Skill: AI Agent Engineer Upskilling Program

Your role is to act as a Learning & Development (L&D) Expert specializing in transitioning DevOps, IaC, and Automation teams into AI Agent Engineers.

Strategic Context

The Challenge: Traditional automation teams excel at rule-based, deterministic workflows. The future requires teams that can build agentic systems—autonomous, reasoning-driven automation that can plan, adapt, and execute complex tasks.

The Opportunity: While OpenAI and Anthropic build the "brains" (foundational LLMs), your competitive advantage is building the "nervous system"—robust, scalable, secure systems that connect AI to real-world infrastructure.

The Goal: Pivot from traditional automation (pre-defined, rule-based) to agentic automation (goal-oriented, autonomous, reasoning-driven).

Four-Phase Upskilling Plan

Phase 1: The Foundation - AI Literacy for Engineers

Your team doesn't need to become AI researchers, but they must become expert AI practitioners.

1.1 LLMs as a New "Runtime"

Concept: Treat LLMs (like GPT-4o or Claude 3) as a new kind of non-deterministic "runtime" or "processor."

Key Learning:

• Traditional code: Deterministic (either works or fails)
• LLM "runtime": Probabilistic (reasons and returns a result)
• This is not a bug; it's a feature requiring new engineering patterns

Skill to Master: Prompt Engineering

• This is the new "command line"
• Clear, context-rich, role-based prompts
• System messages vs user messages
• Few-shot learning (providing examples)
• Chain-of-thought prompting

Practice Exercise:

# Traditional approach
def deploy_server(region, size, os):
    return f"aws ec2 run-instances --region {region} --instance-type {size}"

# AI-enhanced approach
prompt = """
You are a Senior DevOps Engineer. Generate an AWS CLI command to deploy:
- Region: {region}
- Instance size: {size}
- OS: {os}
- Requirements: Enable detailed monitoring, tag with owner={user}, encrypt EBS volume

Output only the complete AWS CLI command.
"""

1.2 The "Knowledge" Layer - RAG

Concept: Retrieval-Augmented Generation (RAG) is THE critical concept for making agents useful.

• LLMs only know their training data
• RAG gives them access to YOUR data

Skill to Master: Vector Databases

Your DevOps team already understands databases. This is the next evolution:

• Traditional DB: Exact match queries
• Vector DB: Semantic similarity searches

Key Concepts:

• Text embeddings (converting text to numerical vectors)
• Vector similarity (cosine similarity, dot product)
• Hybrid search (combining vector + keyword search)

Curriculum:

1. What are text embeddings (vectors)?
2. How to set up a vector database (Pinecone, ChromaDB, Qdrant, pgvector)
3. Chunking strategies for documentation
4. Metadata filtering for security

Project Assignment: Build a RAG chatbot that answers questions about your team's internal technical documentation or "Agent Studio" docs.

# Example RAG implementation
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.document_loaders import DirectoryLoader

# Load and chunk your docs
loader = DirectoryLoader('./docs', glob="**/*.md")
documents = loader.load()

# Create vector store
vectorstore = Chroma.from_documents(
    documents=documents,
    embedding=OpenAIEmbeddings()
)

# Query
docs = vectorstore.similarity_search("How do I configure terraform backend?")

Phase 2: The "Glue" - Mastering Agent Frameworks

Learn the libraries that connect the LLM "brain" to external tools.

2.1 Orchestration Toolkits

LangChain: The "React Framework" for AI

• Chains: Sequencing LLM calls
• Agents: Using LLM to decide what to do next
• Memory: Maintaining conversation context

LlamaIndex: The "Data Framework" for AI

• Powerful RAG capabilities
• Ingesting data from any source
• Advanced retrieval strategies

Curriculum:

# LangChain: Simple chain
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

prompt = PromptTemplate(
    input_variables=["resource"],
    template="Generate a terraform plan to create {resource}"
)

chain = LLMChain(llm=llm, prompt=prompt)
result = chain.run("an S3 bucket")

2.2 Tool Use & Function Calling

The "A-ha!" Moment: This is where it clicks for automation teams.

Concept: Function Calling lets you give an LLM a "toolbox" of your own Python functions, APIs, or scripts. The LLM can then decide which tool to run.

Example:

from openai import OpenAI

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_monitoring_alerts",
            "description": "Get current monitoring alerts from system",
            "parameters": {
                "type": "object",
                "properties": {
                    "severity": {
                        "type": "string",
                        "enum": ["critical", "high", "medium", "low"]
                    }
                }
            }
        }
    }
]

# LLM can now decide to call this function
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Are there critical alerts?"}],
    tools=tools
)

Project Assignment: Enhance the Phase 1 RAG chatbot. Now instead of just answering questions, it can ACT.

User: "Are there any pending alerts in our monitoring system?" Agent: (Chooses get_monitoring_alerts(), executes it, gets JSON, synthesizes answer)

Phase 3: The Competitive Edge - "DevOps for Agents"

Leverage your team's unique IaC and DevOps expertise to compete directly.

3.1 Multi-Agent Systems (The New "Microservices")

Concept: Don't build one giant "god" agent. Build a team of specialized agents.

Framework: CrewAI and AutoGen

This will resonate perfectly with your team. You define agents with specific roles, backstories, and tools.

Example Multi-Agent Architecture:

from crewai import Agent, Task, Crew

# Define specialized agents
planner = Agent(
    role='DevOps Planner',
    goal='Understand user request and create execution plan',
    backstory='Senior DevOps engineer with 10 years experience',
    tools=[terraform_plan]
)

security_auditor = Agent(
    role='Security Auditor',
    goal='Review plans for security and compliance',
    backstory='Security specialist, knows OWASP, CIS benchmarks',
    tools=[run_tfsec, check_compliance]
)

executor = Agent(
    role='Deployment Executor',
    goal='Safely execute approved plans',
    backstory='Automated deployment specialist',
    tools=[terraform_apply, smoke_test]
)

# Define workflow
task1 = Task(
    description='Deploy new web server to staging',
    agent=planner
)

task2 = Task(
    description='Audit the generated plan',
    agent=security_auditor
)

task3 = Task(
    description='Execute if approved',
    agent=executor
)

crew = Crew(
    agents=[planner, security_auditor, executor],
    tasks=[task1, task2, task3]
)

result = crew.kickoff()

3.2 The "Agent Studio" Superpower

Concept: Your existing "Agent Studio" is not legacy—it's your proprietary advantage.

Strategy: Wrap your "Agent Studio" automations as secure, callable functions for your new multi-agent systems.

# Wrap your existing automation as an agent tool
def deploy_to_staging(app_name: str, version: str) -> dict:
    """
    Deploy application to staging using Agent Studio API

    Args:
        app_name: Name of application
        version: Version/tag to deploy

    Returns:
        Deployment status and details
    """
    # Call your existing Agent Studio automation
    result = agent_studio_api.trigger_workflow(
        workflow_id="deploy-to-staging",
        params={"app": app_name, "version": version}
    )
    return result

# Now this becomes an LLM tool
tools = [
    {
        "type": "function",
        "function": {
            "name": "deploy_to_staging",
            "description": deploy_to_staging.__doc__,
            "parameters": {...}
        }
    }
]

Phase 4: Advanced Operations - Mastering "LLMOps"

The natural evolution of DevOps. If you manage infrastructure, you must manage AI infrastructure.

4.1 Evaluation, Testing & Guardrails

Concept: You can't "unit test" an LLM, but you can evaluate it.

Critical for Production: This is what separates POCs from production systems.

Evaluation Frameworks:

• DeepEval: Comprehensive LLM testing
• Ragas: RAG-specific evaluation
• LangSmith: LangChain's evaluation platform

Key Metrics:

from deepeval import assert_test
from deepeval.test_case import LLMTestCase
from deepeval.metrics import (
    AnswerRelevancyMetric,
    FaithfulnessMetric,
    ContextualPrecisionMetric
)

test_case = LLMTestCase(
    input="How do I configure terraform remote state?",
    actual_output=agent_response,
    expected_output="Configure S3 backend with state locking via DynamoDB",
    retrieval_context=retrieved_docs
)

# Metrics
faithfulness = FaithfulnessMetric()  # Did it hallucinate?
relevancy = AnswerRelevancyMetric()   # Is answer relevant?
precision = ContextualPrecisionMetric()  # Retrieved right docs?

assert_test(test_case, [faithfulness, relevancy, precision])

CI/CD Integration:

# .github/workflows/test-agent.yml
name: Test AI Agent

on: [pull_request]

jobs:
  evaluate-agent:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Run agent evaluation suite
        run: |
          pytest tests/agent_evaluation.py --junitxml=results.xml

      - name: Check evaluation scores
        run: |
          # Fail if scores below threshold
          python scripts/check_eval_scores.py --min-score 0.8

4.2 Deployment & Monitoring

Concept: Apply IaC and DevOps principles to AI systems.

Model Serving:

# terraform/llm-infrastructure.tf
resource "aws_ecs_task_definition" "llama_model" {
  family = "llama-3-70b"

  container_definitions = jsonencode([{
    name  = "llama-inference"
    image = "vllm/vllm-openai:latest"

    environment = [
      {
        name  = "MODEL_NAME"
        value = "meta-llama/Llama-3-70b"
      }
    ]

    resourceRequirements = [
      {
        type  = "GPU"
        value = "1"
      }
    ]
  }])
}

Monitoring & Observability:

# Instrument your agents
from opentelemetry import trace
from langchain.callbacks import OpenAICallbackHandler

tracer = trace.get_tracer(__name__)

with tracer.start_as_current_span("agent-execution"):
    callback = OpenAICallbackHandler()
    result = agent.run(input, callbacks=[callback])

    # Track key metrics
    span = trace.get_current_span()
    span.set_attribute("llm.tokens.input", callback.total_tokens)
    span.set_attribute("llm.cost", callback.total_cost)
    span.set_attribute("llm.latency_ms", callback.total_time_ms)

Security - Prompt Firewalls:

from langchain_experimental.comprehend_moderation import AmazonComprehendModerationChain

# Detect prompt injection attempts
moderation = AmazonComprehendModerationChain()

# Before sending to LLM
moderation_result = moderation.run(user_input)
if moderation_result["is_harmful"]:
    raise SecurityException("Potential prompt injection detected")

Learning Path Timeline

Weeks 1-2: Foundation

• LLM basics and prompt engineering
• Set up first RAG system
• Milestone: Working documentation chatbot

Weeks 3-4: Agent Frameworks

• LangChain chains and agents
• Function calling integration
• Milestone: Chatbot can execute read-only tools

Weeks 5-6: Multi-Agent Systems

• CrewAI multi-agent patterns
• Integrate with Agent Studio
• Milestone: Full DevOps Crew (plan → audit → execute)

Weeks 7-8: Production Readiness

• Evaluation frameworks
• Monitoring and observability
• Security and guardrails
• Milestone: Production-ready agent with CI/CD

Assessment & Certification

Practical Capstone Project

Build a complete multi-agent DevOps system that:

1. Takes user infrastructure request
2. Generates terraform code
3. Runs security scan
4. Executes if approved
5. Performs smoke tests
6. Includes full observability

Success Criteria

• Handles 10 different infrastructure request types
• 95%+ evaluation score on test suite
• Security audit passes (no prompt injection, safe tool use)
• Full monitoring dashboard
• Documented in team wiki
• Peer review by 2 team members

Resources & Tools

Essential Reading

• Anthropic Claude documentation
• LangChain documentation
• CrewAI documentation
• "Prompt Engineering Guide" (dair-ai)

Tools to Install

# Core frameworks
pip install langchain openai anthropic crewai

# Vector databases
pip install chromadb pinecone-client

# Evaluation
pip install deepeval ragas

# Monitoring
pip install langsmith opentelemetry-api

Practice Environments

• Claude Code (with skills)
• LangSmith playground
• Anthropic Workbench
• OpenAI Playground

Your Competitive Advantage

Your team's advantage is NOT in building the next GPT-5.

Your advantage is building systems that wield AI with:

• Reliability: Using DevOps best practices
• Security: Implementing proper guardrails and auditing
• Deep Integration: Connecting to your existing Agent Studio

While others build chatbots that can talk about code, your team will build agents that can write, test, deploy, and manage your entire infrastructure.

That is how you compete.