Support Agent: End-to-End on InteractiveAI

A hands-on learning project for the InteractiveAI platform

InteractiveAI is the operating platform for building, deploying, and managing production AI systems. It provides end-to-end infrastructure for the complete agentic AI lifecycle: from tracing LLM interactions and managing prompts to evaluating output quality and monitoring performance across development and production environments.

This project was designed as a focused, hands-on exercise to learn the platform's architecture by building something real: a fictional SaaS customer support agent that can onboard users, answer technical questions, and handle mock ticket data — all while enforcing brand rules.

Decoupling application logic from behavioral context

InteractiveAI's architecture fundamentally separates what the agent does (routines) from how it behaves (policies). The Build > Context section is where you define all four components that shape the agent's personality, knowledge, and workflows.

Sample: Policy YAML

# policy: support-guardrails
name: support-guardrails
description: Core behavioral rules for the SaaS support agent

conditions:
  - type: always  # applies to every turn

actions:
  - type: instruct
    content: |
      You are a helpful, technically-precise SaaS support agent.
      - Always be professional and empathetic.
      - NEVER guess or fabricate answers. If information is not
        available in your context, say so clearly.
      - Cite the specific product feature or documentation
        when answering technical questions.
      - If the user appears frustrated, acknowledge their
        frustration before providing a solution.

Sample: Routine YAML with branching logic

# routine: customer-support-flow
name: customer-support-flow
description: Main support routine with intent-based branching

nodes:
  - id: greeting
    type: chat
    content: "${welcome-greeting}"
    transitions:
      - target: billing-help
        condition: "user asks about billing, pricing, or invoices"
      - target: technical-help
        condition: "user asks a technical question"
      - target: onboarding
        condition: "user is new or asks about getting started"

  - id: billing-help
    type: chat
    content: |
      I'd be happy to help with your billing question.
      Let me pull up your account details.
    transitions:
      - target: resolve
        condition: "user confirms issue is resolved"

  - id: technical-help
    type: chat
    content: |
      Let me look into that technical issue for you.
      Can you share any error messages or steps to reproduce?
    transitions:
      - target: resolve
        condition: "user confirms issue is resolved"
      - target: escalate
        condition: "issue requires engineering escalation"

  - id: onboarding
    type: chat
    content: |
      Welcome! I'll walk you through getting started
      with our platform. Let's begin with setup.

  - id: escalate
    type: tool
    tool: support-tools:create_ticket
    content: "Creating a support ticket for the engineering team."

  - id: resolve
    type: chat
    content: "Glad I could help! Is there anything else?"

External integrations and secure credential management

The Connect section handles everything the agent needs to interact with the outside world: tool servers (via MCP), a knowledge base for retrieval grounding, and a secrets manager for API keys and credentials.

# Mock tool server declaration in the agent manifest
mcps:
  - name: support-tools
    url: https://my-mcp-server.example.com
    api_key:
      env_ref: SUPPORT_TOOLS_API_KEY   # resolves from Secrets Manager
    tools:
      - name: get_user
        description: "Fetch user profile by email or ID"
      - name: create_ticket
        description: "Create a support ticket for engineering escalation"

From configuration to running service in 5 steps

The Interactive Agent deployment is where agent configurations become running services. The platform's deployment wizard walks through five steps: Identity, Endpoint, Runtime, Schedule, and Review.

Try the live agent

The agent is deployed and running at its public endpoint. Interact with it below — it's the same agent served by InteractiveAI's built-in chat UI:

Live InteractiveAI agent — sign in with the Agent API Key to start a conversation.

Complete visibility into what the AI system is doing

The Govern section is where enterprise AI platforms shine — and it's a critical interview topic. Every agent interaction generates a hierarchical trace that you can inspect in detail.

# Using the InteractiveAI SDK to retrieve traces programmatically
from interactive_ai import InteractiveAI

client = InteractiveAI()

# List recent traces for debugging
traces = client.traces.list(
    project_id="my-project",
    limit=10,
    order_by="created_at",
    order="desc"
)

for trace in traces:
    print(f"Trace: {trace.id}")
    print(f"  Duration: {trace.duration_ms}ms")
    print(f"  Tokens: {trace.total_tokens}")
    print(f"  Status: {trace.status}")
    
    # Drill into observations
    for obs in trace.observations:
        print(f"    [{obs.type}] {obs.name}: {obs.duration_ms}ms")

Systematic quality evaluation and regression testing

The Improve section closes the loop: take real production traces, turn them into test cases, and run evaluations before shipping changes. This is how you ensure zero regressions when updating prompts or context.

Frontend Integration & Cross-Origin Security

Integrating third-party enterprise platforms into a portfolio site introduces real-world security challenges. I designed this implementation to bypass modern browser cookie restrictions while maintaining a seamless user experience.

Integration Architecture Diagram