Oracle 26C: The Boring Feature That Will Quietly Run Your Agent Fleet - the Debugger, the CLI, Policy Models, and the End of AI Configurator
Part one of two. This piece covers the AI Agent Studio platform changes in 26C. Part two covers what 26C means for HCM specifically - the migration work, the agent estate module by module + pricing
Disclaimer: Everything below is taken from Oracle’s official 26C readiness documentation for Common Technologies and User Experience, published this week. These are confirmed 26C features, not roadmap items. As always, functionality described in readiness documents can change at Oracle’s discretion before it reaches your pod.
The 26C readiness notes landed this week, and the question you’ve been getting from clients and colleagues would be the usual one: what’s actually new, and what do I need to do about it?
The honest answer is that the headline of 26C is not a new agent. It’s the platform underneath the agents. Steve Miranda has talked about the installed base growing from maybe a hundred agents at launch to well over a thousand. At that scale, the question customers face changes from, Can we build an agent for this? to How do we run all of these properly? 26C is Oracle’s answer to that second question, and it makes this one of the most significant quarterly update for AI Agent Studio since the product launched.
Quick Summary
If you just want the headlines:
AI Configurator is deprecated in 26C. All prompts configured there must be re-created and re-validated in AI Agent Studio.
A new Debugger lets you pause on a node, step through a flow one node at a time, rerun from any point, force a node's output to test what follows, and save reusable run profiles.
A new command line interface (CLI) works standalone or through the VS Code extension, and supports Codex-assisted and Claude Code-assisted editing.
Policy Models bring deterministic, repeatable decision logic into agent workflows through a new policy node.
The open Agent2Agent (A2A) protocol lets published Fusion agents be discovered and called by external agents, and vice versa.
Long-running sessions support multi-day agent conversations. Long-term memory adds episodic memory notes and user preferences shared across agents.
Bring Your Own LLM is now available via a service request to My Oracle Cloud Support, typically completed in two weeks, when OCI is your model provider.
HCM administrators are now scoped by product family for prompt configuration.
AI Configurator Is Deprecated
The change that might consume the most project hours is also the one buried deepest in the notes. AI Configurator - the prompt editor built into HCM Experience Design Studio for locally editing prompts behind embedded AI features - is deprecated in 26C. Any prompts you configured there must be re-created and re-validated in AI Agent Studio. The old tool stays accessible, but only so you can copy your prompts out of it.
There’s a governance change riding along with this. HCM administrators are now scoped by product family, so a compensation admin can no longer edit recruiting prompts. In multi-pillar implementations that’s a welcome improvement - it just needs to be in your security design before the upgrade, not discovered after it.
If you’ve customised AI Assist prompts across recruiting, talent or HR over the past year, this is a real migration and regression testing exercise, and it lands in the same window as your normal 26C testing. I cover what that means practically in part two. The short version: treat it as a workstream with an owner, not a line on the upgrade checklist.
The Debugger
Agent Studio finally has a real debugger for agentic flows. Drop a breakpoint on a node and execution pauses there, so you can walk the flow one node at a time rather than watching it run end to end. From any node you can rerun forward, and you can pin or override what a node hands back to see how everything downstream reacts to a value you’ve forced in. The test setups you keep coming back to get saved as run profiles, so you’re not rebuilding them by hand every time.
For anyone who has sat reading trace logs trying to reconstruct why a team of agents produced a daft answer, this is the part of 26C that changes the day job most. Agent misbehaviour used to be something you reasoned about after it happened. Now you can halt the flow mid-execution and watch the decision being made. That pays off twice in delivery. Defects surface during the build instead of in a client demo, and the reasoning becomes visible enough that “the agent just did that” stops being a good enough answer for anyone in the room.
The CLI
There’s now a command line interface for Agent Studio, run on its own or from a terminal inside the VS Code extension. It handles the whole lifecycle of an artefact locally. You author it, validate it, debug and test it, then save it back, so none of that has to happen in the browser canvas any more. Artefacts can be validated before they’re handed on, and the part worth dwelling on is that it supports building and editing agents with Codex and with Claude Code.
Two years ago, Oracle baking first-class support for rival vendors’ coding assistants into its own product would have raised eyebrows. It is the right move regardless. Once an agent lives as a file on disk, it can sit in version control, go through the same peer review as any other change, and move across environments like normal code. The canvas is not going anywhere for business users. The CLI is there for the teams who have outgrown it.
Policy Models
You can now create policy models in Agent Studio for reliable, repeatable, deterministic policy-based decision-making at runtime, invoked from agentic workflows through a new policy node.
Approval limits, eligibility tests, the compliance checks where 'mostly right' is simply a fail, all of that can now be expressed as deterministic policy logic, with the LLM's probabilistic reasoning kept for the parts of the flow that genuinely need it. Clients have been asking how to get deterministic behaviour out of agentic workflows for a year, and until now the honest answer involved workarounds. Now it’s a platform feature.
Agent2Agent (A2A)
Published Fusion agents can now collaborate with agents on other platforms through the open A2A protocol. One thing to hold onto: an Agent Card broadcasts what an agent can do to anything that comes looking, so publishing needs to be a deliberate, per-agent decision rather than the default setting.
In practice this means an agent in Slack or Teams can ask a Fusion agent a payroll question and get a properly governed answer. One thing to hold onto: an Agent Card broadcasts what an agent can do to anything that comes looking, so publishing wants to be a deliberate, per-agent decision rather than the default setting.
Long-Running Sessions and Long-Term Memory
Two related changes. First, agent conversations can now stretch across extended, including multi-day, interactions. If the system needs time to work through a complex query, or you need to leave the conversation to gather data, you can rejoin the same conversation in progress.
Second, agents get long-term memory, in two components. Episodic memory captures summaries of past interactions as memory notes, generated automatically through a memory curation process. User preferences are shared across agents, so a person gets a consistent experience regardless of which agent they’re talking to.
Shared preferences across a thousand-agent estate is what makes the whole thing start to feel like one assistant rather than a directory of bots. One thing to flag to your privacy team early: memory notes are derived personal data, and most organisations don’t yet have a retention position for them. Better to write one now than after the first data subject access request mentions it.
Bring Your Own LLM
You can now request enablement of additional supported LLMs through a service request to My Oracle Cloud Support. Most requests are completed within two weeks. More complex model integrations can take up to four. The BYOLLM path applies when Oracle Cloud Infrastructure is selected as the model provider, and doesn’t apply to the basic and premium models Oracle provides itself. For reference, Oracle’s published line-up in Agent Studio currently includes GPT 5.1 mini, GPT 4.1 mini and GPT-OSS-120B.
Model choice has been the loudest ask from regulated clients since Agent Studio launched. Now that the choice is available, the accountability comes with it - which model runs which workload becomes a decision your architecture governance owns, with cost and behaviour consequences attached. The cost side matters more than it used to, for reasons covered in part two.
A Note on the Agentic Applications Builder
You’ll see the Agentic Applications Builder mentioned in a lot of 26C coverage - the natural-language, low-code builder for assembling agentic applications from Oracle, partner and custom agents, announced in Oracle’s London press release on 24 March. Worth knowing: in the readiness documentation it sits under 26B, not 26C. It’s part of the same platform story but if you present it as a 26C feature in front of a client, someone will correct you, and they’ll be right.
What to Make of All This
Taken together, 26C reads like Oracle deciding that agents are software and should be treated accordingly. A debugger, a CLI, version control, deterministic policy logic, a standard interoperability protocol - this is the toolchain you’d expect around any serious development platform, and until this quarter Agent Studio didn’t have it.
The consolidation matters too. With AI Configurator gone, Agent Studio becomes the single place where all prompt configuration and agent management happens. That’s cleaner architecture and better governance, at the price of a migration that every customer with customised prompts now has to run.
That said, most of what’s new in 26C only pays off if implementation teams actually use it. Breakpoints, run profiles and peer-reviewed agent definitions are only valuable to teams that bring engineering discipline to agent work, and in my experience most programmes aren’t set up that way yet. The platform has grown up faster than the delivery practices around it.
Part two covers the other half of the story: what 26C asks of your ERP/HCM programme specifically, the agent landscape across all nine pillars, and the pricing question I can only partly answer.