Una de las solicitudes más comunes a nuestros amigos en la gestión de talentos se les pide que hagan es ayudar a entrenar a alguien a través de una conversación difícil o para mejorar sus habilidades como líder.Aunque esto a menudo es una de las partes más agradables del trabajo, puede ser desafiante proporcionar el tipo de entrenamiento que necesita un gerente o empleado sin el conjunto completo de datos. Afortunadamente, aquí es donde el SDK de Talent Agent y la intersección de HR e IT realmente brilla. Precondiciones Vamos a dejar que el SDK del agente haga la mayor parte del levantamiento pesado aquí y sacar el agente HRBP y el agente de analista de recursos humanos para ayudarnos. En primer lugar, contacta con Peoplelogic.dev para asegurar una caja de arena para el SDK de Agente A continuación, clone el agente SDK de inicio de https://github.com/peoplelogic/agent-sdk-starter Luego, activemos el HRBP y el Analista de Recursos Humanos dentro de src/main/resources/application.properties: peoplelogic.agent.HRBusinessPartnerAgent.enabled=true
peoplelogic.agent.HRAnalystAgent.enabled=true
 Crea tu herramienta de coaching Para añadir la funcionalidad de entrenamiento al agente HRBP, necesitamos crear una nueva herramienta. Esto se debe hacer a través de una nueva También vamos a cables en varios de los otros componentes de SDK que necesitaremos trabajar con. Inicio Guía @PeoplelogicTool @PeoplelogicTools("hrbp-coaching-tools")
public class HRBPCoachingTools {
    @Autowired
    PersonalContentRetriever personalContentRetriever;

    @Autowired
    @Qualifier("apmContentRetriever")
    ContentRetriever apmContentRetriever;

    @Autowired
    CustomerKnowledgeContentRetriever customerKnowledgeContentRetriever;

    @Autowired
    ChatMemoryProvider chatMemoryProvider;

    @Autowired
    ChatModel chatLanguageModel;

    @Autowired
    HRAnalystAgent analyst;

    HRBusinessPartnerAgent agentWithFiles;
}
 Hasta ahora, tan bueno - hemos introducido componentes que nos ayudan a recoger datos de la organización y también enviados a través de la conversación y hemos introducido algunos componentes básicos como el modelo de chat y la memoria para que podamos configurar algunas sobrecargas de nuestros agentes. La definición: agentWithFiles private final String COACHING_CONVERSATION_SEGMENT = "You are providing structured coaching guidance for managers based on company-provided training materials, performance documentation, values, employee history, and engagement insights. " +
            "Your goal is to help managers facilitate productive, constructive, and values-driven coaching conversations with their direct reports. " +  "Use the company handbook, leadership principles, performance documentation (including APM), and engagement trends from PersonalContentRetriever to guide best practices." +
            "Do not just provide generic coaching advice - prompt the user for additional information through the questions below to tailor the advice specifically for the users questions. " +
            "\n\nHere are the steps to take and the details to provide:\n" +
            "**1) Understand the Context:**\\n" +
            "   * Identify the employee’s role, responsibilities, and recent performance trends.\\n" +
            "   * Reference recent performance reviews, feedback, and IDP goals to align coaching with personal development areas.\\n" +
            "   * Retrieve relevant company values, leadership principles, or training materials to inform the conversation.\\n" +
            "   * Consider engagement data: Has the employee reported lower motivation? Are they flagged for burnout risk? Have survey responses indicated disengagement or stress?\\n" +
            "   * Review prior coaching conversations for continuity.\\n" +
            "\\n **2) Prepare a Coaching Plan:**\\n" +
            "   * Summarize key strengths and recent achievements of the employee.\\n" +
            "   * Highlight areas for growth, focusing on skill development, leadership behaviors, or goal alignment.\\n" +
            "   * Identify any performance concerns and prepare structured, constructive feedback.\\n" +
            "   * Suggest conversation starters that align with company coaching frameworks (e.g., open-ended questions, values-based feedback, and action-oriented dialogue).\\n" +
            "\\n **3) Incorporate Engagement Insights:**\\n" +
            "   * If recent engagement data is available, summarize key findings.\\n" +
            "   * Highlight any early warning signs of burnout, stress, or disengagement.\\n" +
            "   * Recommend specific leadership actions to address motivation concerns, such as increased recognition, rebalancing workload, or providing career growth opportunities.\\n" +
            "   * If the employee has high engagement scores, suggest ways to keep momentum going (e.g., stretch assignments, mentorship roles).\\n" +
            "\\n **3) Handling Specific Coaching Scenarios:**\\n" +
            "   **Scenario: Coaching an Underperforming Employee**\\n" +
            "   * Identify the specific performance gaps based on reviews, OKRs, and manager feedback.\\n" +
            "   * Provide a script for addressing performance concerns using a structured approach (e.g., SBI: Situation-Behavior-Impact).\\n" +
            "   * Recommend follow-up actions, such as skills training, mentorship, or reassigning work to better match strengths.\\n" +
            "   **Scenario: High-Potential Coaching & Career Growth**\\n" +
            "   * Recognize recent achievements and set expectations for leadership development.\\n" +
            "   * Suggest stretch assignments, cross-functional projects, or learning opportunities.\\n" +
            "   * Align growth plans with company goals to maximize impact.\\n" +
            "   **Scenario: Addressing Low Engagement or Burnout Risk**\\n" +
            "   * Review recent engagement data to understand concerns (e.g., workload, manager relationships, career stagnation).\\n" +
            "   * Provide conversation starters to uncover underlying challenges.\\n" +
            "   * Suggest leadership interventions (e.g., workload adjustments, increased flexibility, career pathing discussions).\\n" +
            "   **Scenario: Giving Feedback Based on Company Values**\\n" +
            "   * Retrieve company values and leadership principles from the PersonalContentRetriever.\\n" +
            "   * Structure feedback using real-world examples that reinforce those values.\\n" +
            "   * Recommend actions that align with cultural expectations (e.g., collaboration, innovation, customer focus).\\n" +
            "\\n **4) Set Next Steps:**\\n" +
            "   * Summarize key takeaways from the conversation.\\n" +
            "   * Provide actionable follow-ups, such as scheduling a check-in, setting measurable goals, or recommending additional support.\\n" +
            "   * If needed, generate a follow-up message or email summarizing key points for the employee.\\n" +
            "\\n **5) Generate Additional Prompts for the User:**\\n" +
            "   * 'How can I coach Alice on improving leadership skills based on her performance review?'\\n" +
            "   * 'What feedback should I give Bob in our 1:1 based on company values?'\\n" +
            "   * 'How can I address engagement concerns with my direct report in a coaching session?'\\n" +
            "   * 'Give me a script for a coaching conversation based on Charlie’s recent performance trends.'\\n" +
            "   * 'How can I coach Derek to take on a more strategic leadership role in the company?'\\n" +
            "   * 'What actions should I take if an employee’s engagement scores have dropped?'";
 Muy rápido, pero nadie dijo nunca que el entrenamiento era fácil! Con eso de la manera que solo necesitamos ajustar el agente HRBP un poco. Puedes ver que hemos autowied en el HRAnalystAgent con su configuración predeterminada - eso es porque queremos llamarlo con todas sus herramientas disponibles. Ahora, necesitamos ajustar el HRBP para que cuando lo llamamos desde dentro de nuestra herramienta que no utiliza ninguna otra herramienta - sólo el conocimiento que le estamos dando. private HRBusinessPartnerAgent getAgentWithPersonalFiles() {
        if (agentWithFiles == null) {
            agentWithFiles = AiServices.builder(HRBusinessPartnerAgent.class)
                    .retrievalAugmentor(PeoplelogicRetrievalAugmentor.builder()
                            .queryTransformer(ExpandingQueryTransformer.builder().chatModel(chatLanguageModel).build())
                            .queryRouter(new DefaultQueryRouter(apmContentRetriever, personalContentRetriever, customerKnowledgeContentRetriever))
                            .build())
                    .chatMemoryProvider(chatMemoryProvider)
                    .chatModel(chatLanguageModel)
                    .tools(Collections.emptyList()).build();
        }
        
        return agentWithFiles;
}
 Con eso fuera del camino, podemos llegar al trabajo real - construyamos nuestra herramienta. Construir el entrenador Para construir el entrenador real, necesitaremos crear un método que podamos tener la llamada SDK cuando quiere ayudar a alguien con el coaching de liderazgo. @Tool("Provides an interactive coach or roleplay scenario to help HR and managers have a coaching conversation with an employee. " +
        "You need to ask who the employee is.  If no specific topic provided after prompting, just assume general performance coaching." +
        "You should ask if there is any additional information the user wants to provide (surveys, okrs, etc) relevant to the conversation. " +
        "You can also ask for the employee's job description (optional) just so we know the roles and responsibilities." +
        "If additional details are not provided and job description is not provided, you may ask the user if they'd like the information retrieved from connected tools." +
        "This tool will always output the full coaching plan or the parts that have been compiled so far.")
@SneakyThrows
public String roleplayOrProvideCoachingAdviceHelpMeCoachAnEmployee(@ToolMemoryId String memoryId, @P("Employee Name") String employeeName, 
        @P("Specific topic for coaching") String specificTopic,
        @P(value = "Filenames of additional inputs - excluding job descriptions", required = false) String additionalFiles,
        @P(value = "Employee's Job Description", required = false) String jobDescription) {
}
 Como puede ver, estamos dando a la herramienta tanto una descripción muy detallada Y un nombre detallado para que el LLM tenga la mayor cantidad de información posible para llamar a nuestra herramienta. Una versión más avanzada de esta herramienta podría optar por usar la API Universal Talent para recoger todos estos detalles e incluirlos en la respuesta sin la necesidad de cargarlos. Ahora echemos un vistazo a cómo podríamos utilizar toda esta información: String userQuery = "Help me have a coaching conversation with " + employeeName + " around " + specificTopic + ". " +
        "If additional files are provided, try to use those. It should be an interactive conversation. " +
        "At the end, always provide me with the additional questions I may ask you to continue the conversation or suggest some scenarios to roleplay based on the information provided and the specific topic.";

// Setup the reviews and surveys processing through the HR Analyst and wait for files to be uploaded
if (additionalFiles != null && !additionalFiles.isEmpty()) {
    additionalFiles = getFilenamesAsJoinedString(additionalFiles);

    String analysisQuery = "analyze these okr cycles, engagement results and review results located in files '" + additionalFiles + "'. All files have been provided already - do not ask for them again.  Everything is confirmed - no more required.";
    SearchFileContext.setCurrentFiles(additionalFiles.split(","));
    if (!waitForUpload(analysisQuery, personalContentRetriever)) {
        return "There was a problem uploading the files to analyze.  Please try again.";
    }

    // Call the HR Analyst and append the results to the user query
    Result<PeoplelogicResult> analystResults = analyst.acceptWork(memoryId + "_coaching", analysisQuery, "");
    userQuery = userQuery + "\n\n" + analystResults.content().getResponse();
}
 Primero, vamos a configurar la instrucción básica para ir con nuestro mensaje anterior del sistema - recuerda, cada llamada a los agentes necesita las dos! Luego, necesitamos hacer algo de limpieza de las entradas adicionales y asegurarnos de que las hayan sido realmente cargadas. Finalmente, vamos a llamar al Analista de Recursos Humanos y dejar que trabaje su magia analizando cualquier revisión, encuesta o OKRs e incluirlas en lo que queremos pasar a la herramienta de entrenamiento. // Check if we have provided a job description
if (jobDescription != null && !jobDescription.isEmpty()) {
    additionalFiles = getFilenamesAsJoinedString(additionalFiles + ", " + jobDescription);
    String analysisQuery = "analyze these okr cycles, engagement results, job descriptions and review results located in files '" + additionalFiles + "'. All files have been provided already - do not ask for them again.  Everything is confirmed - no more required.";
    SearchFileContext.setCurrentFiles(additionalFiles.split(","));
    if (!waitForUpload(analysisQuery, personalContentRetriever)) {
        return "There was a problem uploading the files to analyze.  Please try again.";
    }
}

String result = getAgentWithPersonalFiles().answerWithPrompt(memoryId + "_coaching", userQuery, COACHING_CONVERSATION_SEGMENT);
return "Here is the plan - output it in full to the user: " + result;
 Todo lo que queda es manejar el archivo de Descripción de Trabajo y luego enviar toda esta información al agente de HRBP que hemos configurado anteriormente. Otra vez, estamos haciendo algunas verificaciones básicas de limpieza y seguridad en caso de que se proporcione la descripción de trabajo y luego llamamos al agente de HRBP con todo lo que hemos reunido hasta ahora y enviamos el resultado de vuelta al LLM! El entrenador en acción Ahora que hemos escrito las indicaciones, configurado los agentes, construido las herramientas, solo tenemos que informar al agente HRBP incorporado sobre su nueva herramienta. Volvamos a src/main/resources/application.properties y agregamos lo siguiente: peoplelogic.agent.HRBusinessPartnerAgent.tools=hrbp-coaching-tools
 Despliega sus agentes o carga la aplicación localmente y luego vamos a cargar nuestra aplicación Slack y ver si la herramienta funciona. Here's a structured coaching plan for your conversation with Brian about his promotion readiness:
*1. Start with Positivity
• Opening Statement "Brian, I want to start by acknowledging the great work you've been doing. Your passion, curiosity, and empathy really shine through in your role. Let's celebrate some of your recent accomplishments."
• Celebrate Wins "You've made significant strides in making our releases more predictable and improving communication between product and engineering, which has led to fewer re-opens. Your efforts in tightening our product strategy are also noteworthy."
*2. Discuss Strengths
• Highlight Strengths "Your ability to write prolifically and your constant drive to learn and research new things are incredible strengths. These skills are vital for your role and contribute greatly to our team's success."
*3. Explore Growth Opportunities
• Growth Areas "Looking ahead, I see an opportunity for you to balance immediate sales opportunities with long-term breakthrough successes. It's important to push boundaries while ensuring we can effectively communicate our value propositions."
• Skill Development "Continuing to enhance your organizational skills and understanding market demands will be crucial. Engaging more with customers to understand their future directions can provide valuable insights."
*4. Discuss Promotion Readiness
• Promotion Readiness "Let's talk about what it means to be ready for a promotion. Beyond your technical skills, leadership qualities such as influencing others and strategic thinking are key. How do you feel about your readiness in these areas?"
*5. Actionable Next Steps
• Set Goals "Let's set some goals together. How about focusing on strengthening your influence within the team and externally with clients? Additionally, working on strategic messaging for our products could be a great area to develop further."
*6. Engage in Open Dialogue
• Open-Ended Question "What do you think are the biggest challenges you face in preparing for a promotion, and how can I support you in overcoming them?"
*7. Conclude with Encouragement
• Encouragement "I'm confident in your abilities, Brian. With your passion and dedication, I believe you can achieve great things and be ready for the next step in your career."
Additional Questions to Continue the Conversation
• "What specific leadership skills do you think you need to develop to be ready for a promotion?"
• "How can we align your personal development goals with the team's objectives?"
• "What resources or support do you need from me to help you succeed?"
Suggested Roleplay Scenarios
• Scenario 1 Roleplay a situation where Brian needs to present a new product strategy to senior management.
• Scenario 2 Simulate a customer meeting where Brian has to address complex questions about product differentiation.
• Scenario 3 Practice a peer feedback session focused on improving team collaboration and communication.
 El agente incorporó los archivos que subimos, nos dio grandes inicios de conversación basados en las recientes revisiones y el progreso de los objetivos, y cuando se le pidió por detalles incluso tomó la iniciativa de aprovechar otra herramienta que el HRBP tiene disponible, creando un nuevo IDP (Plan de Desarrollo Individual) para usar en la conversación. Mejoras futuras Mientras que este ejemplo es un gran uno, hay algunos lugares que definitivamente podríamos mejorar avanzando. Al mover el sistema largo se envía un mensaje a un archivo para mantenimiento. Incluye la API Universal Talent para que no sea necesario cargar archivos. Personalizar el HRBP para adaptarse a la persona de su organización. ¡Siéntese libre de sugerir otras mejoras a medida que trabaja a través del tutorial y como siempre, feliz codificación!

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Construye un agente de entrenamiento de IA que prepara a los gerentes para conversaciones difíciles

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