Use your AI Agents to solve the crime

The only thing missing now is your Lead Detective Agent. This agent will then use the information retrieved from the other two agents to solve the crime and determine the value of the stolen items.

Build Your Lead Detective Agent

Step 1: Add the Agent Configuration

👉 Navigate to /project/Python/starter-project/config/agents.yaml

👉 Add the configuration for the Lead Detective Agent below your other agent.

lead_detective_agent:
  role: >
    Lead Detective Agent
  goal: >
    Solve the crime by determining which of the three suspects stole the painting.
  backstory: >
    You are a lead detective, solving burglaries.
  llm: sap/gpt-4o

Step 2: Add Tasks for your Agent

👉 Navigate to /project/Python/starter-project/config/tasks.yaml

👉 Add the configuration for the solve crime for your new Lead Detective Agent.

solve_crime:
  description: >
    Find the thief among the suspects by activating the evidence analyst agent and instructing them to look for information on each of the three suspects
    using the grounding tool. They should find information on alibis and motives and return a report for you to analyze.
  expected_output: >
    The name of the thief and the total value of the stolen goods for the insurance.
  agent: lead_detective_agent

Step 3: Add the Agent and Task to the Crew

Now you'll add the Lead Detective Agent and its task to your investigator crew. This agent will coordinate the investigation by using results from the other agents.

👉 Open /project/Python/starter-project/investigator_crew.py

👉 Inside the InvestigatorCrew class, add the new agent and task methods after the existing analyze_evidence_task method and before the @crew method:

    @agent
    def lead_detective_agent(self) -> Agent:
        return Agent(
            config=self.agents_config['lead_detective_agent'],
            verbose=True
        )

    @task
    def solve_crime(self) -> Task:
        return Task(
            config=self.tasks_config['solve_crime'],
            context=[self.appraise_loss_task(), self.analyze_evidence_task()]  # 👈 Lead detective uses results from other tasks
        )

💡 Where to place this code: Add these methods inside the InvestigatorCrew class, after your analyze_evidence_task() method. The final order should be:

appraiser_agent() method

appraise_loss_task() method

evidence_analyst_agent() method

analyze_evidence_task() method

👈 Add lead_detective_agent() here

👈 Add solve_crime() here

crew() method (keep at the end)

💡 Understanding the context parameter:

context=[self.appraise_loss_task(), self.analyze_evidence_task()] tells CrewAI that the solve_crime task depends on the other two tasks

The Lead Detective will receive the output from both the Loss Appraiser and Evidence Analyst

This enables the detective to combine financial predictions with evidence analysis to solve the crime

Step 4: Verify Crew Configuration

Your crew configuration should already be set from Exercise 04, but let's verify it's correct.

👉 In /project/Python/starter-project/investigator_crew.py, check that your @crew method looks like this:

    @crew
    def crew(self) -> Crew:
        return Crew(
            agents=self.agents,  # Automatically collected by @agent decorator (all 3 agents)
            tasks=self.tasks,    # Automatically collected by @task decorator (all 3 tasks)
            process=Process.sequential,  # Tasks run in order: appraise → analyze → solve
            verbose=True  # Print detailed execution logs
        )

✅ If this is already in your file, you're good! CrewAI will automatically collect all decorated agents and tasks.

Step 5: Verify main.py (No Changes Needed)

Your main.py from Exercise 04 should already be correct. It doesn't need any changes for Exercise 06!

💡 What's happening: The same main.py that ran 2 agents in Exercise 04 will now automatically run all 3 agents (including your new Lead Detective). CrewAI collects all @agent and @task decorated methods automatically.

👉 (Optional) Double-check your /project/Python/starter-project/main.py has both required inputs:

result = InvestigatorCrew().crew().kickoff(inputs={
    'payload': payload,
    'suspect_names': "Sophie Dubois, Marcus Chen, Viktor Petrov"
})

✅ If you see these two inputs, you're all set—no changes needed!

Solve the Crime

👉 Run your crew to start the investigation!

python project/Python/starter-project/main.py

⏱️ This may take 2-5 minutes as your agents:

Search evidence documents for each suspect

Predict values of stolen items using RPT-1

Analyze findings and identify the culprit

👉 Review the final output—who does your Lead Detective identify as the thief?

👉 Call for the instructor and share your suspect.

If Your Answer is Incorrect

If your Lead Detective identifies the wrong suspect, you'll need to refine your agent prompts to improve their reasoning.

Which prompts to adjust:

Lead Detective's Goal (config/agents.yaml)
- Make it more specific about what evidence to prioritize
- Example: Add "Focus on alibis, financial motives, and access to the museum"
Lead Detective's Task Description (config/tasks.yaml)
- Add explicit steps for analysis
- Example: "Compare each suspect's alibi against security logs. Identify who had opportunity, means, and motive."
Evidence Analyst's Task (config/tasks.yaml)
- Ensure it searches thoroughly for each suspect
- Example: "For each suspect, search for: alibi verification, financial records, access logs, and connections"

Tips for improving prompts:

✅ Be specific about what to analyze (alibi, motive, opportunity)
✅ Ask agents to cite specific documents
✅ Request cross-referencing of information
✅ Instruct agents to explain their reasoning step-by-step
❌ Avoid vague instructions like "solve the crime" without guidance
❌ Don't assume agents know which evidence is most important

👉 After updating prompts in the YAML files, run python project/Python/starter-project/main.py again

👉 Verify the answer with the instructor

Understanding Multi-Agent Orchestration

What Just Happened?

You created a complete multi-agent system where:

The Lead Detective Agent orchestrates the investigation by delegating tasks
The Evidence Analyst Agent retrieves and analyzes evidence from documents
The Loss Appraiser Agent predicts financial values of stolen items
Agent Communication flows through task delegation and result aggregation
Reasoning Integration combines evidence, alibis, motives, and values to solve the crime

The Investigation Flow

Lead Detective → Evidence Analysis → Grounding Search → Suspect Investigation
              ↓
         Loss Appraisal → RPT-1 Predictions → Value Determination
              ↓
         Crime Resolution → Suspect Identification → Final Report

Why This Matters

Multi-agent systems are powerful because they:

Distribute Responsibilities across specialized agents with distinct roles
Enable Collaboration through task delegation and information sharing
Improve Reasoning by combining multiple expert perspectives
Handle Complexity by breaking down large problems into manageable subtasks
Scale Efficiently as new agents and tools can be added without disrupting existing ones

Key Takeaways

Multi-Agent Systems decompose complex problems into specialized, collaborative agents
Sequential Processing allows agents to work in order, with later agents using earlier results
Task Delegation allows agents to coordinate work and share results through the CrewAI framework
Tool Integration across multiple agents (RPT-1, Grounding Service) provides comprehensive capabilities
Agent Prompts must be carefully crafted to guide reasoning and ensure correct tool usage
Crew Orchestration handles sequencing, communication, and result aggregation between agents
Iterative Refinement of agent prompts and task descriptions improves investigation accuracy

Next Steps

In the following exercises, you will:

✅ Build a basic agent
✅ Add custom tools to your agents so they can access external data
✅ Create a complete crew with multiple agents working together
✅ Integrate the Grounding Service for better reasoning and fact-checking
✅ Solve the museum art theft mystery using your fully-featured agent team (this exercise)

Congratulations on completing the CodeJam! You've successfully built a sophisticated multi-agent AI system that can:

Analyze evidence from documents
Predict financial values using the SAP-RPT-1 model
Coordinate between multiple specialized agents
Solve complex real-world problems through collaborative reasoning

Troubleshooting

Issue: AttributeError: 'NoneType' object has no attribute 'get' when running main.py

Solution: This error occurs when YAML configuration has incorrect indentation. Check your config/tasks.yaml file and ensure all fields (description, expected_output, agent) are indented with 2 spaces under each task name:

solve_crime:
  description: > # ← Must be indented 2 spaces
    Your task description
  expected_output: > # ← Must be indented 2 spaces
    Expected output
  agent: lead_detective_agent # ← Must be indented 2 spaces

Issue: Agent is not using the Grounding Service tool

Solution: Ensure the Evidence Analyst Agent has tools=[call_grounding_service] in its definition and the task description explicitly mentions searching for evidence

Issue: Lead Detective Agent doesn't delegate to other agents

Solution: Verify that:
- All agents are included in the crew: agents=[appraiser_agent, evidence_analyst_agent, lead_detective_agent]
- Tasks are assigned to the correct agents
- Task descriptions clearly indicate which agents should be consulted

Issue: RPT-1 tool is not returning predictions

Solution: Ensure:
- The Loss Appraiser Agent has tools=[call_rpt1] assigned
- Your .env file contains valid RPT-1 deployment URL and credentials
- The payload structure matches the RPT-1 API specification

Issue: Grounding search returns no relevant documents

Solution: Check that:
- The pipeline ID is correct
- Documents are properly indexed in SAP AI Launchpad
- Your search query is descriptive and targets the right evidence type
- The maxChunkCount parameter is appropriate

Issue: Agents not executing in order or parallel execution

Solution: Ensure you've set process=Process.sequential in your @crew method. Use Process.hierarchical if you need a manager agent to coordinate.

Issue: Agent is hallucinating or not finding the correct suspect

Solution: Refine your Lead Detective Agent's prompt to:
- Be more specific about what evidence to look for
- Include explicit instructions to cross-reference alibis and motives
- Ask the agent to provide reasoning before concluding
- Consider adjusting the task description to guide the investigation more clearly

Issue: CrewAI raises errors about task assignment or agent delegation

Solution: Verify that:
- All task configurations in tasks.yaml match task method names
- All agent configurations in agents.yaml match agent method names
- Tasks are assigned to agents using the agent field in the YAML
- The crew includes all agents in the correct order

Resources

CrewAI Multi-Agent Documentation

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Use your AI Agents to solve the crime

Build Your Lead Detective Agent

Step 1: Add the Agent Configuration

Step 2: Add Tasks for your Agent

Step 3: Add the Agent and Task to the Crew

Step 4: Verify Crew Configuration

Step 5: Verify main.py (No Changes Needed)

Solve the Crime

If Your Answer is Incorrect

Understanding Multi-Agent Orchestration

What Just Happened?

The Investigation Flow

Why This Matters

Key Takeaways

Next Steps

Troubleshooting

Resources

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Use your AI Agents to solve the crime

Build Your Lead Detective Agent

Step 1: Add the Agent Configuration

Step 2: Add Tasks for your Agent

Step 3: Add the Agent and Task to the Crew

Step 4: Verify Crew Configuration

Step 5: Verify main.py (No Changes Needed)

Solve the Crime

If Your Answer is Incorrect

Understanding Multi-Agent Orchestration

What Just Happened?

The Investigation Flow

Why This Matters

Key Takeaways

Next Steps

Troubleshooting

Resources