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.
Viksa AI Operational Architecture
The platform is built around a closed-loop framework. Every goal follows a three-step cycle:
| Phase | Action description | System status |
|---|---|---|
| THINK | Executor parses goal, checks environment variables, and selects agents. | Analyzing telemetry |
| ACT | Selected agents coordinate, run commands, call APIs, and deploy workloads. | Executing tasks |
| OBSERVE | Traces output data, logs latency, registers approvals, and learns outcomes. | Verifying system metrics |
Step-by-Step Guide
- 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
Build your first Python Agent
Define your agent using the typed Python SDK. Each decorated
@endpointregisters 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>=2.31.0Upload these files to your Agents Fleet repository in the dashboard, select build target, and deploy.
- 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
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." Think → Act → Observe: 1. hello_agent.greet(name="John") 2. server_health.check_health() - 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" }
Next Steps
Now that you have deployed your first agentic loop, explore advanced configuration topics: