Agentic DevOps for the Rest of Us: A New Era of Intelligent SDLC

The concept of Agentic DevOps got introduced in Microsoft Build conference 2025. I was brainstorming ideas for Superagentic AI and I stumbled upon this crazy concept – what if we merged “Agentic” (think AI agents) with DevOps? I know, it sounds wild. I even came up with Agentic Co-Intelligence and Agent Experience, but then I thought, “Wait, is this just a bunch of nonsense?” Can DevOps and AI agents really get along? I mean, saying “Agentic DevOs” or “Agentic CI/CD” sounded like gibberish to me. But, I was too afraid to share my crazy ideas with the world. That is, until Microsoft went and introduced “Agentic DevOps” at their Build conference in 2025! I wasn’t that crazy after all. The moral of the story? Don’t be afraid to share your wild ideas, no matter how silly they may seem. And now, we’ve got Agentic AI, Agentic Workflows, Agentic Systems… and of course, Agentic DevOps. What’s next? Anyway, in this post, we’re going to dive into the wonderful world of Agentic DevOps.

In 2025, a quiet revolution began to reshape how we build software. It wasn’t just about faster coding—it was about transforming the entire software lifecycle with autonomous and semi-autonomous agents working alongside developers. Welcome to the age of Agentic DevOps—where intelligent agents don’t just assist; they collaborate, optimize, and co-create across development, QA, and operations. It’s a new era of Intelligent Software Development Life Cycle (SDLC).

Not Just for the Big Players

While Microsoft introduced this idea using tools like GitHub Copilot, Azure, and Visual Studio, Agentic DevOps is a paradigm, not a product. You don’t need to be in the Microsoft ecosystem to benefit. Agentic DevOps is a paradigm that combines human developer expertise with AI-powered tools and frameworks to enhance software delivery. While Microsoft popularized the concept, it’s not limited to their ecosystem. You can apply Agentic DevOps with:

Coding agents like : Cursor, Windsurf, Claude Code, Codex etc
Local AI coding agents: Continue, Cline, Roo
Open-source models: Llama 4, Mistral, DeepSeek, Qwen
On-prem deployment tools: vLLM, SGLang, TGI
Agent Frameworks: LangGraph, AutoGen, CrewAI, DSPy etc

These are just few examples but choose your preferred tooling and infrastructure, whether on-prem or in the cloud with AWS, GCP, Azure, IBM Cloud, Oracle Cloud, or Alibaba Cloud. Agentic DevOps is about augmenting human capabilities with AI, not about vendor lock-in. Agentic DevOps is for everyone.

What is Agentic DevOps?

Agentic DevOps is the next evolution of traditional DevOps. It introduces task-specific agents—AI-powered teammates embedded into your developer workflow—who help streamline, automate, and optimize all stages of software delivery:

Development Agents: Review pull requests, refactor legacy code, generate tests, implement features based on specs, and ensure security best practices.
QA Agents: Run automated tests, suggest test coverage improvements, find flaky tests, and triage bugs with context.
Code Optimisation/Cleaning Agents: Get rid of tech-debt and optimise existing code.
SRE/Production Agents: Monitor systems, respond to incidents, run diagnostics, propose fixes, and log follow-up issues automatically.

These agents collaborate with each other and with humans in the loop, acting as reliable copilots or independent problem-solvers—depending on the complexity and risk.

Examples in the Real World: DSPy + MCP + Agenspy

Let’s consider following examples as use cases for the Agentic DevOps.

PR Review Agent

A pull request is opened on GitHub. An agent immediately analyzes the changes:

Flags potential security issues or missed edge cases.
Suggests documentation updates.
Recommends cleaner implementation based on codebase history.
Runs a risk impact analysis on downstream modules.

All within minutes. You just review and approve.

Tech Debt Agent

An agent periodically scans your repositories:

Detects deprecated dependencies.
Suggests modernization paths.
Refactors functions with excessive cyclomatic complexity.
Highlights gaps in test coverage.

Instead of piling onto your backlog, you triage with intent.

Incident Response Agent

An alert fires at 3 a.m. Instead of waking up a human:

The agent runs root cause diagnostics.
Attempts auto-remediation (e.g., restarting pods, rolling back changes).
Logs the incident and proposed fix in your issue tracker.
If unresolved, it pings the right on-call engineer with a full report.

Fewer wake-up calls. More resilient systems. These are some examples but you can literally automate everything within SDLC with Agentic DevOps.

Agentic DevOps in Action: DSPy + MCP + Agenspy

Enough talk, let me show how we can implement the Agentic DevOps in action with some code.

Let’s bring this to life: We will create some example agents using there DSPy and MCP servers for GitHub. We will use the Agenspy library to get connection with MCP servers.

We will create a PRReviewAgent that analyze a sample PR and provide Security analysis, Documentation review, Implementation suggestions and perform Impact assessment. You need a Python setup and OPENAI_API_KEY and GITHUB_TOKEN. Then install dependencies.

pip install "agenspy[mcp]" dspy openai
npm install -g @modelcontextprotocol/server-github

1 2	pip install "agenspy[mcp]" dspy openai npm install -g @modelcontextprotocol/server-github

You have full setup guide in the README.md file. Here is the code for the PRReviewAgent that you can run directly.

import os
from typing import Dict, List, Optional
import dspy
from dspy import Prediction
from agenspy import RealMCPClient

class PRReviewAgent(dspy.Module):
    """Agent for automated PR reviews."""
    
    def __init__(self, mcp_client, github_token: Optional[str] = None):
        super().__init__()
        self.mcp_client = mcp_client
        self.github_token = github_token

        # Define predictors with modern signature syntax using ->
        self.analyze_changes = dspy.Predict(
            "pr_content -> security_issues: list[str], edge_cases: list[str]"
        )
        
        self.review_docs = dspy.Predict(
            "changes -> doc_updates: list[str], doc_suggestions: list[str]"
        )
        
        self.analyze_impact = dspy.Predict(
            "changes, codebase -> impact_analysis: str, risk_score: float"
        )

    def forward(self, pr_url: str) -> Prediction:
        """Review a pull request and provide comprehensive analysis."""
        print(f" Reviewing PR: {pr_url}")
        
        # Define default values
        default_security = ["No security issues identified in the current codebase"]
        default_edge_cases = ["No potential edge cases detected"]
        default_doc_updates = ["No documentation updates required"]
        default_doc_suggestions = ["Documentation appears to be complete"]
        default_impact = "No significant impact detected"
        default_risk = 0.0
        
        # Fetch PR data
        pr_data = self.mcp_client(
            context_request=f"Get PR details for {pr_url}",
            tool_name="get_pull_request",
            tool_args={"url": pr_url}
        )

        # Access the data properly from pr_data
        context_data = pr_data.get('context_data', {})
        diff_data = pr_data.get('diff', '') 

        try:
            # Analyze changes using modern predictor calls
            analysis = self.analyze_changes(pr_content=context_data)
            doc_review = self.review_docs(changes=diff_data)
            impact = self.analyze_impact(
                changes=diff_data,
                codebase=context_data
            )

            # Return a Prediction object with defaults
            return dspy.Prediction(
                security_issues=getattr(analysis, 'security_issues', []) or default_security,
                edge_cases=getattr(analysis, 'edge_cases', []) or default_edge_cases,
                doc_updates=getattr(doc_review, 'doc_updates', []) or default_doc_updates,
                doc_suggestions=getattr(doc_review, 'doc_suggestions', []) or default_doc_suggestions,
                impact_analysis=getattr(impact, 'impact_analysis', '') or default_impact,
                risk_score=getattr(impact, 'risk_score', 0.0) or default_risk
            )
        except Exception as e:
            print(f"Analysis error: {str(e)}")
            # Return prediction with default values on error
            return dspy.Prediction(
                security_issues=default_security,
                edge_cases=default_edge_cases,
                doc_updates=default_doc_updates,
                doc_suggestions=default_doc_suggestions,
                impact_analysis=default_impact,
                risk_score=default_risk
            )

def main():
    """Run PR Review Agent demo."""
    github_token = os.getenv('GITHUB_TOKEN')
    openai_key = os.getenv('OPENAI_API_KEY')
    
    if not all([github_token, openai_key]):
        raise EnvironmentError("Please set GITHUB_TOKEN and OPENAI_API_KEY")

    # Configure DSPy with modern configuration
    lm = dspy.LM('openai/gpt-4o-mini', api_key=openai_key)
    dspy.configure(lm=lm)

    # Setup MCP client
    mcp_client = RealMCPClient(
        ["npx", "-y", "@modelcontextprotocol/server-github"]
    )

    try:
        # Create and run agent
        agent = PRReviewAgent(mcp_client, github_token)
        result = agent.forward("https://github.com/SuperagenticAI/SuperXLab/pull/1")

        print("\n PR Review Results:")
        for key, value in result.items():
            print(f"\n{key.replace('_', ' ').title()}:")
            if isinstance(value, list):
                for item in value:
                    print(f"  - {item}")
            else:
                print(f"  {value}")
                
    except Exception as e:
        print(f"Error during PR review: {str(e)}")

if __name__ == "__main__":
    main()

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import os

from typing import Dict, List, Optional

import dspy

from dspy import Prediction

from agenspy import RealMCPClient

class PRReviewAgent(dspy.Module):

"""Agent for automated PR reviews."""

def __init__(self, mcp_client, github_token: Optional[str] = None):

super().__init__()

self.mcp_client = mcp_client

self.github_token = github_token

# Define predictors with modern signature syntax using ->

self.analyze_changes = dspy.Predict(

"pr_content -> security_issues: list[str], edge_cases: list[str]"

)

self.review_docs = dspy.Predict(

"changes -> doc_updates: list[str], doc_suggestions: list[str]"

)

self.analyze_impact = dspy.Predict(

"changes, codebase -> impact_analysis: str, risk_score: float"

)

def forward(self, pr_url: str) -> Prediction:

"""Review a pull request and provide comprehensive analysis."""

print(f" Reviewing PR: {pr_url}")

# Define default values

default_security = ["No security issues identified in the current codebase"]

default_edge_cases = ["No potential edge cases detected"]

default_doc_updates = ["No documentation updates required"]

default_doc_suggestions = ["Documentation appears to be complete"]

default_impact = "No significant impact detected"

default_risk = 0.0

# Fetch PR data

pr_data = self.mcp_client(

context_request=f"Get PR details for {pr_url}",

tool_name="get_pull_request",

tool_args={"url": pr_url}

)

# Access the data properly from pr_data

context_data = pr_data.get('context_data', {})

diff_data = pr_data.get('diff', '')

try:

# Analyze changes using modern predictor calls

analysis = self.analyze_changes(pr_content=context_data)

doc_review = self.review_docs(changes=diff_data)

impact = self.analyze_impact(

changes=diff_data,

codebase=context_data

)

# Return a Prediction object with defaults

return dspy.Prediction(

security_issues=getattr(analysis, 'security_issues', []) or default_security,

edge_cases=getattr(analysis, 'edge_cases', []) or default_edge_cases,

doc_updates=getattr(doc_review, 'doc_updates', []) or default_doc_updates,

doc_suggestions=getattr(doc_review, 'doc_suggestions', []) or default_doc_suggestions,

impact_analysis=getattr(impact, 'impact_analysis', '') or default_impact,

risk_score=getattr(impact, 'risk_score', 0.0) or default_risk

)

except Exception as e:

print(f"Analysis error: {str(e)}")

# Return prediction with default values on error

return dspy.Prediction(

security_issues=default_security,

edge_cases=default_edge_cases,

doc_updates=default_doc_updates,

doc_suggestions=default_doc_suggestions,

impact_analysis=default_impact,

risk_score=default_risk

)

def main():

"""Run PR Review Agent demo."""

github_token = os.getenv('GITHUB_TOKEN')

openai_key = os.getenv('OPENAI_API_KEY')

if not all([github_token, openai_key]):

raise EnvironmentError("Please set GITHUB_TOKEN and OPENAI_API_KEY")

# Configure DSPy with modern configuration

lm = dspy.LM('openai/gpt-4o-mini', api_key=openai_key)

dspy.configure(lm=lm)

# Setup MCP client

mcp_client = RealMCPClient(

["npx", "-y", "@modelcontextprotocol/server-github"]

)

try:

# Create and run agent

agent = PRReviewAgent(mcp_client, github_token)

result = agent.forward("https://github.com/SuperagenticAI/SuperXLab/pull/1")

print("\n PR Review Results:")

for key, value in result.items():

print(f"\n{key.replace('_', ' ').title()}:")

if isinstance(value, list):

for item in value:

print(f" - {item}")

else:

print(f" {value}")

except Exception as e:

print(f"Error during PR review: {str(e)}")

if __name__ == "__main__":

main()

You can save this file and run it to see the results. There are other agents in the repo

Source Code with Repo: Agentic-DevOps

From Reactive to Creative

Traditional DevOps helped break down silos. Agentic DevOps breaks down friction. It frees you from the grind—bug triage, boilerplate writing, security patching—and returns focus to what brought you into tech in the first place: building something meaningful.

By offloading the mundane and scaling expertise through agents, teams can:

Ship features faster.
Maintain quality without burning out.
Crush tech debt instead of being crushed by it.
Embed security and compliance into the fabric of development—not as an afterthought.

Why Now?

Several forces are converging:

Generative AI maturity: We now have models capable of deep code understanding, not just autocomplete.
Toolchain interoperability: Many IDEs, cloud platforms, and CICD tools are exposing agent interfaces.
Workforce demand: Devs are overwhelmed; talent is scarce. Augmenting human effort is no longer optional.

Agentic DevOps represents a new operating system for software teams—not to replace humans, but to amplify them.

Getting Started: No Microsoft Required

You can begin exploring Agentic DevOps today by:

Integrating AI PR review bots (like those built on OpenAI, Codeium, or Hugging Face models).
Using CI pipelines that trigger agents for code quality, test generation, or infra scans.
Running fine-tuned agents on your codebase using frameworks like DSPy, LangChain, or OpenDevin.
Designing agents to analyze issues, route bugs, or propose architectural improvements based on changelog diffs.

All it takes is intent, some glue code, and experimentation. There are some examples in the repo.

Source Code with Repo: Agentic-DevOps

Conclusion

The future is Agentic and joyful. Agentic DevOps is not just about productivity—it’s about bringing joy back to development. By reducing toil, you recover your flow. By scaling best practices, you improve reliability. By freeing time, you open space to dream again. As this field matures, you won’t just code faster—you’ll build smarter, safer, and with greater imagination. Agentic DevOps really a New Era of Intelligent Software Development Life Cycle. Stay tuned with Superagentic AI for more complex and business ready Agentic DevOps use cases.

Life 3.0: Goodbye Apple . Welcome Superagentic AI.

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