AI DevOps Engineer: Autonomous Incident Response & IaC Generation

Two DevOps engineers manage 40+ microservices. Night shifts, OOMKilled, CrashLoopBackOff, CPU and memory limit overruns, slow database queries. 60% of on-call time goes to L1 incidents. Engineers burn out. MTTR grows. No time left for architectural improvements. We develop an AI DevOps engineer—a digital DevOps specialist that independently handles incidents, analyzes logs, and generates IaC and CI/CD pipelines. Our experience: over 5 years in DevOps and AI, engineers certified on Kubernetes and AWS. We guarantee the AI agent will not execute dangerous operations without explicit confirmation. The AI DevOps engineer is an infrastructure AI agent that automates DevOps tasks and reduces team workload.

How the AI DevOps Engineer Reduces On-Call Load

The AI DevOps Engineer consists of a set of specialized agents:

• Incident Response agent — processes PagerDuty alerts, collects diagnostics (logs, metrics, Pod status), performs safe actions (restart, scale up), and escalates complex cases with full context.
• Log Analysis agent — groups errors, finds unusual patterns, and suggests root cause.
• IaC Generator — generates Terraform and Ansible code from textual descriptions.
• CI/CD Pipeline Generator — creates GitHub Actions, GitLab CI, and other pipelines.

Thanks to this, L1 tasks are handled by AI, while engineers focus on architecture and complex problems. The AI agent provides on-call automation, reducing incident response time.

Incident Response Agent

from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from typing import TypedDict, Annotated, Optional
import operator

llm = ChatOpenAI(model="gpt-4o", temperature=0)

class IncidentState(TypedDict):
    alert_data: dict
    investigation_steps: Annotated[list, operator.add]
    root_cause: Optional[str]
    severity: Optional[str]
    actions_taken: Annotated[list, operator.add]
    resolved: bool
    escalation_required: bool

@tool
def get_recent_logs(service: str, minutes: int = 30, level: str = "ERROR") -> str:
    """Get recent logs of a service from Loki/Elasticsearch.

    Args:
        service: Service name
        minutes: Time period in minutes
        level: Log level (ERROR, WARN, INFO)
    """
    logs = loki_client.query(
        query=f'{{app="{service}"}} |= "{level}"',
        start=f"-{minutes}m",
        limit=100,
    )
    return "\n".join(logs[:50])

@tool
def get_metrics(service: str, metric_names: list[str], minutes: int = 60) -> str:
    """Get service metrics from Prometheus."""
    metrics = {}
    for metric in metric_names:
        result = prometheus.query_range(
            query=f'{metric}{{service="{service}"}}',
            start=f"-{minutes}m",
            step="1m",
        )
        metrics[metric] = result
    return json.dumps(metrics)

@tool
def check_kubernetes_pods(namespace: str, label_selector: str = "") -> str:
    """Check Pod status in Kubernetes."""
    pods = k8s_client.list_pods(namespace=namespace, label_selector=label_selector)
    pod_status = [{
        "name": p.metadata.name,
        "phase": p.status.phase,
        "ready": all(c.ready for c in (p.status.container_statuses or [])),
        "restarts": sum(c.restart_count for c in (p.status.container_statuses or [])),
        "age_minutes": (datetime.now() - p.metadata.creation_timestamp).seconds // 60,
    } for p in pods.items]
    return json.dumps(pod_status)

@tool
def restart_deployment(namespace: str, deployment_name: str) -> str:
    """Restart a deployment in Kubernetes (rollout restart)."""
    k8s_apps.patch_namespaced_deployment(
        name=deployment_name,
        namespace=namespace,
        body={"spec": {"template": {"metadata": {"annotations": {
            "kubectl.kubernetes.io/restartedAt": datetime.now().isoformat()
        }}}}},
    )
    return f"Deployment {deployment_name} restarting"

@tool
def scale_deployment(namespace: str, deployment_name: str, replicas: int) -> str:
    """Scale a deployment."""
    if replicas > 20:
        return "Error: scaling limit exceeded (20 replicas)"
    k8s_apps.patch_namespaced_deployment_scale(
        name=deployment_name,
        namespace=namespace,
        body={"spec": {"replicas": replicas}},
    )
    return f"Deployment {deployment_name} scaled to {replicas} replicas"

# Incident response agent
incident_tools = [get_recent_logs, get_metrics, check_kubernetes_pods, restart_deployment, scale_deployment]

INCIDENT_RESPONSE_PROMPT = """You are a Senior SRE/DevOps Engineer. Investigate the incident autonomously.

When investigating:
1. First gather data (logs, metrics, pod status)
2. Determine root cause
3. Try to resolve automatically if safe (restart, scale up)
4. If manual intervention is required, escalate with detailed context

Never do automatically:
- Changes to production databases
- Rollback of deployment without explicit instruction
- Scaling to > 10 replicas
- Deletion of resources"""

from langgraph.prebuilt import create_react_agent

incident_agent = create_react_agent(
    llm.bind_tools(incident_tools),
    tools=incident_tools,
    state_modifier=INCIDENT_RESPONSE_PROMPT,
)

Log Analysis Agent

class LogAnalyzer:

    async def analyze_error_pattern(
        self,
        service: str,
        time_range: str = "1h",
    ) -> dict:
        """Analyze error patterns in logs"""

        # Get and cluster errors
        error_logs = await loki_client.query_errors(service, time_range)
        clustered = self.cluster_errors(error_logs)

        # LLM analyzes patterns
        analysis = await llm.ainvoke(f"""Analyze error patterns:

Top errors (clusters):
{json.dumps(clustered[:10], ensure_ascii=False, indent=2)}

Time pattern: {self.get_time_pattern(error_logs)}

Determine:
1. Root cause of most frequent errors
2. Anomalous patterns (sudden spikes, cyclicity)
3. Remediation recommendations""")

        return {
            "clusters": clustered,
            "analysis": analysis.content,
            "anomalies": self.detect_anomalies(error_logs),
        }

    def cluster_errors(self, logs: list[dict]) -> list[dict]:
        """Simple clustering by error fingerprint"""
        from collections import Counter
        fingerprints = Counter()
        examples = {}

        for log in logs:
            # Normalize error (remove dynamic parts)
            fingerprint = re.sub(r'\b\d+\b', 'N', log.get("message", ""))
            fingerprint = re.sub(r'[0-9a-f]{8}-[0-9a-f-]{23}', 'UUID', fingerprint)
            fingerprints[fingerprint] += 1
            if fingerprint not in examples:
                examples[fingerprint] = log["message"]

        return [
            {"fingerprint": fp[:100], "count": count, "example": examples[fp]}
            for fp, count in fingerprints.most_common(20)
        ]

IaC Generator

class InfrastructureCodeGenerator:

    async def generate_terraform(
        self,
        infrastructure_description: str,
        cloud_provider: str = "aws",
        existing_modules: list[str] = None,
    ) -> str:
        """Generate Terraform configuration"""

        modules_context = f"\nAvailable modules: {existing_modules}" if existing_modules else ""

        response = await llm.ainvoke(f"""Generate Terraform configuration for:
{infrastructure_description}

Provider: {cloud_provider}
Requirements:
- Use latest stable provider versions
- Follow best practices: don't hardcode credentials, use variables and outputs
- Add tags for cost allocation
- Include basic security groups / IAM policies
{modules_context}

Return full HCL code with comments.""")

        return response.content

    async def generate_ansible_playbook(
        self,
        task_description: str,
        target_os: str = "ubuntu",
        idempotency_required: bool = True,
    ) -> str:
        """Generate Ansible playbook"""

        response = await llm.ainvoke(f"""Generate Ansible playbook for:
{task_description}

Target OS: {target_os}
Idempotency: {'required — all tasks must be idempotent' if idempotency_required else 'preferred'}

Requirements:
- Use ansible-lint best practices
- Handlers for services
- Check before/after if applicable
- Verifiable — add verify tasks

Return YAML playbook.""")

        return response.content

CI/CD Pipeline Generator

async def generate_github_actions_pipeline(
    project_type: str,  # "python-fastapi", "node-react", "go"
    deployment_target: str,  # "kubernetes", "lambda", "ecs"
    requirements: list[str],  # ["tests", "security-scan", "docker", "terraform"]
) -> str:

    response = await llm.ainvoke(f"""Generate GitHub Actions workflow for:
Project type: {project_type}
Deployment: {deployment_target}
Requirements: {requirements}

Include:
- Parallel jobs where possible
- Dependency caching
- Correct conditions (push main → deploy prod, PR → tests only)
- Environment protection rules for production
- Notify on failure

Return full YAML workflow.""")

    return response.content

Practical Case: Startup with 2 DevOps for 15 Developers

From our practice: a client had 2 DevOps engineers, 40+ microservices, night shifts exhausted the team. L1 incidents (OOMKilled, high load, slow queries) took 60% of on-call time.

We implemented an AI DevOps First-Responder:

• Processes PagerDuty alerts autonomously
• Collects diagnostic data (logs, metrics, k8s state)
• Executes safe automatic actions (restart, scale up)
• For complex cases: wakes the engineer with full context instead of a raw alert

Results:

• L1 incidents closed autonomously: 61%
• Average time to wake engineer at night: reduced by 58%
• Mean Time to Recovery (MTTR): 45 min → 18 min (2.5x reduction)
• DevOps focus: architecture, optimization, not routine restarts
• Night alerts: -63%
• On-call operational cost savings: up to 60%

According to the client's DevOps Lead, the AI DevOps engineer reduced night alerts by 63%, transforming the team's work.

IaC generation: 180 PRs with Terraform/Ansible code in 3 months, 91% accepted without major revisions.

Why the AI DevOps Engineer Does Not Replace Humans?

The AI DevOps engineer does not replace humans. It takes over routine L1 tasks: restarting pods, collecting diagnostics, generating code. Engineers focus on architecture, optimization, and complex incidents. This approach increases team efficiency and reduces burnout. The Kubernetes AI agent and AI for SRE work alongside people, not instead of them.

What Is Included in Developing a Digital DevOps Engineer?

We guarantee each module undergoes code review and testing in an isolated environment before deployment.

Comparison: Traditional On-Call vs AI DevOps

The AI DevOps engineer does not replace humans but takes over routine tasks, allowing engineers to focus on complex problems.

How Is the Development Process Organized and How Long Does It Take?

1. Audit current infrastructure and on-call processes
2. Design agent architecture and integrations
3. Develop and configure each module
4. Integrate with existing tools (PagerDuty, Grafana, K8s)
5. Test in staging environment
6. Deploy to production and train the team

Timeline: 6 to 10 weeks depending on the number of modules and integration complexity. Cost is calculated individually based on audit results.

Contact us for a project assessment. Order a turnkey AI DevOps engineer development — get a digital employee that saves budget and accelerates incident response. Get a consultation on implementing an AI agent in your infrastructure.

LLM Development: Fine-Tuning, RAG, Agents, and Production Deployment

Using GPT‑4 or Claude 3.5 Sonnet through a public API is not a solution — it's just a tool. When the requirement is to "make it like ChatGPT, but on our data," there is a real engineering challenge behind it: from prompt engineering to training a 70B model on your own infrastructure. End-to-end LLM solution development is a complex stack, and we have been doing it for over 5 years. During this time, we have completed over 20 projects in generative AI: from RAG systems for legal departments to custom support agents. Where exactly your task falls depends on data, latency requirements, budget, and how critical confidentiality is.

A typical situation: the client has already tried ChatGPT, but results are unstable — sometimes accurate, sometimes hallucinating. Or they need integration into a corporate portal while complying with security policies. Let's break down each layer of the stack in detail — from RAG to production deployment.

Why Do RAG Systems Break and How to Fix It?

RAG (Retrieval-Augmented Generation) looks simple: find relevant documents, put them in context, get an answer. In practice, it fails in several places.

Chunking without overlap. Classic mistake: chunk_size=512, overlap=0. If the answer lies across two chunks, retrieval won't find either with sufficient confidence. Solution: overlap 15–25% of chunk_size, or better yet, sentence-aware splitting with spaCy or NLTK instead of naive character splitting.

Poor embedder. text-embedding-ada-002 is good for general use, but on legal or medical texts, specialized models like E5-large-v2, BGE-M3, or fine-tuned sentence-transformers on domain data outperform it. Recall@5 differences can be 15–25%.

No re-ranking. Vector search optimizes for speed, not relevance. A cross-encoder re-ranker (ms-marco-MiniLM-L-6-v2, bge-reranker-large) after initial retrieval improves top-3 accuracy with acceptable latency (+50–150ms). This is often more impactful than improving the embedding model.

Hybrid search. Dense vectors alone work poorly on exact queries: names, SKUs, codes. BM25 (sparse) finds exact matches but misses semantics. Hybrid via RRF (Reciprocal Rank Fusion) is the optimal compromise. Qdrant, Weaviate, and pgvector 0.7+ support hybrid search natively.

When to Fine-Tune Instead of Prompt Engineering?

Prompt engineering solves ~70% of LLM adaptation tasks for a domain. The remaining 30% require fine-tuning. Three indicators: the model ignores a specific output format even with detailed prompting; the task requires deep knowledge of specialized vocabulary (medicine, law); you need to significantly reduce token costs by replacing a large model with a smaller specialized one.

LoRA and QLoRA are the standard for SFT. LoRA adds trainable low-rank matrices to attention layers. A typical configuration for Llama-3 8B: r=64, lora_alpha=128, target_modules=["q_proj","v_proj","k_proj","o_proj"] yields ~0.8% trainable parameters, training on one A100 40GB. QLoRA adds 4-bit quantization (NF4) and allows fine-tuning 70B models on two A100 40GB, though speed drops by half compared to bf16.

DPO instead of RLHF. Direct Preference Optimization requires only (chosen, rejected) pairs, not scalar reward signals. DPOTrainer from the trl library (Hugging Face) implements it in a few dozen lines.

Common mistake. A dataset of 500 examples, 5 epochs, validation loss 0.8 — seems fine. But on test, the model degrades on general instructions. Cause: catastrophic forgetting. Solution: add 10–20% general instruction-following examples (Alpaca, FLAN) to the training set to preserve original capabilities.

How to Choose a Base Model: 8B or 70B?

For most tasks, fine-tuning an 8B model is sufficient. 70B is needed when deep reasoning is required or the 8B baseline does not reach the required quality even after fine-tuning. Inference cost for Llama-3 8B via vLLM on A100 is efficient; the exact cost depends on volume.

What Does PagedAttention Bring to Production?

vLLM is the first choice for serving open-source models. PagedAttention is the key technical innovation: KV-cache is managed like virtual memory in an OS, without fragmentation. This yields 2–4x higher throughput compared to naive HuggingFace Transformers inference. The vLLM documentation confirms that continuous batching and PagedAttention are the standard for high-load LLM services.

Typical numbers on A100 80GB for Llama-3 8B (bf16): 400–600 req/s, P50 latency 200–400ms, P99 latency 600–900ms at concurrency 64. For 70B on two A100 with tensor parallelism: 80–120 req/s, P99 latency 1.5–2.5s. AWQ or GPTQ quantization reduces memory consumption by 2x with quality loss within 1–3%.

Multi-Agent Systems

Agents are LLMs with access to tools: search, code execution, API calls, database interaction. Common patterns:

• ReAct (Reason + Act): the model reasons → chooses a tool → observes the result → reasons again. LangChain and LlamaIndex implement it out of the box.
• Multi-agent orchestration: multiple specialized agents with a coordinator on top. Example: coordinator → researcher (search + summarization) → coder (code generation and execution) → critic (verification). Tools: AutoGen (Microsoft), CrewAI, custom implementation on LangGraph.

In production, agent systems are non-deterministic. Essential: guardrails, step limits, logging of each step, human-in-the-loop for critical actions.

How We Work: Stages, Timeline, Deliverables

What Is Included

We deliver:

• Technical documentation (model card, configs, deployment instructions)
• Access to infrastructure (code repository, trained weights)
• 1 month of post-deployment support (consultations, bug fixes)
• Customer team training (2–3 sessions on system operation)

Timeline: basic RAG prototype — 1–2 weeks. Fine-tuning with customer data — 3–6 weeks (including data preparation). Production system with monitoring and retraining — 2–4 months. Cost is calculated individually based on data volume, model complexity, and infrastructure requirements.

We guarantee the quality of the final model with performance benchmarks and ongoing monitoring. Our engineers have hands‑on experience with dozens of production LLM systems.

Want to evaluate your project? Leave a request — we will prepare a preliminary summary within 1–2 business days. Or get a consultation on choosing the approach: RAG, fine-tuning, or hybrid — we will tell you what works best for you. Contact us to discuss your LLM development needs. Schedule a free consultation today.