Fix agentic-tutorial after bump to 1.8.0

agentic-tutorial/src/main/java/_1_basic_agent/_1a_Basic_Agent_Example.java

@@ -37,7 +37,7 @@ public static void main(String[] args) throws IOException {
         CvGenerator cvGenerator = AgenticServices
                 .agentBuilder(CvGenerator.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("masterCv") // we can optionally define the name of the output object
+                .outputKey("masterCv") // we can optionally define the key of the output object
                 .build();
 
         // 4. Load text file from resources/documents/user_life_story.txt

agentic-tutorial/src/main/java/_2_sequential_workflow/_2a_Sequential_Agent_Example.java

@@ -41,14 +41,14 @@ public static void main(String[] args) throws IOException {
         CvGenerator cvGenerator = AgenticServices
                 .agentBuilder(CvGenerator.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("masterCv") // if you want to pass this variable from agent 1 to agent 2,
-                // then make sure the output name here matches the input variable name
+                .outputKey("masterCv") // if you want to pass this variable from agent 1 to agent 2,
+                // then make sure the output key here matches the input variable name
                 // specified in the second agent interface agent_interfaces/CvTailor.java
                 .build();
         CvTailor cvTailor = AgenticServices
                 .agentBuilder(CvTailor.class)
                 .chatModel(CHAT_MODEL) // note that it is also possible to use a different model for a different agent
-                .outputName("tailoredCv") // we need to define the name of the output object
+                .outputKey("tailoredCv") // we need to define the key of the output object
                 // if we would put "masterCv" here, the original master CV would be overwritten
                 // by the second agent. In this case we don't want this, but it's a useful feature.
                 .build();
@@ -59,7 +59,7 @@ public static void main(String[] args) throws IOException {
         UntypedAgent tailoredCvGenerator = AgenticServices // use UntypedAgent unless you define the resulting composed agent, see below
                 .sequenceBuilder()
                 .subAgents(cvGenerator, cvTailor) // this can be as many as you want, order matters
-                .outputName("tailoredCv") // this is the final output of the composed agent
+                .outputKey("tailoredCv") // this is the final output of the composed agent
                 // note that you can use as output any field that is part of the AgenticScope
                 // for example you could output 'masterCv' instead of tailoredCv (even if in this case that makes no sense)
                 .build();

agentic-tutorial/src/main/java/_2_sequential_workflow/_2b_Sequential_Agent_Example_Typed.java

@@ -42,14 +42,14 @@ public static void main(String[] args) throws IOException {
         CvGenerator cvGenerator = AgenticServices
                 .agentBuilder(CvGenerator.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("masterCv") // if you want to pass this variable from agent 1 to agent 2,
-                // then make sure the output name here matches the input variable name
+                .outputKey("masterCv") // if you want to pass this variable from agent 1 to agent 2,
+                // then make sure the output key here matches the input variable name
                 // specified in the second agent interface agent_interfaces/CvTailor.java
                 .build();
         CvTailor cvTailor = AgenticServices
                 .agentBuilder(CvTailor.class)
                 .chatModel(CHAT_MODEL) // note that it is also possible to use a different model for a different agent
-                .outputName("tailoredCv") // we need to define the name of the output object
+                .outputKey("tailoredCv") // we need to define the key of the output object
                 // if we would put "masterCv" here, the original master CV would be overwritten
                 // by the second agent. In this case we don't want this, but it's a useful feature.
                 .build();
@@ -66,7 +66,7 @@ public static void main(String[] args) throws IOException {
         SequenceCvGenerator sequenceCvGenerator = AgenticServices
                 .sequenceBuilder(SequenceCvGenerator.class) // here we specify the typed interface
                 .subAgents(cvGenerator, cvTailor)
-                .outputName("bothCvsAndLifeStory")
+                .outputKey("bothCvsAndLifeStory")
                 .output(agenticScope -> { // any method is possible, but we collect some internal variables.
                     Map<String, String> bothCvsAndLifeStory = Map.of(
                             "lifeStory", agenticScope.readState("lifeStory", ""),

agentic-tutorial/src/main/java/_3_loop_workflow/_3a_Loop_Agent_Example.java

@@ -38,17 +38,17 @@ public static void main(String[] args) throws IOException {
         // 3. Create all agents using AgenticServices
         CvReviewer cvReviewer = AgenticServices.agentBuilder(CvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("cvReview") // this gets updated in every iteration with new feedback for the next tailoring
+                .outputKey("cvReview") // this gets updated in every iteration with new feedback for the next tailoring
                 .build();
         ScoredCvTailor scoredCvTailor = AgenticServices.agentBuilder(ScoredCvTailor.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("cv") // this will be updated in every iteration, continuously improving the CV
+                .outputKey("cv") // this will be updated in every iteration, continuously improving the CV
                 .build();
 
         // 4. Build the sequence
         UntypedAgent reviewedCvGenerator = AgenticServices // use UntypedAgent unless you define the resulting composed agent, see _2_Sequential_Agent_Example
                 .loopBuilder().subAgents(cvReviewer, scoredCvTailor) // this can be as many as you want, order matters
-                .outputName("cv") // this is the final output we want to observe (the improved CV)
+                .outputKey("cv") // this is the final output we want to observe (the improved CV)
                 .exitCondition(agenticScope -> {
                             CvReview review = (CvReview) agenticScope.readState("cvReview");
                             System.out.println("Checking exit condition with score=" + review.score); // we log intermediary scores

agentic-tutorial/src/main/java/_3_loop_workflow/_3b_Loop_Agent_Example_States_And_Fail.java

@@ -36,11 +36,11 @@ public static void main(String[] args) throws IOException {
         // 1. Create all sub-agents (same as before)
         CvReviewer cvReviewer = AgenticServices.agentBuilder(CvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("cvReview") // this gets updated in every iteration with new feedback for the next tailoring
+                .outputKey("cvReview") // this gets updated in every iteration with new feedback for the next tailoring
                 .build();
         ScoredCvTailor scoredCvTailor = AgenticServices.agentBuilder(ScoredCvTailor.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("cv") // this will be updated in every iteration, continuously improving the CV
+                .outputKey("cv") // this will be updated in every iteration, continuously improving the CV
                 .build();
 
         // 2. Build the sequence and store the reviews on each exit condition check
@@ -53,7 +53,7 @@ public static void main(String[] args) throws IOException {
 
         UntypedAgent reviewedCvGenerator = AgenticServices // use UntypedAgent unless you define the resulting composed agent, see below
                 .loopBuilder().subAgents(cvReviewer, scoredCvTailor) // this can be as many as you want, order matters
-                .outputName("cvAndReview") // this is the final output we want to observe
+                .outputKey("cvAndReview") // this is the final output we want to observe
                 .output(agenticScope -> {
                     Map<String, Object> cvAndReview = Map.of(
                             "cv", agenticScope.readState("cv"),

agentic-tutorial/src/main/java/_4_parallel_workflow/_4_Parallel_Workflow_Example.java

@@ -43,17 +43,17 @@ public static void main(String[] args) throws IOException {
         // 3. Create all agents using AgenticServices
         HrCvReviewer hrCvReviewer = AgenticServices.agentBuilder(HrCvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("hrReview") // this will be overwritten in every iteration, and also be used as the final output we want to observe
+                .outputKey("hrReview") // this will be overwritten in every iteration, and also be used as the final output we want to observe
                 .build();
 
         ManagerCvReviewer managerCvReviewer = AgenticServices.agentBuilder(ManagerCvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("managerReview") // this overwrites the original input instructions, and is overwritten in every iteration and used as new instructions for the CvTailor
+                .outputKey("managerReview") // this overwrites the original input instructions, and is overwritten in every iteration and used as new instructions for the CvTailor
                 .build();
 
         TeamMemberCvReviewer teamMemberCvReviewer = AgenticServices.agentBuilder(TeamMemberCvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("teamMemberReview") // this overwrites the original input instructions, and is overwritten in every iteration and used as new instructions for the CvTailor
+                .outputKey("teamMemberReview") // this overwrites the original input instructions, and is overwritten in every iteration and used as new instructions for the CvTailor
                 .build();
 
         // 4. Build the sequence
@@ -63,7 +63,7 @@ public static void main(String[] args) throws IOException {
                 .parallelBuilder()
                 .subAgents(hrCvReviewer, managerCvReviewer, teamMemberCvReviewer) // this can be as many as you want
                 .executor(executor) // optional, by default an internal cached thread pool is used which will automatically shut down after execution is completed
-                .outputName("fullCvReview") // this is the final output we want to observe
+                .outputKey("fullCvReview") // this is the final output we want to observe
                 .output(agenticScope -> {
                     // read the outputs of each reviewer from the agentic scope
                     CvReview hrReview = (CvReview) agenticScope.readState("hrReview");

agentic-tutorial/src/main/java/_5_conditional_workflow/EmailAssistant.java

@@ -7,7 +7,7 @@
 
 public interface EmailAssistant {
 
-    @Agent("Sends rejection emails to candidates that didn't pass")
+    @Agent("Sends rejection emails to candidates that didn't pass, returns the sent email ID or 0 if no email could be sent")
     @SystemMessage("""
             You send a kind email to application candidates that did not pass the first review round.
             You also update the application status to 'rejected'.

agentic-tutorial/src/main/java/_5_conditional_workflow/_5b_Conditional_Workflow_Example_Async.java

@@ -35,19 +35,19 @@ public static void main(String[] args) throws IOException {
         ManagerCvReviewer managerCvReviewer = AgenticServices.agentBuilder(ManagerCvReviewer.class)
                 .chatModel(CHAT_MODEL)
                 .async(true) // async agent
-                .outputName("managerReview")
+                .outputKey("managerReview")
                 .build();
         EmailAssistant emailAssistant = AgenticServices.agentBuilder(EmailAssistant.class)
                 .chatModel(CHAT_MODEL)
                 .async(true)
                 .tools(new OrganizingTools())
-                .outputName("sentEmailId")
+                .outputKey("sentEmailId")
                 .build();
         InfoRequester infoRequester = AgenticServices.agentBuilder(InfoRequester.class)
                 .chatModel(CHAT_MODEL)
                 .async(true)
                 .tools(new OrganizingTools())
-                .outputName("sentEmailId")
+                .outputKey("sentEmailId")
                 .build();
 
         // 2. Build async conditional workflow

agentic-tutorial/src/main/java/_6_composed_workflow/_6_Composed_Workflow_Example.java

@@ -51,26 +51,26 @@ public static void main(String[] args) throws IOException {
         CvGenerator cvGenerator = AgenticServices
                 .agentBuilder(CvGenerator.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("cv")
+                .outputKey("cv")
                 .build();
 
         ScoredCvTailor scoredCvTailor = AgenticServices
                 .agentBuilder(ScoredCvTailor.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("cv")
+                .outputKey("cv")
                 .build();
 
         CvReviewer cvReviewer = AgenticServices
                 .agentBuilder(CvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("cvReview")
+                .outputKey("cvReview")
                 .build();
 
         // 2. Create the loop workflow for CV improvement
         UntypedAgent cvImprovementLoop = AgenticServices
                 .loopBuilder()
                 .subAgents(scoredCvTailor, cvReviewer)
-                .outputName("cv")
+                .outputKey("cv")
                 .exitCondition(agenticScope -> {
                     CvReview review = (CvReview) agenticScope.readState("cvReview");
                     System.out.println("CV Review Score: " + review.score);
@@ -87,7 +87,7 @@ public static void main(String[] args) throws IOException {
                 .subAgents(cvGenerator, cvReviewer, cvImprovementLoop)
                 // here we use the composed agent cvImprovementLoop inside the sequenceBuilder
                 // we also need the cvReviewer in order to generate a first review before entering the loop
-                .outputName("cv")
+                .outputKey("cv")
                 .build();
 
         // 4. Load input data
@@ -112,19 +112,19 @@ public static void main(String[] args) throws IOException {
         HrCvReviewer hrCvReviewer = AgenticServices
                 .agentBuilder(HrCvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("hrReview")
+                .outputKey("hrReview")
                 .build();
 
         ManagerCvReviewer managerCvReviewer = AgenticServices
                 .agentBuilder(ManagerCvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("managerReview")
+                .outputKey("managerReview")
                 .build();
 
         TeamMemberCvReviewer teamMemberCvReviewer = AgenticServices
                 .agentBuilder(TeamMemberCvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("teamMemberReview")
+                .outputKey("teamMemberReview")
                 .build();
 
         EmailAssistant emailAssistant = AgenticServices
@@ -144,7 +144,7 @@ public static void main(String[] args) throws IOException {
                 .parallelBuilder()
                 .subAgents(hrCvReviewer, managerCvReviewer, teamMemberCvReviewer)
                 .executor(Executors.newFixedThreadPool(3))
-                .outputName("combinedCvReview")
+                .outputKey("combinedCvReview")
                 .output(agenticScope -> {
                     CvReview hrReview = (CvReview) agenticScope.readState("hrReview");
                     CvReview managerReview = (CvReview) agenticScope.readState("managerReview");

agentic-tutorial/src/main/java/_7_supervisor_orchestration/_7a_Supervisor_Orchestration.java

@@ -44,20 +44,20 @@ public static void main(String[] args) throws IOException {
         // 1. Define all sub-agents
         HrCvReviewer hrReviewer = AgenticServices.agentBuilder(HrCvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("hrReview")
+                .outputKey("hrReview")
                 .build();
         // importantly, if we use the same method names for multiple agents
         // (in this case: 'reviewCv' for all reviewers) we best name our agents, like this:
         // @Agent(name = "managerReviewer", description = "Reviews a CV based on a job description, gives feedback and a score")
 
         ManagerCvReviewer managerReviewer = AgenticServices.agentBuilder(ManagerCvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("managerReview")
+                .outputKey("managerReview")
                 .build();
 
         TeamMemberCvReviewer teamReviewer = AgenticServices.agentBuilder(TeamMemberCvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("teamMemberReview")
+                .outputKey("teamMemberReview")
                 .build();
 
         InterviewOrganizer interviewOrganizer = AgenticServices.agentBuilder(InterviewOrganizer.class)

agentic-tutorial/src/main/java/_7_supervisor_orchestration/_7b_Supervisor_Orchestration_Advanced.java

@@ -54,12 +54,12 @@ public static void main(String[] args) throws IOException {
         InterviewOrganizer interviewOrganizer = AgenticServices.agentBuilder(InterviewOrganizer.class)
                 .chatModel(CHAT_MODEL)
                 .tools(new OrganizingTools())
-                .outputName("response")
+                .outputKey("response")
                 .build();
         EmailAssistant emailAssistant = AgenticServices.agentBuilder(EmailAssistant.class)
                 .chatModel(CHAT_MODEL)
                 .tools(new OrganizingTools())
-                .outputName("response")
+                .outputKey("response")
                 .build();
 
         // 2. Build supervisor

agentic-tutorial/src/main/java/_8_non_ai_agents/ScoreAggregator.java

@@ -11,7 +11,7 @@
  */
 public class ScoreAggregator {
 
-    @Agent(description = "Aggregates HR/Manager/Team reviews into a combined review", outputName = "combinedCvReview")
+    @Agent(description = "Aggregates HR/Manager/Team reviews into a combined review", outputKey = "combinedCvReview")
     public CvReview aggregate(@V("hrReview") CvReview hr,
                              @V("managerReview") CvReview mgr,
                              @V("teamMemberReview") CvReview team) {

agentic-tutorial/src/main/java/_8_non_ai_agents/_8_Non_AI_Agents.java

@@ -48,17 +48,17 @@ public static void main(String[] args) throws IOException {
         // 2. Build the AI sub-agents for the parallel review step
         HrCvReviewer hrReviewer = AgenticServices.agentBuilder(HrCvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("hrReview")
+                .outputKey("hrReview")
                 .build();
 
         ManagerCvReviewer managerReviewer = AgenticServices.agentBuilder(ManagerCvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("managerReview")
+                .outputKey("managerReview")
                 .build();
 
         TeamMemberCvReviewer teamReviewer = AgenticServices.agentBuilder(TeamMemberCvReviewer.class)
                 .chatModel(CHAT_MODEL)
-                .outputName("teamMemberReview")
+                .outputKey("teamMemberReview")
                 .build();
 
         // 3. Build the composed parallel agent
@@ -75,14 +75,14 @@ public static void main(String[] args) throws IOException {
                 .sequenceBuilder()
                 .subAgents(
                         parallelReviewWorkflow,
-                        new ScoreAggregator(), // no AgenticServices builder needed for non-AI agents. outputname 'combinedCvReview' is defined in the class
+                        new ScoreAggregator(), // no AgenticServices builder needed for non-AI agents. outputKey 'combinedCvReview' is defined in the class
                         new StatusUpdate(), // takes 'combinedCvReview' as input, no output needed
                         AgenticServices.agentAction(agenticScope -> { // another way to add non-AI agents that can operate on the AgenticScope
                             CvReview review = (CvReview) agenticScope.readState("combinedCvReview");
                             agenticScope.writeState("scoreAsPercentage", review.score * 100); // when agents from different systems communicate, output conversion is often needed
                         })
                 )
-                .outputName("scoreAsPercentage") // outputName defined on the non-AI agent annotation in ScoreAggregator.java
+                .outputKey("scoreAsPercentage") // outputKey defined on the non-AI agent annotation in ScoreAggregator.java
                 .build();
 
         // 5. Load input data