10 Minute Setup

Quickstart Guide

Learn how to author a Python agent, register endpoints in the Agents Fleet, trigger dynamic goals, and monitor live Think-Act-Observe logs.

01

Viksa AI Operational Architecture

The platform is built around a closed-loop framework. Every goal follows a three-step cycle:

PhaseAction descriptionSystem status
THINKExecutor parses goal, checks environment variables, and selects agents.Analyzing telemetry
ACTSelected agents coordinate, run commands, call APIs, and deploy workloads.Executing tasks
OBSERVETraces output data, logs latency, registers approvals, and learns outcomes.Verifying system metrics
02

Step-by-Step Guide

  1. 1

    Sign in to your Workspace

    Viksa AI is designed for secure, enterprise environments. Obtain your organization login credentials or access the platform directly using corporate SSO/SAML integrations.

    • Log in via your dedicated URL and create a new project.
    • Retrieve your project access keys from the secure dashboard dashboard.
  2. 2

    Build your first Python Agent

    Define your agent using the typed Python SDK. Each decorated @endpoint registers a callable action that the execution engine can dynamically invoke at runtime.

    agent.py
    from viksaai import agent, endpoint
    
    @agent(
        name="hello_agent",
        description="Returns a personalized greeting",
    )
    class HelloAgent:
    
        @endpoint(name="greet", description="Greet someone by name")
        def greet(self, name: str) -> dict:
            return {"message": f"Hello, {name}! Welcome to Viksa AI."}
    

    List your runtime dependencies in a standard requirements.txt:

    requirements.txt
    viksaai-sdk>=1.0.0
    requests>=2.31.0
    

    Upload these files to your Agents Fleet repository in the dashboard, select build target, and deploy.

  3. 3

    Add a Specialized Agent (Optional)

    The Viksa AI executor excels at chaining multiple agents together to fulfill a single, high-level goal. Add a second server health checker:

    server_health.py
    from viksaai import agent, endpoint
    
    @agent(name="server_health", description="Check service health")
    class ServerHealthAgent:
    
        @endpoint(name="check_health")
        def check_health(self) -> dict:
            return {"status": "ok", "cpu_percent": 12.4}
    
  4. 4

    Trigger dynamic execution

    Open the **Interactive Chat** in your dashboard, describe the objective in plain English, and watch the executor dynamically plan, coordinate, and execute the steps:

    Execution preview
    User: "Greet John and check if our servers are healthy."
    
    ThinkActObserve:
      1. hello_agent.greet(name="John")
      2. server_health.check_health()
  5. 5

    Invoke Webhook Triggers

    Automate goal execution from alerts or third-party webhooks. Configure an active **Trigger**, and POST a JSON payload specifying the objective:

    POST /api/v1/triggers/tg-982a/invoke
    {
      "event": "incident.opened",
      "severity": "high",
      "service": "payments-api",
      "goal": "Investigate elevated error rate and restart unhealthy pods if needed"
    }
03

Next Steps