{"id":27044,"date":"2026-09-23T14:28:04","date_gmt":"2026-09-23T12:28:04","guid":{"rendered":"https:\/\/www.regestaitalia.eu\/agentic-ai-in-the-workplace-use-cases-for-automation-support-and-monitoring\/"},"modified":"2026-09-23T14:28:45","modified_gmt":"2026-09-23T12:28:45","slug":"agentic-ai-in-the-workplace-use-cases-for-automation-support-and-monitoring","status":"publish","type":"post","link":"https:\/\/www.regestaitalia.eu\/en\/agentic-ai-in-the-workplace-use-cases-for-automation-support-and-monitoring\/","title":{"rendered":"Agentic AI in the Workplace: Use Cases for Automation, Support, and Monitoring"},"content":{"rendered":"

Agentic AI uses software agents to perform goal-oriented tasks, utilizing data, tools, and applications within a defined scope. In addition to autonomously automating tasks, one of its most interesting applications is compliance analysis, where it can link requirements, technical standards, and product characteristics, identify deviations, and prepare verifiable evidence to support experts\u2019 decisions.<\/strong> <\/p>\n

One of the most significant use cases involves the analysis of technical specifications and requests for proposals.<\/strong> The company must determine which products meet the specifications, which conditions require further clarification, and which differences must be disclosed. To reach this assessment, it is necessary to compare different documents, trace regulatory references, and verify catalog information.<\/strong> This is a perfect task for artificial intelligence: it requires applying a certain level of discretion to a series of document and data checks.<\/p>\n<\/div>

It was precisely with this type of process in mind that we developed Reggy Compliance Analyst<\/strong>: reviewing specifications, mapping requirements to the relevant standards, and generating a deviation list\u2014a list of discrepancies compared to available products. This area demonstrates how automation, operational support, and monitoring can be integrated into an Agentic AI project. <\/p>\n

Compliance Analysis: Reconstructing the Relationship Between Requirements and Products<\/h2>\n

In this context, compliance analysis involves verifying that the required specifications, applicable technical requirements, and documented product characteristics align. It applies to both those participating in bids and those managing requests for proposals<\/strong> for products subject to industry-specific, safety, or environmental specifications.<\/p>\n<\/div><\/div><\/div>

\"Reggy<\/span><\/div><\/div><\/div><\/div>

The specifications<\/strong> may directly specify a requirement or refer to a standard that defines its conditions, test methods, and acceptance criteria. The product data sheet<\/strong>, in turn, may contain data that applies only to certain configurations. The comparison therefore requires interpreting parameters, constraints, and relationships between documents.<\/strong><\/p>\n

For example, a performance specification stated at a certain temperature does not prove that the product will behave the same way within the range specified by the customer. Similarly, when the same standard is cited in two documents, it is necessary to verify the editions and scope of application. These differences affect the technical evaluation and the preparation of the proposal. <\/p>\n

From GenAI to Agentic AI: How Work on Specifications Is Changing<\/h2>\n

GenAI can synthesize a set of specifications, extract information, or answer questions about its content. An agent-based system links these processes to a specific, higher-level goal: completing a review, identifying missing information, or preparing a list of discrepancies to submit to the engineering department.<\/strong> <\/p>\n

For example, the agent can identify a requirement, retrieve the relevant regulation from authorized sources, consult the appropriate data sheets, and verify whether it has the necessary information. Depending on the result, it either continues the analysis or requests a human assessment.
\nFrom a technical standpoint, the main distinction concerns sequence control.<\/strong> One of the most widely adopted best practices in the industry, as suggested by Anthropic\u00b9<\/strong>, involves separating workflows\u2014which follow paths predefined by code\u2014from agents, in which the model dynamically directs the process and tools.<\/p>\n

A project can combine the two approaches, keeping the control steps fixed and assigning the agent the tasks that depend on the content of the documents.
\nThe final output is an assessment supported by evidence that allows it to be verified.<\/strong> The ability to generate a plausible text is undoubtedly a crucial step that allows artificial intelligence to best demonstrate its added value, but it is not enough to determine whether a configuration meets a specific technical requirement.<\/p>\n

Ontology and Knowledge Graph: Linking Specifications, Standards, and the Catalog<\/h2>\n

To structure the comparison, it is necessary to define what information to extract and how to relate it. An ontology describes relevant entities\u2014such as requirements, parameters, standards, and products\u2014along with the relationships that are meaningful within the business domain.
\nIn our Reggy assistant, an AI engineer defines this semantic schema.<\/strong> AI breaks down the specifications according to the ontology and populates a knowledge base in a graph database. Relationships link the requirements to the cited standards and product characteristics; a linguistic model uses this information to generate the deviation list and identify the most suitable products. <\/p>\n

The quality of the schema must be verified with specialists in the specific context. If the relationship between a performance metric and its test conditions is missing, even a correctly extracted value may be compared incorrectly. In our approach, designing the knowledge base therefore requires a collaborative effort between AI expertise and technical experience.<\/strong>
\nThe graph makes the connections between different pieces of<\/strong> information explicit and clear; however, the accuracy of the evidence produced and the relationships extracted still requires validation. <\/p>\n

When comparing numerical values, it is also advisable to base unit conversions, thresholds, and tolerances on verifiable rules. The model can identify the parameter in the document; the quantitative check must apply explicit criteria and preserve the conditions to which that value refers.<\/strong> <\/p>\n

Three Use Cases: Automation, Support, and Compliance Monitoring<\/h2>\n

The three use cases presented below demonstrate how Agentic AI can support the entire compliance analysis cycle<\/strong>: from the preliminary assessment of requests to the management of discrepancies, all the way through to the review of document updates.<\/p>\n

Automate the preliminary analysis of requests for proposals<\/h3>\n

Let\u2019s start with the first phase of a potential new project: upon receipt of an RFQ (Request for Quotation), the system can break down the specifications into requirements, identify the relevant standards, and compare the specifications with the catalog entries<\/strong>, with the goal of preparing an initial structured analysis for the engineering department.<\/p>\n

This first step reduces the amount of work required to collect and organize the information. The engineer can focus on issues that need to be addressed, such as deviations, ambiguous interpretations, and conditions that require a design or business decision.<\/strong><\/p>\n

The scope must be clearly defined by the analyst: documents reviewed, audits used, product families considered, and missing information. A quick analysis is useful when the auditor can understand its scope and recognize its limitations. <\/p>\n

Assist experts in evaluating variances<\/h3>\n

The deviation list serves as the foundation for the subsequent steps: design, quality, and sales. For each discrepancy identified, the system should allow users to trace it back to the requirement, the product characteristic, and the documentation used in the comparison.<\/strong><\/p>\n

The absence of evidence does not automatically indicate noncompliance. If a form does not include a parameter, the system must indicate that the verification is incomplete.<\/strong> An operational classification can distinguish between these situations: <\/p>\n<\/div>\n

\n\n\n\n\n\n\n\n\n
Analysis Results<\/th>\nMeaning<\/th>\nNext step<\/th>\n<\/tr>\n<\/thead>\n
Documented Correspondence<\/td>\nThe available evidence supports the requirement<\/td>\nValidate the comparison<\/td>\n<\/tr>\n
Detected deviation<\/td>\nA documented characteristic differs from the requirement<\/td>\nEvaluate an alternative or modification<\/td>\n<\/tr>\n
Insufficient evidence<\/td>\nData is missing to complete the comparison<\/td>\nRetrieve documentation or evidence<\/td>\n<\/tr>\n
Ambiguous Requirement<\/td>\nThe request is open to different interpretations<\/td>\nRequest clarification<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

This distinction helps guide the work. An alternative product, a technical modification, and a request for clarification each have different implications for the costs, timeline, and content of the proposal. The decision rests with the relevant managers. <\/p>\n

Assess the impact of document revisions<\/h2>\n

Another use case involves subsequent updates. If a specification changes or a product sheet is revised, the relevant sections of previous evaluations must be reviewed.
\nThe graph’s relationships can help identify requirements, products, and comparisons affected by the change.<\/strong> By linking document versioning and workflows, you can initiate a new review and assign it to the person in charge, while maintaining a history of the results.<\/p>\n

This extension requires the system to receive updates and manage revisions, and the system must be designed to handle them: simply replacing each document with the most recent version can alter the scope of the analysis, causing it to deviate from the initial scope.<\/p>\n

The Role of ERP, PLM, and Document Management<\/h2>\n

The analyses conducted in this way are truly useful when they use data that is consistent with the data the company uses for design, production, and sales.<\/strong> The catalog can be distributed across ERP, PLM, document management systems, and technical archives, with different parties responsible for the information.
\nThe project must identify the authoritative source for each piece of data and maintain the link between the product code, configuration, revision, and documentation. A commercial variant and a technical change may have different effects on compliance with the requirement. <\/p>\n

In an integrated architecture, the results of the analysis can be used to support verification activities, change requests, or proposal preparation. These integrations must be defined on a case-by-case basis. Authorizations must distinguish between viewing, proposing, and updating, while preserving the controls already in place within the applications. <\/p>\n

Governance: Verifiable Sources, Oversight, and Data Protection<\/h2>\n

The governance of Agentic AI must ensure that conclusions are verifiable and actions are controllable. For each outcome, references to sources, versions used, and a trace of the processing steps are required. In Reggy\u2019s architecture, the Bishop<\/a> platform can provide common services for security, identity, integration, and AI governance. The architecture includes a Neo4j-based graph<\/strong>, orchestration with the ability for the operator to review and halt processes, execution tracking, and profiled access. These functions must be translated into specific responsibilities and usage procedures. <\/p>\n

The auditor must be able to distinguish between extracted information, comparisons made, and proposed conclusions<\/strong>, correcting any errors. Even knowledge derived from human verification requires an approval process before it can be reused.
\nIn terms of implementation, another best practice suggested by OWASP\u00b2 recommends limiting tools to what is necessary, granting minimal privileges, and requiring approvals for high-impact operations.<\/strong> Authorizations must be enforced in the target systems, rather than relying solely on the model to ensure compliance. <\/p>\n

Specifications and data sheets may contain confidential information. For Reggy, we anticipate on-premises or private cloud deployments and the selection of models on a per-step basis. Data control must also include model endpoints, logs, and document copies. The deployment characteristics must therefore be verified across the entire processing workflow. <\/p>\n

Measuring the Benefits of Agentic AI in Compliance Analysis<\/h2>\n

The first indicator is the time required to complete the analysis, including human review. It should be considered alongside requirement coverage, correctly identified discrepancies, omissions, and false positives. In testing, it is particularly important to verify how many inconsistencies the system allows to pass as matches. <\/p>\n

One example of its application involvesone of our clients, a manufacturer of automotive pumps.<\/strong> Thanks to the implementation of Reggy,the company<\/strong> was able to reduce the time spent analyzing bids by 90%<\/strong> while also structuring the engineers\u2019 knowledge.<\/p>\n

Generally speaking, to launch the project, it is advisable to select use cases that have already been evaluated by experts, including complex cases, missing data, and known discrepancies. This comparison allows you to verify the quality of the ontology, the completeness of the documentation, and the reliability of the results before expanding its use.
\nThe goal is to achieve a faster, documented, and repeatable evaluation<\/strong>: experts receive organized requirements, linked evidence, and discrepancies to examine, while retaining responsibility for technical decisions and the commitments made in the proposal. <\/p>\n<\/div>

<\/div><\/div>

Schedule a consultation with our team<\/strong> to learn how to turn compliance verification into a fast, accurate, and traceable workflow.<\/p>\n<\/div>

BOOK A CALL<\/span><\/a><\/div>

\u00b9 Source: Anthropic.com<\/a>
\n\u00b2 Source: Genai.owasp.org<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":28,"featured_media":27035,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[167],"tags":[],"class_list":["post-27044","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence-ai"],"yoast_head":"\nAgentic AI: Compliance Analysis and Specifications<\/title>\n<meta name=\"description\" content=\"Agentic AI enables the analysis of specifications and the linking of requirements and technical standards, with verifiable evidence and human oversight.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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