Skip to main content

Multilingual Issue Intake & Orchestration Agent

A multilingual Copilot Studio agent that structures technical reports submitted by text or speech and activates controlled Power Automate workflows.

At a glance

Project type
Internal tool
Year
2026
Project stage
Completed
Role of 5A Technologies
Expertise now brought together within 5A Technologies, gained during a previous professional role: end-to-end architecture, Copilot Studio development, multilingual intake, Power Automate orchestration, validation and error handling.
Technologies
  • Microsoft Copilot Studio
  • Microsoft 365 Copilot
  • Microsoft Teams
  • Microsoft Power Automate
  • JSON
Multilingual issue intake — controlled agent orchestrationHover or focus for details. On mobile, tap a phase to open it.

Eight-phase workflow: report a problem, capture the report, constrain relevant context, prepare known values, structure the issue with bounded AI, validate and execute, review the outcome, and return the result. 1. Report problem: Text or speech in the user's language 2. Capture report: Text or speech is captured in a usable form 3. Constrain context: Only relevant and confirmed context remains 4. Prepare context: Known values proceed through a controlled handoff 5. Structure issue: Fixed JSON without invented fields 6. Validate and execute: Power Automate checks and starts the allowed action 7. Review outcome: An employee or team follows up results and exceptions 8. Return result: Outcome and reference return to the employee Connections: Report problem to Capture report (free-form report). Capture report to Constrain context (captured input). Constrain context to Prepare context (known context). Prepare context to Structure issue (bounded context). Structure issue to Validate and execute (fixed contract). Validate and execute to Review outcome (workflow outcome). Review outcome to Return result (human follow-up). Validate and execute to Review outcome (uncertainty or missing information).

Multilingual issue intake · process flow8 phases · human follow-up
  1. Report problemText or speech in the user's language
    Human input and resultThe employee describes the problem in their own words

    Intake starts through text or speech in a supported user environment. The employee does not need to know the technical ticket structure or canonical application name in advance.

  2. Capture reportText or speech is captured in a usable form
    Deterministic processingThe original report remains recognizable

    The free-form report is captured through a supported user environment. The employee does not need to know the technical ticket structure or canonical application name in advance.

  3. Constrain contextOnly relevant and confirmed context remains
    Deterministic processingManaged sources constrain the usable context

    A glossary and application registry map terms to the correct application and environment. Unconfirmed values remain outside the structured issue context.

  4. Prepare contextKnown values proceed through a controlled handoff
    Deterministic processingOnly bounded context reaches the AI step

    The confirmed application, environment and issue values are passed on as controlled context. Unknown information remains empty and is not invented.

  5. Structure issueFixed JSON without invented fields
    Bounded AI interpretationFree-form language becomes one explicit data contract

    The agent fills a predefined JSON structure with the known issue context. That structure creates a reviewable boundary between generative interpretation and execution.

  6. Validate and executePower Automate checks and starts the allowed action
    Deterministic processingA valid contract activates one known route

    Power Automate checks the required fields and executes only a preconfigured action or a safe error path.

  7. Review outcomeAn employee or team follows up results and exceptions
    Human follow-upHuman follow-up remains part of the business flow

    Operational teams retain their normal review, prioritization and follow-up. Missing context or an error path remains visible and can be completed in a targeted way.

  8. Return resultOutcome and reference return to the employee
    Human input and resultThe result returns to the same conversation

    The employee receives the outcome and, where applicable, a ticket reference. The response remains part of the same conversation.

Human input and resultDeterministic processingBounded AI interpretationHuman follow-upFree text or speech becomes a controlled data contract and a bounded workflow outcome; uncertain context remains available for human follow-up.

Uncertain or missing information remains visible for human follow-up.

In brief

This custom agent was developed during a previous professional role at Xerius. At 5A Technologies, we build on the resulting expertise in multilingual intake and controlled orchestration. The agent enabled employees to report technical problems through text or speech. It understood reports in multiple languages, identified the correct application and environment, accounted for internal business terminology and converted everything into a fixed, structured issue description.

Based on that description, the agent automatically started the appropriate Power Automate workflow. It could, for example, retrieve relevant knowledge, find existing work items, prepare a bug or incident and carry out the appropriate follow-up through Teams or email.

  • Context: multilingual technical issue intake across a large application landscape
  • Project: internal tool delivered during a previous professional role
  • Status: completed and technically handed over
  • Role of 5A Technologies: carrying forward expertise in end-to-end architecture, Copilot Studio development, orchestration, validation and error handling
  • Core principles: managed business context, fixed output structures and bounded follow-up actions

Important design principle

Free-form language makes intake accessible, but it does not become an executable action directly. The agent first converts the report into a fixed data contract; missing or uncertain context remains visible for further questions or human follow-up.

The challenge

Within a large application landscape, the same application can have several names, URLs and environments. Employees also describe problems in their own way and not always in the same language.

As a result, reports were often:

  • incomplete or difficult to interpret;
  • not immediately linked to the correct application;
  • dependent on internal terminology;
  • manually converted into bugs or incidents;
  • disconnected from existing documentation and work items.

The solution

The agent combined three elements:

  1. Copilot Studio for conversational intake and interpretation;
  2. managed knowledge sources for business concepts, applications, URLs and environments;
  3. Power Automate for executing specific follow-up actions.

Conversational intake

Copilot Studio processed reports submitted through text or speech, identified the language and intent and asked targeted follow-up questions when information was missing.

Managed business context

A business glossary and application registry mapped internal terms, alternative names, URLs and environments to the correct context.

Controlled orchestration

The AI layer structured free-form language into a predefined contract. Power Automate used that contract to execute only configured follow-up actions in a controlled manner and according to fixed rules.

How the workflow worked

  1. The user described the problem through text or speech.
  2. The agent identified the language, intent, application and environment.
  3. Internal terms and alternative names were mapped to the correct business context.
  4. Missing information was completed through targeted follow-up questions.
  5. The agent produced a predefined JSON structure.
  6. Power Automate selected and started the appropriate workflow.
  7. The result and any ticket reference were returned to the user.

Structured output

The agent translated free-form input into one fixed data contract that every workflow could use.

{
  "issueType": "bug",
  "application": "canonical-application-name",
  "environment": "acceptance",
  "summary": "Short description of the problem",
  "observedBehaviour": "What the user experiences",
  "businessImpact": "Reported impact",
  "recommendedAction": "create_bug"
}

Fields that could not be derived reliably were left empty or clarified through another question first. The agent did not invent a technical cause, priority or owner.

Possible follow-up actions

Depending on the report, the solution could:

  • retrieve information from the internal wiki;
  • find related work items;
  • prepare or create a new bug ticket;
  • register an incident;
  • send a Teams message;
  • send an email;
  • start an additional follow-up action;
  • return the result in the same conversation.

User channels

The agent could be made available through:

  • Microsoft 365 Copilot;
  • Microsoft Teams as a chatbot or within a channel;
  • a separate agent interface.

This allowed employees to use the solution from the tools in which they already worked.

Technical building blocks

  • Conversational agent — Microsoft Copilot Studio: intake, interpretation and follow-up questions
  • User channels — Microsoft 365 Copilot and Teams: text- and speech-driven access
  • Business context — glossary and application registry: normalize terms, applications, URLs and environments
  • Structured handoff — predefined JSON contract: reliable boundary between AI and workflows
  • Orchestration — Microsoft Power Automate: routing, ticketing, retrieval and communication
  • External sources — wiki and work-item platform: retrieve existing knowledge and related issues

No separate custom language model was trained. The solution used the generative capabilities of Copilot Studio within clearly defined knowledge sources, fixed output structures and bounded actions.

Expertise we bring forward

At 5A Technologies, we build on experience across:

  • designing the end-to-end architecture;
  • building and configuring the Copilot Studio agent;
  • developing the multilingual issue-intake process;
  • linking the business glossary and application mapping;
  • designing the JSON contract;
  • building the Power Automate workflows;
  • integrating ticketing, the wiki, Teams and email;
  • defining validation, error handling and action boundaries.

Outcome

The solution brought several reporting and follow-up steps together in one accessible workflow. Employees did not need to know the technical ticket structure, canonical application name or internal routing themselves. The agent gathered and structured the context, while Power Automate executed the appropriate business action.

The project demonstrates how AI interpretation and conventional workflow automation can form a practical system together: natural language at the front, controlled and reproducible actions at the back.

What this project demonstrates

This project shows how 5A Technologies:

  • converts text and speech into one fixed data contract;
  • connects multilingual input to managed business and application context;
  • separates AI interpretation from controlled workflow execution;
  • keeps missing or uncertain information visible for further follow-up;
  • brings knowledge, ticketing and communication together without loosening action boundaries.

Would you like to discuss a similar solution?

Tell us where your process slows down today or where AI and automation should work together more effectively. We will explore which controlled approach fits.

Discuss your process