Array ( [0] => Array ( [_id] => 6847e63b86b706a70ff128a5 [name] => New Hire Onboarding Agent [description] => New Hire Onboarding Agent is a solution designed by ZBrain to streamline and automate the initial onboarding process for new employees. It addresses challenges such as manual administrative tasks, inconsistent communication, and coordination delays, enabling HR teams to deliver a structured and timely onboarding experience across all functions and locations.

The agent integrates with the HR Management System and activates when a new hire record is created. It automates key tasks including personalized welcome communications, orientation scheduling, account provisioning, and role-specific training assignments. The workflows dynamically adjust based on employee attributes such as role, location, and seniority, ensuring compliance with internal policies and scalability across hiring volumes.

By automating routine onboarding activities, the agent reduces administrative workload and accelerates time-to-productivity for new hires. It also provides a feedback mechanism for HR to monitor progress and refine onboarding steps as needed. This results in improved operational efficiency and a consistent, professional onboarding experience enterprise-wide.

[image] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/onboarding-handbook-generator-agent.svg [video] => [icon] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/onboarding-handbook-generator-agent.svg [sourceType] => FILE [status] => REQUEST [department] => Human Resources [subDepartment] => Employee Lifecycle [process] => Recruiting and Staffing [subtitle] => Detects new employee records in the HRM system and automatically initiates onboarding tasks like sending welcome emails, scheduling orientation, and assigning training modules. [route] => new-hire-onboarding-agent [addedOn] => 1749542459267 [modifiedOn] => 1749542459267 ) [1] => Array ( [_id] => 68370b30792b893ca20ba9b1 [name] => Job Description Creation Agent [description] =>

ZBrain's Job Description Creation Agent accelerates the creation of high-quality job descriptions by automating the drafting process based on user requirements. Powered by a Large Language Model (LLM) and other utilities, the agent analyzes user input, such as job titles, skills, and experience levels, to generate precise, role-aligned JDs. It integrates seamlessly with HR platforms, reducing manual effort, improving consistency, and ensuring every job posting supports employer branding and compliance.

Challenges the Job Description Creation Agent Addresses

Manual job description creation is slow, inconsistent, and often plagued by incomplete inputs and fragmented data sources. HR teams spend excessive time interpreting vague requirements, reconciling with historical roles, and drafting content that must meet compliance and branding standards. These inefficiencies delay job postings, increase compliance risks, and drain HR resources, especially as hiring volumes and regulatory complexity grow.

ZBrain's Job Description Creation Agent leverages LLM-powered analysis to instantly analyze job requirements, consolidate data, and generate structured, accurate job descriptions. It retrieves up-to-date role information, incorporates essential skills and qualifications, and outputs detailed and accurate JDs ready for review. By automating and standardizing the process, the agent accelerates time-to-hire, reduces manual effort, and enables HR teams to deliver tailored, on-brand job descriptions at scale.

How the Agent Works

ZBrain's job description creation agent automates the creation of relevant JDs for diverse roles, ensuring context and role alignment. Below, we outline the detailed steps that illustrate the agent's workflow, from the initial input of user queries to continuous improvement:

Step 1: User Query Reception and Job Opportunities Retrieval

The agent workflow begins when a user submits a job description creation request. The agent then identifies and retrieves the latest job opportunities from the integrated system to provide full context for matching.

Key Tasks:

• Agent Activation: The agent gets triggered upon receiving a new user request to create a JD with specific requirements via its interface.
• Retrieve Job Opportunities: The agent extracts all current job roles, including fields such as opportunity Id, title, job family (department), description, and date of posting.
• Organize Data: It preprocesses and structures retrieved job data for downstream analysis.

Outcome:

• Job Opportunity Data Organized: Ensures the agent has access to the latest, well-structured job opportunities for accurate analysis and selection.

Step 2: Analysis of User Query and Relevant Job Identification

The agent uses an LLM to analyze the user's requirements and identify the most relevant job opportunity from the available jobs retrieved in the previous step.

Key Tasks:

• Intent Understanding: The agent uses an LLM to analyze the user query and extract role requirements, desired skills, and experience levels.
• Role Matching: LLM compares the user’s requirements against all job opportunities, prioritizes exact matches by title or job family, and applies semantic similarity for near matches.
• Prioritization: If multiple roles appear relevant, the LLM prioritizes the specific match or the most recently posted job.
• Selection and Justification: The agent selects the most appropriate job opportunity or provides fallback messaging with justification if no relevant match is found.
• Data Preparation: Prepares selected job data for job description generation, including all relevant details.

Outcome:

• Relevant Job Identified: Accurately identifies the best-matching historical job role to use its description as a base for accurate JD generation.

Step 3: Automated Job Description Generation

The agent synthesizes all available data—including user input, matched job details, relevant historical job descriptions, and boilerplate (standard) details—using LLM capabilities to draft a comprehensive, tailored job description.

Key Tasks:

• Knowledge Base Access: The agent accesses a comprehensive knowledge base containing brand guidelines and legal compliance requirements to ensure that the created job descriptions are consistent with organizational standards and regulatory requirements.
• Data Enrichment: Collects and consolidates information for the selected role, including qualifications, responsibilities, and skills, and incorporates boilerplate content from previous job descriptions.
• JD Drafting: Utilizes an LLM to compose a new job description, ensuring it is structured, accurate, and aligns with the user’s requirements. Covers all required sections—organization name, department, locations, employment type, job description, employer description, responsibilities, qualifications, and skills.
• Format and Validate: Ensures the output is clearly formatted, complete, and ready for downstream workflows.

Outcome:

• Tailored JD Generated: Produces a well-structured, role-aligned job description by combining user input and previous JDs, ensuring the output meets all user requirements and current job standards.

Step 4: Continuous Improvement Through Human Feedback

To maintain high standards of quality and relevance, the agent incorporates user feedback into its job description generation workflow.

Key Tasks:

• Feedback Collection: Users review the generated job descriptions and provide feedback on clarity, relevance, alignment with stated requirements, or completeness.
• Feedback Analysis and Learning: The agent analyzes the received feedback to identify common issues, such as unclear responsibilities, missing qualifications, formatting inconsistencies, or gaps in organizational context, and improve over time.

Outcome:

• Ongoing JD Enhancement: By integrating user input, the agent continuously improves the accuracy, consistency, and quality of job descriptions, ensuring outputs remain precise and accurate.

Why Use Job Description Creation Agent?

• Accelerated JD Creation: Automates the entire job description drafting process, significantly reducing turnaround time and manual effort for HR teams.
• Contextual Accuracy: Leverages LLM capabilities to ensure job descriptions are tailored, consistent, and aligned with both user requirements and organizational standards.
• Reduced Manual Review: Minimizes repetitive editing cycles and reduces reliance on manual drafting, freeing HR teams for more strategic initiatives.
• Scalability: Supports high-volume JD creation needs, maintaining quality and consistency as hiring demands grow.
• Improved Employer Branding: Delivers consistently well-crafted, on-brand job descriptions that strengthen organizational reputation and attract top talent.
• Standardization and Compliance: Enforces uniform structure and compliance with internal policies and industry best practices across all generated job descriptions.

[image] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/employee-attrition-prediction-worker.svg [video] => [icon] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/employee-attrition-prediction-worker.svg [sourceType] => FILE [status] => REQUEST [department] => Human Resources [subDepartment] => Employee Lifecycle [process] => Recruiting and Staffing [subtitle] => Generates precise, role-aligned job descriptions by leveraging ERP data and contextual user inputs. [route] => job-description-creation-agent [addedOn] => 1748437808558 [modifiedOn] => 1748437808558 ) [2] => Array ( [_id] => 6808f3eecbd8ee0228f52745 [name] => Job Description Update Agent [description] =>

ZBrain's Job Description Update Agent automates the validation and revision of enterprise job descriptions using a Large Language Model (LLM). By integrating directly with Oracle Fusion HCM or a similar enterprise system and aligning job content with internal rule sets from a connected knowledge base, the agent ensures each job description is accurate, compliant, and ready for publishing, without the need for manual intervention. It intelligently updates only the non-compliant sections, preserving the original tone, structure, and role intent, while generating transparent summaries of applied changes.

Challenges the ZBrain Job Description Update Agent Addresses

As job roles evolve and hiring criteria shift, enterprises struggle to keep job descriptions up to date across departments. HR teams often rely on manually reviewing and editing JDs stored in systems like Oracle Fusion, which is time-consuming, inconsistent, and error-prone. Many JDs miss required skills, outdated terminology remains unchecked, and compliance guidelines are often overlooked. Existing workflows rely heavily on subject matter experts or hiring managers for validation, creating bottlenecks in the recruitment cycle. Traditional tools lack the contextual awareness to assess whether a JD meets internal standards and regulatory criteria without overwriting important content.

ZBrain Job Description Update Agent eliminates these challenges by integrating directly with Oracle Fusion HCM or a similar system to extract current job descriptions and validating them against role-specific rules sourced from a connected enterprise knowledge base. It uses an LLM to identify non-compliant or missing elements, revise only the necessary sections, and generate a complete, updated job description. The agent also provides a structured compliance checklist and a summary of applied fixes, ensuring transparency and auditability. With standardized, policy-aligned outputs ready for review and system integration, the agent helps HR teams reduce manual workloads, accelerate recruitment readiness, and scale job description governance with confidence.

How the Agent Works

ZBrain Job Description Update Agent streamlines the end-to-end process of validating and updating job descriptions by integrating Oracle Fusion data, enterprise rule sets, and LLM-powered logic. Below, we break down each step, from raw input through to final delivery, and highlight the key tasks and outcomes at every stage.

Step 1: Input Submission and Classification

The process begins when a user submits a request containing a Job Title, Opportunity Number, or Opportunity ID via an interface or webhook.

Key Tasks:

• Trigger Activation: The agent is activated through a webhook whenever an input is received.
• LLM Classification: A LLM interprets the input and classifies it into one of three categories—Job Title, Opportunity Number, or Opportunity ID.
• Routing: Based on the classification, the agent routes the request to the appropriate Oracle data retrieval path.

Outcome:

• The agent ensures the correct processing path is initiated, allowing for accurate data retrieval from Oracle Fusion regardless of input format.

Step 2: Oracle Job Data Retrieval

After the input is classified by the LLM, the agent determines the type of input provided—Opportunity ID, Opportunity Number, or Job Title, and follows a tailored Oracle API sequence to retrieve complete job data.

• Case 1: If the input is an Opportunity ID (Direct Retrieval):
  • Triggered when: The user provides a valid Oracle Opportunity ID directly.
  • Key Tasks:
    • API Call: The agent makes a direct GET request to the Oracle HCM Recruiting Opportunity API using the provided Opportunity ID.
    • Job Data Retrieval: Oracle returns a detailed JSON object containing all relevant job posting fields.
    • Custom Code Processing: A JavaScript function parses the job object and extracts structured fields such as:
      • job_title
      • description
      • responsibilities
      • qualifications
      • recruiter
      • hiring_manager
      • location
      • organization
      • requisition_number
      • publish_date
  • Outcome:
    • Complete and accurate job data retrieved instantly without additional processing.
• Case 2: If the input is an Opportunity Number (Two-step Resolution):
  • Triggered when: The user provides a job number (e.g., “50037”) but not the internal Opportunity ID.
  • Key Tasks:
    • Initial Lookup: The agent performs a POST to Oracle’s indexed job search endpoint using the Opportunity Number as the query.
    • Opportunity ID Extraction: The search results include the internal Opportunity ID corresponding to the job number.
    • Secondary API Call: A follow-up GET request is sent using this derived Opportunity ID to fetch full job details.
    • Data Normalization: As in case 1, the custom code block processes the response and extracts structured job data.
  • Outcome:
    • The Opportunity Number is first used to look up the corresponding Opportunity ID, followed by the retrieval of full job details in two steps.
• Case 3: If the input is a Job Title (Fuzzy Search & Semantic Matching):
  • Triggered when: The user submits a Job Title (e.g., “HR Analyst” or “Lead Cloud Architect”).
  • Key Tasks:
    • Indexed Search Request: A POST request is sent to Oracle’s indexed search API using the input title as the keyword.
    • Result Set Filtering: The search may return multiple results due to similarity-based scoring (e.g., a search for “Analyst” may return “Data Analyst,” “Business Analyst,” and “Analyst Supervisor”).
    • LLM Ranking: A dedicated prompt-based LLM reviews the title list and selects the best semantic match based on:
      • Exactness of match
      • Functional relevance
      • Contextual intent (e.g., "HR" vs. "IT")
    • Opportunity ID Extraction: The best-matched result’s Opportunity ID is extracted.
    • Final API Call: The ID is used to perform a GET request to fetch the full job description and related metadata.
    • Job Data Structuring: The response is parsed and normalized via the custom code block.
  • Outcome:
    • Enables flexible, user-friendly input while ensuring precise job role identification through intelligent semantic analysis.
• Case 4: If the input cannot be classified or resolved:
  • When triggered: None of the input types (ID, Number, or Title) could be confidently classified or resolved.
  • Key Tasks:
    • Validation Check: The input is re-evaluated for partial matches or recognizable patterns.
    • Exit:
      • If the agent cannot classify or resolve the input, it halts downstream execution.
      • Returns an appropriate error message.
  • Outcome:
    • Ensures that unclear or invalid inputs are caught early, reducing false processing and enabling the user to correct their request.
• Final Outcome:
  • Regardless of the input type, the agent produces a complete, structured, and validated job data object. This object is passed to the next stage for knowledge base rule matching and compliance evaluation.

Step 3: Knowledge Base Rule Retrieval

After the job data is extracted from Oracle, the agent initiates a title-based search against the enterprise knowledge base to retrieve role-specific validation rules. The way this title is obtained depends on the input type used in the earlier step.

• If the input was an Opportunity ID:
  • Key Tasks:
    • The job title is extracted directly from the Oracle job detail response using the Opportunity ID.
    • The agent queries the enterprise knowledge base with this job title.
    • The KB returns role-specific rule sets, which may include:
      • Required skills
      • Years of experience
      • Certifications or educational background
      • Organizational or regional compliance criteria
  • Outcome:
    • An accurate role-specific rule set is retrieved, with no ambiguity, as the title is directly mapped from Oracle’s official job record.
• If the input was an Opportunity Number:
  • Key Tasks:
    • The agent performs a preliminary Oracle search using the Opportunity Number to retrieve its corresponding Opportunity ID.
    • Then it performs the same Oracle job detail lookup as in case 1 using that ID.
    • The title from the Oracle response is used to query the KB.
    • A rule set specific to that title is returned.
  • Outcome:
    • This path mirrors case 1 after resolution. A clean job title from Oracle leads to accurate rule retrieval with no manual disambiguation needed.
• If the input was a Job Title:
  • Key Tasks:
    • The agent submits a fuzzy title match request to Oracle's index search endpoint.
    • Oracle returns a list of close matches ranked by semantic similarity (not exact match).
    • An LLM processes the list and selects the best match, returning:
      • Selected Title
      • Matched Opportunity ID
    • Using the Opportunity ID, the full job details are fetched from Oracle.
    • The selected title is then used to query the KB for corresponding rules.
    • If the title is ambiguous or generic, the agent uses an LLM to:
      • Filter irrelevant rule sets
      • Consolidate similar ones
      • Resolve inconsistencies
  • Outcome:
    • Despite a more uncertain input, the LLM ensures the agent retrieves the most semantically aligned title and an appropriate rule set for that role.

Step 4: LLM-based Validation and Revision

After retrieving the job data and applicable rules, the agent invokes an LLM to validate the description and revise it if needed.

Key Tasks:

• The agent packages two inputs for the LLM:
  • The structured job data from Oracle
  • The validation rule set from the Knowledge Base
• The LLM evaluates whether each field (e.g., qualifications, responsibilities, tools) satisfies the rules.
• Validation Logic:
  • If a field is compliant, it is retained without modification.
  • If a field violates a rule, the LLM revises only that field, preserving original tone, intent, and structure.

Outcome:

• A validated, policy-aligned version of the job description is generated with contextual edits applied only where needed.

Step 5: Final Processing and Delivery

Once the job description has been validated and revised by the LLM, the agent prepares and delivers the final outputs to the user.

Key Tasks:

• Formatted Output Generation:
  • The LLM returns a formatted Markdown output containing:
    • Job Validation Checklist: A tabular breakdown of each rule with compliance and fix status.
    • Summary of Fixes: Clear, human-readable explanation of what was changed and why.
    • Updated Job Record (JSON Format): A complete, restructured job description in machine-readable format.
• Output Storage:
  • The full Markdown output is stored in the agent’s runtime state using a key-value storage block. This allows it to be retrieved later in the final output stage.
• Webhook/API Response:
  • The final response is returned to:
    • The calling webhook (if triggered via API)
    • The integrated application (e.g., HR dashboard, documentation system)
    • The agent dashboard

Outcome:

• The user receives a finalized, compliance-checked job description in both human- and machine-readable formats. This can be directly published, further reviewed, or piped into downstream systems with zero manual cleanup required.

Why use Job Description Update Agent?

• Rule-aligned Content: Ensures job descriptions consistently align with organization-defined standards and evolving role requirements.
• Accuracy and Consistency: Delivers accurate, standardized, and role-aligned JDs across departments.
• Reduced Manual Effort: Minimizes HR workload by automating comparison and revision tasks that would otherwise require SME intervention.
• Faster Updates: Accelerates the process of keeping job descriptions current, especially during role changes or hiring surges.
• Audit-Ready Output: Produces a revision checklist to track compliance and provide a transparent view of content changes.
• Improved Hiring Accuracy: Enhances candidate-job fit by maintaining up-to-date, standards-aligned descriptions.
• Scalable Implementation: Can handle bulk updates across numerous roles, making it ideal for large enterprises and fast-scaling organizations.
• System Agnostic Scalability: While currently integrated with Oracle Fusion, the agent can be adapted to other job data systems with minimal change.

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Detects new employee records in the HRM system and automatically initiates onboarding tasks like sending welcome emails, scheduling orientation, and assigning training modules.

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Job Description Creation Agent

Generates precise, role-aligned job descriptions by leveraging ERP data and contextual user inputs.

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Job Description Update Agent

Enhances job descriptions for clarity, inclusivity, and localization using AI—driving better talent engagement and hiring outcomes.