Name: Viksa
Rating: 4.9 (150 reviews)

For the last decade, infrastructure automation has relied on explicit, hardcoded pipelines. We write Bash scripts, Ansible playbooks, and Jenkins pipelines. We define exact, step-by-step instructions. But in modern distributed systems, environments drift, microservices fail silently, and configurations change. When a script hits an unexpected state, it crashes. Enter Agentic Ops: the paradigm shift from hardcoded pipelines to goal-driven autonomous runbooks.

The Brittle Nature of Traditional DAGs

Traditional automation platforms require developers to define a Directed Acyclic Graph (DAG) for every possible failure scenario. If you want to handle a disk space warning, you build a flowchart: check usage, alert team, find logs, run delete command, recheck. If any step fails—say, the logs folder has changed permissions—the flowchart halts. You end up with page alerts in the middle of the night, debugging an automation script that was supposed to save you time.

The Think-Act-Observe Closed Loop

Instead of detailing the steps, Agentic Ops defines the outcome. You equip the agent with tools (like checking metrics, running queries, restarting pods) and state a goal: 'Identify and clear old log files if staging disk space is above 80%.' The agent executes a continuous loop: 1. **Think**: Evaluates the current state, checks what tools are available, and creates a plan. 2. **Act**: Invokes a tool, such as running a disk status check. 3. **Observe**: Inspects the outcome. If it encounters a permission error, it doesn't fail; it 'thinks' again and chooses a remediation tool (such as requesting helper access or checking a different mount point).

Equipping Agents with the Python SDK

In Viksa, capabilities are defined as standard Python functions using the SDK. Developers write atomic tools, and the orchestration engine handles schemas, routing, and scheduling automatically.

tools/disk_cleaner.pypy

from viksa_ai.runtime import mcp_endpoint, ViksaAuth

@mcp_endpoint(description="Scan and locate log files exceeding 100MB")
async def scan_large_logs(directory: str, auth: ViksaAuth) -> dict:
    # Code to securely scan the directory
    # Only runs within the permissions granted to the agent token
    return {"large_files": ["/var/log/app/debug.log", "/var/log/nginx/access.log"]}

Trusting Agents in Production

Autonomy does not mean lack of control. To run agents safely on critical infrastructure, guardrails are essential. Sensitive actions (like writing database scripts or killing processes) should require a human operator's approval. By routing approvals to collaboration platforms like Slack, teams get the speed of automation with the safety of manual oversight.

By adopting goal-driven loops over rigid flowcharts, teams can reduce the overhead of maintaining thousands of lines of automation scripts. Agentic Ops makes systems self-healing, responsive, and observable.