Array ( [0] => Array ( [_id] => 6895d7058c3c4e9e9f815939 [name] => Access Governance AI Agent [description] => The Access Governance AI Agent, developed by ZBrain, is designed to help enterprises maintain secure, compliant, and efficient user access across systems. As organizations scale, user entitlements often accumulate without consistent oversight. This agent proactively monitors permissions to detect privilege drift, unused access, and misalignments between user roles and entitlements.

By examining historical access logs, the agent identifies permissions that are unused within a set period or no longer needed due to project completion or role changes. It utilizes Large Language Model (LLM) capabilities to deliver clear, contextual explanations for each flagged issue, enabling IT and security teams to assess and address risks with greater clarity and precision.

In addition to finding outdated access, the agent highlights anomalies such as department changes without corresponding updates to user privileges. These insights support more accurate access reviews, reduce exposure to unauthorized access, and ensure alignment with least-privilege principles. In doing so, the Access Governance AI Agent helps strengthen security and streamlines the process of managing user entitlements.

[image] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/access-log-analysis-agent.svg [video] => [icon] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/access-log-analysis-agent.svg [sourceType] => FILE [status] => REQUEST [department] => Information Technology [subDepartment] => Identity and Access Management [process] => Privilege Drift Detection [subtitle] => Monitors access drift and misalignments using LLMs to explain redundant privileges and streamline continuous access governance. [route] => access-governance-ai-agent [addedOn] => 1754650373758 [modifiedOn] => 1754650373758 ) [1] => Array ( [_id] => 688c125d1a9ad32c0ae3ba40 [name] => Code Assistance Agent [description] => The Code Assistance Agent is a ZBrain-developed AI agent purpose-built to support developers across the software lifecycle by providing contextual, reliable assistance for debugging, code comprehension, and implementation guidance. It serves as an intelligent technical companion that understands a wide variety of programming languages, frameworks, and runtime environments.

The agent is designed to analyze inputs such as code snippets, detailed error messages, stack traces, and natural language queries. By leveraging large language models trained on real-world code and best practices, it identifies underlying issues such as syntax errors, undefined behaviors, logic flaws, or environment-specific misconfigurations and offers actionable, step-by-step recommendations to resolve them. Beyond issue resolution, it can also help interpret complex concepts, suggest refactoring techniques, and highlight potential performance or security improvements.

By streamlining troubleshooting and accelerating root cause identification, the Code Assistance Agent helps developers stay productive, improve code quality, and focus on high-value work. It integrates easily with IDEs, documentation portals, and internal help desks to provide scalable developer support.

[image] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/ticket-escalation-alert-agent.svg [video] => [icon] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/ticket-escalation-alert-agent.svg [sourceType] => FILE [status] => REQUEST [department] => Information Technology [subDepartment] => Software Development [process] => Developer Support and Debugging [subtitle] => Provides instant, contextual guidance to help debug code, resolve errors, and improve your programming workflow. [route] => code-assistance-agent [addedOn] => 1754010205006 [modifiedOn] => 1754010205006 ) [2] => Array ( [_id] => 685947f6cfb50fc5dcad95e1 [name] => Security Questionnaire Automation Agent [description] =>

ZBrain Security Questionnaire Automation Agent empowers organizations to respond instantly and accurately to IT security questionnaires. Leveraging Large Language Models (LLMs) and a structured security knowledge base, the agent intelligently interprets, classifies, and retrieves policy-backed answers for every security query, minimizing manual workload, accelerating security assessments, and enhancing compliance with evolving security standards.

Challenges the Security Questionnaire Automation Agent Addresses

IT security teams regularly receive questionnaires from clients, partners, and auditors, each demanding detailed, domain-specific information on policies, controls, and safeguards. Manual handling involves navigating fragmented documentation and inconsistent sources, which can be slow and error-prone, leading to delays, missed requirements, and compliance risks. As security reviews grow in scale and complexity, these approaches lead to higher operational overhead, delayed stakeholder responses, and risk of audit failures and non-compliance.

ZBrain Security Questionnaire Automation Agent automates the intake, classification, and answering of security questionnaires. Using LLM-driven prompts, the agent parses each question, maps it to the relevant security domain category, and delivers structured, policy-compliant answers sourced directly from the knowledge base. This solution standardizes security knowledge, reduces manual effort, and ensures organizations provide audit-ready, compliant responses at scale, empowering security teams to operate efficiently, respond confidently to external demands, and focus on proactive risk management.

How the Agent Works

ZBrain security questionnaire automation agent is designed to automate the interpretation and delivery of accurate, policy-backed responses to security questionnaires, ensuring consistency and compliance with organizational standards. Below, we outline the detailed steps that illustrate the agent’s workflow, from initial query submission to ongoing improvement:

Step 1: User Query Intake and Pre-Processing

The workflow begins when users submit a security questionnaire through the agent dashboard or integrated enterprise platforms.

Key Tasks:

• Input Reception: The agent accepts security questionnaires and also supports the bulk upload of security questionnaires through Excel, PDF or text files.
• Parsing and Structuring: Using an LLM, the agent identifies and extracts individual questions from the input, organizing them into a structured array for downstream processing. This step handles both simple and complex questionnaires containing multiple or multipart questions.

Outcome:

• Structured Question Array: All submitted questions are extracted and organized into a structured array, ensuring they are ready for downstream processing.

Step 2: Question Classification and Fallback Routing

Each extracted question is processed individually and classified into one of the core security categories using LLM-driven prompts.

Key Tasks:

• Intent-based Classification: An LLM analyzes the semantic intent of each question, assigning it to one of ten security categories (e.g., Compliance, Data Privacy, Infrastructure).
• Specificity Prioritization: The agent prioritizes assigning each question to the most specific applicable category, even if the question appears broad. This approach ensures accurate mapping to the most relevant category and minimizes overgeneralization. For example, the question specific to Governance, Risk & Compliance (GRC) should not be assigned in the Compliance category.
• Handling of Unclassified Questions: If a question cannot be confidently mapped to a category (“Unclassified”), it is routed to a fallback step, where it is re-evaluated against all ten knowledge bases for possible alignment.

Outcome:

• Categorized or Fallback Routed Questions: Each question is either mapped to a specific security category for downstream processing or sent to fallback handling if classification remains uncertain.

Step 3: Knowledge Base Search and Answer Extraction

Classified questions are matched with curated, policy-backed answers from the structured knowledge base, with the answer extraction process guided by confidence scoring.

Key Tasks:

• Targeted Category-based Search: For each classified question, the agent queries the matched category knowledge base, extracting the most relevant answer using a comprehensive, context-aware LLM prompt. Only direct matches or semantically complete responses are considered valid.
• Confidence Scoring and Branching: Each extracted answer is scored for confidence (High, Medium, Low) based on completeness and semantic fit.
  • High/Medium Confidence: If a clear, context-matched answer is found, it is selected and formatted for output.
  • Low Confidence: If no valid or only partial information is found, the workflow routes the question to a re-evaluation process.
• Cross-category Review for Low Confidence: For low-confidence results, the agent searches across all knowledge bases using a detailed prompt, attempting to extract a compliant answer from any relevant category. If the query remains unresolved, a fallback notification is issued.
• Multipart Question Handling: For compound questions, the agent ensures that each sub-part is addressed individually, providing a comprehensive and organized response.
• Strict Context Enforcement: The LLM is constrained to use only the provided knowledge base content without any type of summarization or external assumptions. Every answer must include a justification.

Outcome:

• Policy-backed Answers or Fallback Notifications: Each question receives a policy-backed answer with justification and confidence score or a fallback notification if no valid answer exists.

Step 4: Structured Response Generation and Output Formatting

The agent compiles each answer into an audit-compliant output for user review or export.

Key Tasks:

• Answer Formatting: The LLM formats each response to include the original question, the answer, answer present fields (Yes/No), the classified category, the confidence score (High/Medium/Low), and a clear justification for both category and answer selection.
• Consistent Output Standards: Ensures every response adheres to plain-text, structured formatting, optimized for dashboards and direct customer sharing.
• Fallback Messaging: If no answer is available, the agent provides a standardized SME escalation response. This output includes the original question, category, confidence score, answer present field (No), a clear fallback message, and a justification that specifies why the knowledge base could not support the response.

Outcome:

• Structured Response Generation: Users receive well-structured, compliant answer sets with mandatory fields, all prepared for immediate use in security communications and reporting.

Step 5: Continuous Improvement through User Feedback

A feedback mechanism collects user input on answer quality and clarity to drive ongoing agent refinement.

Key Tasks:

• Feedback Collection: Users evaluate each response for clarity, accuracy, and relevance, providing direct feedback through the agent dashboard.
• Feedback Analysis: The agent systematically reviews feedback to identify recurring issues, gaps in knowledge base coverage, or opportunities for refining prompts and output standards.

Outcome:

• Ongoing Enhancement: User input drives ongoing improvements to answer quality, knowledge base completeness, and overall alignment with organizational security requirements.

Why use Security Questionnaire Automation Agent?

• Accelerated Questionnaire Response: Automates the intake, classification, and answering of security questionnaires, reducing manual effort and speeding up response cycles.
• Increased Operational Efficiency: Eliminates time-consuming manual searches across fragmented documentation, freeing IT security teams to focus on higher-value tasks.
• Improved Stakeholder Trust: Clear, well-structured, and transparent answers build confidence with external auditors, customers, and partners, strengthening business relationships.
• Enhanced Audit Readiness: Delivers consistent, traceable responses that simplify audits and ensure readiness for assessments, certifications and regulatory reviews.
• Reduced Risk Exposure: Minimizes the risk of errors, omissions, and non-compliance in questionnaires, strengthening security posture and reducing penalties.
• Seamless Scalability: Easily manages growing questionnaire demands ensuring consistent performance even during peak periods and organizational growth.

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Each draft includes essential elements such as execution steps, risk considerations, testing protocols, and rollback procedures. This structured outline acts as a starting point for IT teams and change advisory boards, allowing them to review and refine the plan while maintaining alignment with internal compliance and quality standards. The agent’s use of contextual references ensures consistency across change records and helps preserve institutional knowledge.

By reducing the manual burden of drafting plans from scratch, the agent enables faster change cycles, minimizes planning errors, and improves coordination across stakeholders. It supports ITSM workflows by delivering ready-to-review plans directly into collaboration tools or service management platforms—bringing clarity, speed, and rigor to change implementation efforts.

[image] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/contract-template-suggestion-agent.svg [video] => [icon] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/contract-template-suggestion-agent.svg [sourceType] => FILE [status] => REQUEST [department] => Information Technology [subDepartment] => IT Operations [process] => Change Request Planning [subtitle] => Generates initial implementation and testing plans for change requests by analyzing request details and referencing past changes. [route] => change-plan-drafting-agent [addedOn] => 1748859916605 [modifiedOn] => 1748859916605 ) [4] => Array ( [_id] => 68304be9792b893ca2033097 [name] => Contextual Triage Agent [description] => Contextual Triage Agent is an AI-powered solution from ZBrain built to accelerate and enhance the triage phase of incident management. In fast-paced operational environments, the ability to assess and prioritize incidents quickly is often hindered by disconnected data sources and time-consuming context gathering. This agent solves that challenge by automatically compiling relevant system insights at the moment an incident or service request is raised. It centralizes critical diagnostic inputs—such as performance metrics, recent system events, and historical changes—into a structured summary, attached to the ticket, enabling informed decision-making from the outset.

Technically, the agent uses intelligent retrieval logic to collect and correlate data points from relevant observability and change-tracking systems. Once gathered, the information is synthesized into a readable format that aligns with the incident type, helping ensure consistency in how triage information is presented. The structured summaries are dynamically mapped to service tickets, establishing immediate visibility into potential root causes, affected components, or patterns—streamlining the handoff between support tiers.

By delivering real-time, contextual insight during incident intake, the Contextual Triage Agent reduces time-to-diagnosis, supports faster resolution workflows, and helps maintain compliance with service-level objectives. It also improves incident documentation quality, enabling better retrospectives and operational learning. For organizations looking to scale support operations without compromising speed or accuracy, this agent becomes essential for proactive and efficient incident response.

[image] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/feedback-collection-agent.svg [video] => [icon] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/feedback-collection-agent.svg [sourceType] => FILE [status] => REQUEST [department] => Information Technology [subDepartment] => IT Operations [process] => Incident Management [subtitle] => Automatically collects and consolidates contextual information from logs or monitoring tools to enrich incident or request tickets, accelerating root cause analysis and resolution. [route] => contextual-triage-agent [addedOn] => 1747995625068 [modifiedOn] => 1747995625068 ) [5] => Array ( [_id] => 6821a994d1c2cbd4c898c824 [name] => License Audit and Optimization Agent [description] => License Audit and Optimization Agent, developed by ZBrain, is an intelligent enterprise solution designed to ensure that software license investments align with actual business usage. As organizations scale and adopt a diverse range of software across departments, license sprawl and inefficiencies become increasingly common. This agent provides centralized visibility into license utilization, aggregating usage data across systems and functions to identify inactive, underutilized, or misallocated licenses. It empowers IT, procurement, and finance teams with real-time, actionable insights to drive informed decisions around software spend and compliance.

Technically, the agent integrates with both structured APIs and unstructured data sources such as manual usage reports or departmental logs to build a consolidated license usage profile. It performs automated audits by comparing active entitlements against real usage patterns, user roles, and access frequency. The system flags discrepancies—such as users with premium licenses but low activity—and correlates them with organizational structures to recommend optimization strategies. These may include license downgrades, reallocations, removals, or consolidations. Built-in compliance logic also verifies adherence to vendor licensing terms, helping prevent audit risks and overage penalties.

With continuous monitoring and a human-in-the-loop feedback loop, the agent adapts its recommendations based on organizational priorities and evolving usage behavior. IT and procurement teams can review suggestions, adjust thresholds, and feed outcomes back into the system for ongoing refinement. The benefits are significant: reduced software costs, improved license hygiene, and more strategic vendor management. By ensuring that software entitlements reflect real business needs, the License Audit and Optimization Agent becomes a key component of any enterprise’s digital cost optimization and governance strategy.

[image] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/payroll-audit-compliance-agent.svg [icon] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/payroll-audit-compliance-agent.svg [sourceType] => FILE [status] => REQUEST [department] => Information Technology [subDepartment] => Software Asset Management [process] => License Management [subtitle] => The License Audit and Optimization Agent scans software usage data to identify underused licenses and recommends cost-saving actions like downgrades or removals, optimizing license allocation and reducing costs. [route] => license-audit-and-optimization-agent [addedOn] => 1747036564681 [modifiedOn] => 1747036564681 ) [6] => Array ( [_id] => 671662343ff0eb0024d9bdbf [name] => SLA Compliance Monitoring Agent [description] => The SLA Compliance Monitoring Agent automates the task of monitoring service levels to ensure compliance with contractual SLAs. Using GenAI, this agent tracks key performance indicators such as system uptime, response times, and service delivery metrics across all IT operations. It compares these against SLA commitments and generates real-time alerts when performance falls below agreed-upon thresholds. By automating SLA compliance monitoring, this agent ensures that the IT department meets its performance obligations and can take proactive steps to address potential service failures. This agent provides high ROI by improving service delivery, reducing the risk of SLA breaches, and ensuring that contractual obligations are consistently met.
Seamlessly integrating with existing IT systems, the agent collects data from multiple sources for a comprehensive view of service performance. This integration enhances its ability to deliver accurate and timely insights, facilitating smoother workflows across departments. Additionally, the agent includes a human feedback loop, enabling IT teams to provide valuable insights and adjustments based on real-world experience. This continuous feedback allows the agent to learn and adapt, improving its monitoring capabilities and ensuring it remains aligned with evolving service level expectations. [image] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/sla-compliance-monitoring-agent.svg [icon] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/sla-compliance-monitoring-agent.svg [sourceType] => FILE [status] => REQUEST [department] => Information Technology [subDepartment] => IT Operations [process] => Service Level Agreement Monitoring [subtitle] => Automates the monitoring of Service Level Agreements (SLAs), ensuring that IT services meet agreed-upon performance metrics and alerting teams when SLAs are breached. [route] => sla-compliance-monitoring-agent [addedOn] => 1729520180648 [modifiedOn] => 1729520180649 ) [7] => Array ( [_id] => 6716622e3ff0eb0024d9bdb5 [name] => Code Documentation Generator Agent [description] => The Code Documentation Generator Agent automates the process of generating detailed and accurate documentation for software codebases. Using GenAI, this agent analyzes the source code and generates documentation, including descriptions of functions, classes, and modules. It ensures that all code is well-documented, making it easier for developers to understand and maintain the software. By automating documentation generation, this agent reduces the manual effort required by developers and ensures that documentation remains up-to-date with code changes. This improves code maintainability and reduces the time spent on manual documentation tasks, ultimately enhancing the overall quality and efficiency of the software development lifecycle.

The agent seamlessly integrates with popular development tools and platforms, ensuring that it works within the existing workflows of development teams. This integration ensures that documentation generation occurs automatically during code updates, reducing manual intervention and maintaining consistency. Additionally, the agent incorporates a human feedback loop, allowing developers to review and refine the generated documentation. This feature ensures that the documentation remains highly relevant and tailored to the specific needs of the project, enabling continuous improvement in documentation quality and accuracy.

[image] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/code-documentation-generator-agent.svg [icon] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/code-documentation-generator-agent.svg [sourceType] => FILE [status] => REQUEST [department] => Information Technology [subDepartment] => Software Development [process] => Code Documentation [subtitle] => Automatically generates detailed code documentation from the source code, ensuring that developers have access to accurate and up-to-date documentation. [route] => code-documentation-generator-agent [addedOn] => 1729520174796 [modifiedOn] => 1729520174796 ) [8] => Array ( [_id] => 671662293ff0eb0024d9bdb0 [name] => Network Downtime Alert Agent [description] => The Network Downtime Alert Agent automates continuous monitoring of network performance, instantly detecting any downtime or degradation. Leveraging GenAI, the agent tracks essential network metrics such as bandwidth, latency, and uptime and generates real-time alerts when performance drops below acceptable thresholds. This enables the IT team to swiftly investigate and resolve issues, minimizing disruptions. By automating the monitoring process, the agent significantly reduces response times, ensuring a quicker resolution and improved network reliability. The result is reduced downtime, optimized performance, and faster issue resolution, ultimately enhancing overall IT efficiency.

Seamlessly integrating with existing IT infrastructure and monitoring systems, the Network Downtime Alert Agent aligns effortlessly with an organization’s workflows. By automating the generation of alerts during network issues, it reduces the need for manual oversight and ensures swift notification of potential problems. Additionally, the agent features a human feedback loop that allows IT teams to customize alert thresholds, refine response strategies, and enhance accuracy over time. This iterative feedback process ensures the agent adapts to evolving network requirements, delivering improved performance and responsiveness to real-time challenges.

[image] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/network-downtime-alert-agent.svg [icon] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/network-downtime-alert-agent.svg [sourceType] => FILE [status] => REQUEST [department] => Information Technology [subDepartment] => IT Operations [process] => Network Monitoring [subtitle] => Monitors network performance and automatically sends alerts when downtime or performance degradation is detected. [route] => network-downtime-alert-agent [addedOn] => 1729520169855 [modifiedOn] => 1729520169855 ) [9] => Array ( [_id] => 671662243ff0eb0024d9bdab [name] => Ticket Escalation Recommendation Agent [description] => The Ticket Escalation Recommendation Agent automates the process of determining when and to whom a help desk ticket should be escalated. Using GenAI, this agent analyzes the severity, complexity, and urgency of incoming support tickets, making real-time recommendations for escalating issues to the appropriate teams.

This ensures that high-priority problems are addressed promptly by the right level of expertise. By automating ticket escalation, this agent reduces the risk of delayed resolutions and ensures that critical issues are handled efficiently. This agent provides high ROI by improving ticket response times, ensuring proper resource allocation, and enhancing the efficiency of the IT support workflow.

[image] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/ticket-escalation-recommendation-agent.svg [icon] => https://d3tfuasmf2hsy5.cloudfront.net/assets/worker-templates/ticket-escalation-recommendation-agent.svg [sourceType] => FILE [status] => REQUEST [department] => Information Technology [subDepartment] => IT Support [process] => Ticket Escalation Management [subtitle] => Analyzes ticket severity and urgency, automatically recommending escalation paths to ensure that high-priority issues are handled by the appropriate teams. [route] => ticket-escalation-recommendation-agent [addedOn] => 1729520164605 [modifiedOn] => 1729520164605 ) )

Information Technology

Access Governance AI Agent

Monitors access drift and misalignments using LLMs to explain redundant privileges and streamline continuous access governance.

Information Technology

Code Assistance Agent

Provides instant, contextual guidance to help debug code, resolve errors, and improve your programming workflow.

Information Technology

Security Questionnaire Automation Agent

Automates security questionnaire answers using LLMs and a structured knowledge base for faster, consistent, and reliable responses.

Information Technology

Change Plan Drafting Agent

Generates initial implementation and testing plans for change requests by analyzing request details and referencing past changes.

Information Technology

Contextual Triage Agent

Automatically collects and consolidates contextual information from logs or monitoring tools to enrich incident or request tickets, accelerating root cause analysis and resolution.

Information Technology

License Audit and Optimization Agent

The License Audit and Optimization Agent scans software usage data to identify underused licenses and recommends cost-saving actions like downgrades or removals, optimizing license allocation and reducing costs.

Information Technology

SLA Compliance Monitoring Agent

Automates the monitoring of Service Level Agreements (SLAs), ensuring that IT services meet agreed-upon performance metrics and alerting teams when SLAs are breached.

Information Technology

Code Documentation Generator Agent

Automatically generates detailed code documentation from the source code, ensuring that developers have access to accurate and up-to-date documentation.

Information Technology

Network Downtime Alert Agent

Monitors network performance and automatically sends alerts when downtime or performance degradation is detected.

Information Technology

Ticket Escalation Recommendation Agent

Analyzes ticket severity and urgency, automatically recommending escalation paths to ensure that high-priority issues are handled by the appropriate teams.