to be having.

Based on a working session between our team and Oracle's Fusion AI product management group in August 2026, combined with Oracle's public documentation and sample repository. Product details and pricing mechanics change between releases. Confirm specifics against your own environment, rate card, and Oracle account team. Views are my own.

Oracle Put Fusion AI Development in VS Code and Made It Work With Coding Assistants

Oracle Put Fusion AI Development in VS Code and Made It Work With Coding Assistants

A public repo, a CLI, a VS Code extension, and a testing framework almost nobody is talking about.

I have spent years asking Oracle product teams to meet developers where they already work and different platform teams have made great strides in this area over the years with varying degrees of success.

However, I want to be direct about this one: what the Fusion AI team has shipped in a short spawn of time around this theme, I think deserves more attention than it is getting.

There is a public GitHub repository at github.com/oracle/fusion-ai-studio. It contains skills, a CLI, a VS Code extension, Oracle-authored sample applications and workflows, and how-to guides. It is published under the Universal Permissive License and updated regularly.

What is actually in it

·        A skills directory that coding assistants can read

·        An aiapps directory with Oracle-authored template applications and workflows

·        The VS Code extension and CLI

·        A how-to folder covering installation and incremental updates

Sample apps span HCM, including career development, journeys, learning, absences, and succession management, along with supply chain areas like inventory and cost management, and procurement content covering purchase orders and agreements.

The repository moved to release-based branching, with release-26C as the current branch and a dedicated branch planned for each future release. Before that change you downloaded ZIP files, extracted them, and placed the contents into the right directories yourself. Now you clone the branch matching your release and pull updates.

That is a small change, but it tells you something about how the team is thinking. Somebody looked at how people actually consume this and removed the friction.

It works with coding assistants

The VS Code extension provides guided commands and visual editing for setting up a workspace, connecting to the right environment, and creating or opening artifacts. The how-to guide walks through using it alongside Codex.

Oracle's product management team also described pointing Claude Code at a workspace directory. There is a CLAUDE.md in the repo, and the assistant reads the skills and works with Agent Studio from that context.

The reason this matters is not novelty. It is that describing a business outcome and having an assistant apply the change across workflows, business objects, agents, and supporting artifacts is a fundamentally different working mode than opening and editing each file in turn. Anyone who has built a moderately complex agent knows how many artifacts a single change can touch.

The builder assistant

In 26C, Agent Studio gained a natural language interface for describing what you want and generating the workflow agents, the business object definitions, and the agentic app itself.

Two things about it are worth calling out. The first is cost. Oracle's product management team told us the builder assistant does not consume AI units, on the basis that you are instructing it rather than running inference against your business data. I have not been able to find that stated anywhere in Oracle's public documentation, so I would treat it as reported rather than confirmed, and check it against your own environment before you build an assumption on it. The second is that it works in reverse. You can point it at an existing workflow, including one Oracle delivered, and ask it to explain what the thing does and why. If you have ever inherited a complicated workflow with no documentation, you already understand the value.

ATLAS is the part people are sleeping on

Sitting in the repository change log is a framework called ATLAS, the Agentic Testing and Lifecycle Automation Suite. It landed in early August and I have seen almost no discussion of it anywhere.

Consider the problem it addresses. You deploy agents. Models change. Providers deprecate versions and release new ones. How do you swap models across a fleet of agents and know with any confidence that everything still behaves the way it should?

ATLAS turns agent scenarios into repeatable, executable tests. Each test combines an input, expected workflow behavior, representative replay data, and evaluation criteria. It validates structurally, confirming required or prohibited execution paths, and semantically, for natural language responses where exact text matching would be too rigid.

The capabilities I find most interesting:

·        File-based replay holds external service boundaries stable while routing, conditions, code, and LLM nodes continue to execute. That makes regressions reproducible rather than dependent on whatever the source system happened to return that day.

·        Labeled runs let you compare a baseline against a candidate model using consistent evidence.

·        Optimization sweeps evaluate model placement at the individual LLM node level, using quality, latency, and usage data. Rather than picking one model for an entire agent, you work out which specific node needs the stronger one.

·        It integrates with local development and CI/CD, producing reports covering results, evaluated outputs, warnings, token usage, and execution duration.

Connect that last point back to cost and it becomes more interesting still. Node-level model evaluation is the mechanism for answering the question I keep raising internally: which parts of a workflow actually justify a premium model? ATLAS replaces intuition with evidence, and evidence is what a finance conversation requires.

Where this is heading

Oracle's product team described the next phase as fleet management and governance. Once an organization has hundreds of agents deployed, the questions change. It stops being how do I build one and becomes how do I understand what they are all doing, whether any of them are misbehaving, and how I make wholesale changes safely.

The CLI investment is aimed at that broader lifecycle rather than only the build step. Designing, reasoning about which agents make sense for a given business problem, then evaluating and maintaining them once they exist.

A few honest caveats

·        The repository does not accept external pull requests. You can consume it, not contribute to it.

·        The samples carry a broad disclaimer and no warranty. Treat them as examples to learn from rather than production code to deploy as-is.

·        The documented assistant path is most complete for Codex. Other assistants work, but expect to do some of your own figuring.

None of that changes my overall read. An Oracle SaaS product team publishing a public repository, shipping a VS Code extension, supporting third-party coding assistants, and building a testing framework for agent lifecycle management represents a real shift in posture. I would like to see it spread to other Oracle products on the Low Code front with more vigor (such as OIC and VBCS), and I have said so to anyone at Oracle who will listen and I know the teams are working on it!

Where to start

If you work with Fusion AI and have not cloned the repository yet, start there and begin with the how-to folder. Reading Oracle's own sample workflows taught me more about the intended patterns than any presentation I have sat through.

Based on a working session between our team and Oracle's Fusion AI product management group in August 2026, combined with Oracle's public documentation and sample repository. Product details and pricing mechanics change between releases. Confirm specifics against your own environment, rate card, and Oracle account team. Views are my own.

AI Agent Studio or Agentic Apps? Sorting Out Where Fusion AI Is Going

We spent a good part of last year getting our arms around AI Agent Studio. Each release made it better, and it was starting to feel ready for real work.

Then Oracle announced agentic apps, and the immediate reaction on my team was: what is that, and what does it mean for everything we just learned?

I doubt we were alone in that. So when we got time with Oracle's AI product management team, this was the first question I asked.

The short answer

Agentic apps are not a replacement for Agent Studio. They are a layer built on top of it that allow the ecosystem to become very useful in the enterprise. The progression makes considerably more sense once you see the steps that got there.

How the model evolved

Oracle started where most of the industry started, with a supervisor and worker pattern. A supervisor agent reasons about a request and delegates to worker agents.

Then they introduced workflow agents, and this is where it gets interesting. Workflow agents follow a deterministic path built from a graph of nodes: LLM nodes, code nodes, switch nodes, agent nodes. You define the route rather than asking a supervisor to work it out at runtime. You need agentic capability at specific points, an LLM call here, a decision there, but the sequence is not a mystery that has to be solved on every execution.

That matches my experience closely. Most of what we want to automate in Fusion is not open-ended reasoning. It is a known process that requires judgment at a handful of steps.

So what is an agentic app

An agentic app is a container. Inside it sits one or more workflows, and in practice usually somewhere between two and six, depending on the outcome you are after.

What makes it more than a folder is shared context and a common goal. The orchestrator tells the participating agents what the business objective is, and each one contributes its part while working from shared context rather than operating in isolation.

Oracle frames this as a new category of application: one that does not simply store your data or display your data, but interprets it and tells you what to do about it. That is marketing language, but the architecture underneath it holds up.

What makes a workflow agentic-app capable

This is the practical part, and Oracle's public sample repository documents it better than any announcement I have seen.

A workflow participating in an agentic app has to handle messages coming from the app. The pattern is a switch node at the front reading a message hint variable, then branching based on what the app is asking for: an initial display, a summary, an answer to a query, or an action to execute.

The workflow also declares what it is permitted to produce. There is an App Experience tab where you enable actions, enable communications, and select which visual widgets the agent is allowed to render, things like tables, charts, and Sankey diagrams.

Two details worth knowing before you start building anything, based on knowledge I believe to be true:

·        The framework enforces a 60 second response limit. That is a real design constraint, and it is one of the stronger arguments for deterministic workflows over supervisor orchestration, because predictable latency matters a great deal when you have a hard ceiling.

·        User input and chat history are not passed automatically into internal nodes. You have to wire those variables through explicitly.

Where this leaves your investment

If you have been building in Agent Studio, that work is not stranded. Agent Studio is where agents and workflows get defined, and agentic apps consume what you build there. The studio remains the foundation, and time spent learning it still pays off drastically.

If you are deciding where to start, I would build workflow agents first and treat the agentic app as the assembly step once you have components worth assembling.

On the commercial side, taking an agentic app into production requires platform access beyond ordinary usage. You can build and test in lower environments consuming standard AI units. I am deliberately not quoting figures. I would simply say have that conversation with your account team earlier rather than later, because it changes the shape of a business case.

What I am still watching

How much arrives out of the box versus how much we build ourselves. The value of platform access depends heavily on what comes with it. Oracle has committed to delivering high value agentic apps built by the domain teams, and the public sample repository has been filling out steadily across HCM, supply chain, and procurement. I want to see how far that goes and how quickly.

I am also watching how the two builder experiences settle. There is a visual, low-code builder aimed at domain experts, and a pro-code path through the CLI and VS Code. Both are genuinely useful today. Whether they stay equally capable as the platform matures is a question worth revisiting in a few releases.

Neither of those is a criticism. They are the questions I would want answered before committing a roadmap, and I would rather write them down than pretend the picture is fully settled.

Based on a working session between our team and Oracle's Fusion AI product management group in August 2026, combined with Oracle's public documentation and sample repository. Product details and pricing mechanics change between releases. Confirm specifics against your own environment, rate card, and Oracle account team. Views are my own.