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.

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.

AI Enhances Oracle Cloud HCM Support: Elevating Insight with the Oracle HCM Intel CLI

Oracle Gen AI + Public HCM Metadata = Limitless Insight

The Oracle HCM Intel CLI Tool is a professional-grade command-line assistant that uses your extracted HCM metadata and combines it with the capabilities of Oracle’s OCI Generative AI service (powered by Cohere) or OpenAI, or the LLM of your choice, giving you intelligent insights relative to HCM data structures. And it's all done securely and locally—no sensitive data leaves your environment.

By referencing public HCM data definitions, this project demonstrates just how powerfully Oracle Gen AI, and other AI offerings, can be applied in the enterprise to support analysts, developers, and architects with tasks such as:

• Generating SQL queries based on natural language
• Suggesting joins between key Oracle HCM tables
• Explaining and optimizing existing SQL
• Creating BI Publisher-ready templates
• Performing semantic metadata searches

Project GitHub: oracle_hcm_intel_cli

Demo: Watch Demo

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Built With Practical Enterprise Needs in Mind

The tool includes features that any real-world implementation would benefit from:

• Encrypted .env handling using runtime secrets
• Markdown output for reporting or Copilot/Teams usage
• Audit mode for logging what metadata was passed to the LLM
• Modular provider support for OpenAI or Oracle Gen AI
• Interactive prompt chaining for analysts and non-developers

It’s optimized for real users working in real environments—offering flexibility without sacrificing security or precision.

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Powered by Oracle Technology, Honoring Oracle’s Vision

This CLI tool is powered by Oracle’s own public documentation and showcases the capabilities of Oracle Cloud Infrastructure’s Generative AI platform. It's a clear example of how AI and metadata can be responsibly applied in the enterprise, especially when building tooling around Oracle’s HCM ecosystem.

Oracle’s Gen AI service is the star of the show here—it brings context, comprehension, and creative query generation to the hands of business users and technical professionals alike.

Earlier this month, I introduced a Metadata Extractor CLI Tool built to programmatically parse Oracle Cloud HCM’s public documentation and extract table and view metadata into JSON format. That foundational tool—available on GitHub at oracle_hcm_metadata_extractor—has enabled this powerful second act: the Oracle HCM Intel CLI.

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Get Started

1. Extract metadata using: oracle_hcm_metadata_extractor

2. Query and explore with: oracle_hcm_intel_cli

Whether you’re working in HCM data architecture, reporting, or support—this is your AI-powered sidekick.

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I hope this project inspires others to build upon Oracle’s cloud platform and apply Generative AI responsibly. The future of enterprise tooling is intelligent, secure, and deeply integrated—and with Oracle Gen AI, that future is now.

—
Julio @ OracleSpot.net

Introducing the Oracle HCM Fast Formula Agent – Built with AI to Help You Work Smarter

Introducing the Oracle HCM Fast Formula Agent – Built with AI to Help You Work Smarter

After months of juggling fast formulas, fielding questions from HR teams, and trying to decode cryptic payroll logic at 2AM, I decided it was time to build something better.

Today, I'm happy to share an open-source project that came from that journey:

Oracle HCM Fast Formula Agent – A smart, simple tool that uses Generative AI to help you generate, validate, explain, and modify Oracle HCM Fast Formulas, and more!

Check out a quick demo - Watch on Loom

Why I Built It

Working in the Oracle Cloud HCM space, I saw firsthand how challenging it can be to:

• Interpret legacy Fast Formulas written years ago
• Validate logic across hundreds of conditional paths
• Explain formula outcomes to business users
• Generate new formulas based on complex rules, under tight deadlines

And with the rise of AI, I thought: Why not build an assistant that understands Oracle HCM and speaks "formula"?

What It Does

With this Flask-based app, you can:

✅ Generate new Fast Formulas from plain-English descriptions
✅ Explain existing formulas in readable, line-by-line language
✅ Validate and suggest corrections for logic/syntax errors
✅ Modify formulas based on user input
✅ Extend the tool with new advanced actions using a plugin system, like the included Summarize advanced action!

It even has a clean admin panel and a dark mode toggle because... well, late-night debugging deserves a good theme.

📦 Plugin Architecture

Want to add your own action like summarize_formula or compare_logic_blocks? Just drop it in the plugins/ folder. No changes to the main app required. It's built for extensibility.

🐳 Docker Ready

Whether you’re developing locally or deploying in the cloud, the app comes with a Dockerfile that runs the app via Gunicorn for production-grade performance.

🔗 Get Started

Clone it, run it, or fork it for your own use:

👉 GitHub: https://github.com/TheOwner-glitch/hcm-fast-formula-agent
📹 Demo: Watch the video

A Call to the Oracle Community

I built this for consultants, analysts, and developers who need to move fast and support critical Oracle HCM needs in the Fast Formula space, but more importantly to share an example of how AI Powered application development can be compelling and reduce the need for conditional and hardcoded business logic by instead leveraging the power of generative AI  to interpret natural language. If you try it out and find it helpful, or have ideas for improving it, I’d love to collaborate.

Thanks for reading. Now go automate your fast formula headaches away.

- Julio