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.

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Julio @ OracleSpot.net

Oracle Fusion Cloud - BIP Performance Tuning Tips and Documentation

During the early days of Oracle Cloud being adopted relative to SaaS technologies like ERP and HCM, it was quite common to develop extracts and reports using complex custom sql data models that would either be downloaded by users or scheduled to extract data and interface it to external systems. Overtime, Oracle has released guidelines and best practices to follow, and efforts like the SQL guardrails have emerged to prevent poor performing custom SQL from impacting environment performance and stability. To that end, I have been aggregating useful links to documentation around this topic from our interactions with Oracle Support over the past few months, which are consolidated in this post.

Links to Documentation:

• Optimize Performance of Multi-Thread Processes (oracle.com) [docs.oracle.com]This deals with multi-threading in HCM, this configuration will allow jobs to allocate more threads in the environment and therefore run faster.
• OTBI & BICC PSR Technote (2679006.1 [support.oracle.com]) and OTBI self-service [cloudcustomerconnect.oracle.com] monitoring
• The OTBI self-service dashboards can be downladed and installed in your environment so that you can see usage and monitor it. Some of the reports may not work after imported and could require modifications.
• BIP PSR Technote (2800118.1 [support.oracle.com]) and BIP self-service [oracle.com] monitoring
• Similarly to OTBI, there's BIP self-service reports that you can use to monitor performance and activity, and act upon those findings.
• BIP Datamodel validation enhanced with Saffron SQL pattern checks, available to customers in BIP Administration UI
• For custom BIP SQLs, Saffron [saffron.subnet1ad3phxsu.psr4saas01phx.oraclevcn.com] output along with the ref. to BIP Technote (2800118.1 [support.oracle.com]), that describes reported patterns in more details
• Fusion Applications BI Publisher : How to control BI Publisher Data Model time out (Doc ID 1921393.1 [mosemp.us.oracle.com])
• Fusion Applications BI Publisher : How To Configure And Use Audit In BI Publisher For Fusion SaaS Cloud Customers? (Doc ID 2059102.1 [mosemp.us.oracle.com]) to schedule monitoring of the health of your BI jobs.
• This is a key piece to proactive observing which Reports are nearing a critical threshold or identifying a sudden variation in runtime.
• Saffron report with tuning suggestions can be provided or you can use the new feature DM Validate (SAFfron results in Data Model UI) and fix the detected pattern by reviewing the Tech Note 2800118.1 [mosemp.us.oracle.com].
• For custom BIP SQLs, Saffron [saffron.subnet1ad3phxsu.psr4saas01phx.oraclevcn.com] output along with the ref. to BIP Technote (2800118.1 [support.oracle.com]), that describes reported patterns in more details
• Fusion Applications - BI Publisher Memory Guard Best Practices (Doc ID 2472630.1 [mosemp.us.oracle.com])

For scheduled reports, Oracle recommends the following guidelines:

• Having a temporary backlog (wait queue) is expected behavior, as long as the backlog get cleared over the next 24 hours.
• If the customer expect the jobs to get picked up immediately, submit via ‘online’ and wait – as long as they not hit 500 sec limit.
• If there are any jobs that need to be processed with high priority (over the rest), it's advised to mark reports as ‘critical’ so that they picked up by the first available thread.
• Oracle advises customers to tune their custom reports so that they complete faster and not hold threads for long time.
• Oracle advises customers schedule less impactful jobs during off-peak or weekend hours – manage scheduler resource smart.

Additionally, note the following:

• With Release 13 all configuration values including BI Publisher memory guard settings are preset based on your earlier Pod sizing request and cannot be changed.
• For memory guard the Oracle SaaS performance team has calculated and set the largest values that still provide a robust and stable reporting environment for all users to meet business requirements.
• The BI Service must support many concurrent users and these settings act as guard rails so an individual report cannot disrupt your entire service and impact the business.

Ultimately, effective instance management is critical for ensuring that your Cloud HCM system is running smoothly and effectively. Allocating resources based on the usage and demand will require co-ordination with various teams. There is a common misunderstanding that each HCM tool such as HDL, HCM extracts, or manual ESS job submissions operates on its own pool of threads. However, in reality, they all share the same ESS pool of threads. It is, therefore, advisable for customers to properly maintain and optimize their runbook to avoid overburdening the system and creating resource constraints.

Lastly, depending on the size of your pods, you have the option to allocate pods for specific tasks. For example:

• BulkLoading/ Performance testing/Payroll parallel runs: Pod with highest threads is a good candidate be utilized for bulk data loading, payroll parallel runs, and similar resource-intensive tasks such as performance testing.

The below graphic shows how ESS Threads are consumed, to exemplify the statements made prior: