Agentic AI Workflows

The first wave of AI tools were assistants — you asked a question, they answered. The second wave is different: AI agents that can plan and execute complex tasks autonomously, using tools, browsing the web, writing and running code, and calling other agents to help.

In 2026, agentic AI is no longer experimental. Claude Code, Gemini CLI, GitHub Copilot Coding Agents, and OpenAI's Codex CLI are all production-ready tools that can autonomously handle software engineering tasks that previously required hours of human work.

This guide explains agentic AI from first principles, shows you the most important patterns, and helps you decide when and how to use it.

What You'll Learn

• What agentic AI is and how it differs from conversational AI
• The orchestrator/subagent architecture
• Key tools for agentic AI in 2026
• Multi-agent systems with CrewAI and LangGraph
• Real examples of agentic workflows
• When to use agentic AI (and when not to)

What Is Agentic AI?

Conversational AI (like chatting with ChatGPT) works in a single turn: you provide input, the model generates output. That is it.

Agentic AI works in loops. The agent:

1. Receives a goal, not just a single question
2. Plans the steps needed to achieve the goal
3. Takes actions (calls tools, writes files, runs code, searches the web)
4. Observes the results of those actions
5. Adjusts the plan based on results
6. Continues until the goal is achieved

This loop — Observe → Plan → Act → Observe — is the core of agentic AI. The agent is not just answering questions; it is doing work.

Example of agentic behavior:

• Non-agentic: "Write a function to parse CSV files"
• Agentic: "Analyze the CSV files in the /data folder, identify common patterns, write a flexible parser, add unit tests, and create documentation" — the agent does all of this autonomously, making decisions along the way

🇮🇳 India Note: Indian IT services companies — TCS, Infosys, Wipro, HCL — are all building agentic AI systems for their clients. Entry-level developers who understand agentic AI patterns are in extremely high demand. TCS alone is upskilling 42,000+ freshers in FY26 with AI skills including agentic systems.

The Orchestrator/Subagent Pattern

The most important architectural pattern in multi-agent AI is the orchestrator/subagent model.

Orchestrator Agent: The high-level coordinator. It receives the overall goal, breaks it into subtasks, and delegates to subagents. It tracks progress and synthesizes results.

Subagent: Executes a specific subtask. Typically has access to specific tools. Returns results to the orchestrator.

Example — Code Review System:

Orchestrator prompt: "Review this pull request for correctness, security, and performance"

Subagent 1 (Correctness Reviewer): Analyzes logic and tests Subagent 2 (Security Scanner): Checks for vulnerabilities Subagent 3 (Performance Analyzer): Identifies bottlenecks

Orchestrator combines their reports into a final review.

This is more effective than asking one agent to do everything because each subagent can focus on its specific domain, use specialized tools, and operate in parallel.

Key Agentic AI Tools in 2026

Claude Code (Anthropic)

Claude Code is the most capable terminal AI agent for software development. It can:

• Read and write files in your project
• Run terminal commands
• Execute tests and interpret results
• Search the codebase
• Call MCP servers for external tools
• Spawn subagent instances

Multi-agent with Claude Code:

# Orchestrator session
claude "Set up a subagent to handle the frontend refactoring while you 
handle the API changes. Coordinate to ensure the interfaces match."

Claude Code can operate as both orchestrator and subagent in the same project.

Requires: Claude Pro subscription ($20/month USD)

Gemini CLI

Google's terminal agent. Free for 1,000 requests/day with a Gmail account.

# Install
npm install -g @google/gemini-cli

# Authenticate
gemini auth

# Agentic task
gemini "Analyze this project's test coverage, identify uncovered paths, and write tests for the 5 highest-risk uncovered functions"

Gemini CLI has tools for: file system, web search, code execution, and Google services.

💰 Free Deal: Gemini CLI's 1,000 free requests/day is the most generous free tier for agentic AI. Combined with Jio's Gemini Pro promotion, this makes Google's stack the most accessible agentic AI setup for Indian developers.

GitHub Copilot Coding Agents

GitHub Copilot can now autonomously resolve GitHub issues. You assign an issue to Copilot and it creates a branch, makes changes, runs tests, and opens a PR — all without human intervention.

How to use:

1. Create a GitHub Issue with clear acceptance criteria
2. Click "Assign to Copilot" (in the issue sidebar)
3. Copilot creates a branch, works on the solution
4. Reviews the PR it opens when done

This is free for all GitHub users. GitHub Student Pack users get additional quota.

CrewAI (Multi-Agent Framework)

CrewAI is a Python framework for building teams of AI agents that collaborate.

from crewai import Agent, Task, Crew

researcher = Agent(
    role="Research Analyst",
    goal="Find the top 5 competitors of a given company",
    backstory="You are an expert market researcher",
    tools=[search_tool]
)

writer = Agent(
    role="Content Writer",
    goal="Write a competitive analysis report",
    backstory="You write clear, actionable business reports"
)

research_task = Task(
    description="Research the top 5 competitors of {company}",
    agent=researcher
)

writing_task = Task(
    description="Write a 500-word competitive analysis based on research",
    agent=writer
)

crew = Crew(agents=[researcher, writer], tasks=[research_task, writing_task])
result = crew.kickoff(inputs={"company": "Zomato"})

This creates two AI agents that work together — one researches, one writes — producing a complete competitive analysis autonomously.

LangGraph (State Machine Agents)

LangGraph is for complex, stateful agent workflows with explicit control flow:

from langgraph.graph import StateGraph

# Build an agent as a state machine
workflow = StateGraph(AgentState)
workflow.add_node("plan", plan_step)
workflow.add_node("execute", execute_step)
workflow.add_node("evaluate", evaluate_step)
workflow.add_conditional_edges("evaluate", decide_next_step)

LangGraph is best for production systems where you need deterministic, auditable agent behavior.

Real Agentic Workflow Examples

Example 1: Automated Code Migration

Goal: Migrate 50 React class components to functional components with hooks.

Agentic approach:

1. Orchestrator scans codebase, finds all class components
2. For each component, spawn subagent with the component code
3. Each subagent converts the component and writes tests
4. Orchestrator validates all tests pass, opens single PR

Tools: Claude Code + GitHub Copilot agents

Example 2: Competitive Research Report

Goal: Research 5 competitors and write a 2,000-word analysis.

Agentic approach:

1. Orchestrator plans research structure
2. Research subagents (5 in parallel) search the web for each competitor
3. Analysis subagent synthesizes findings
4. Writing subagent drafts the report
5. Editing subagent improves clarity and tone

Tools: CrewAI + Perplexity search tool

Example 3: GST Reconciliation

Goal: Match 500 purchase invoices against GST portal data.

Agentic approach:

1. Data extraction agent reads invoice PDFs
2. GST portal agent fetches matching records via API
3. Reconciliation agent compares and flags mismatches
4. Summary agent writes the exception report

Tools: n8n with AI nodes, Claude API

When to Use Agentic AI

Use agentic AI when:

• Task involves multiple distinct steps
• Work can be parallelized across specialized agents
• Task is well-defined (acceptance criteria are clear)
• Volume is too high for human-in-the-loop

Avoid agentic AI when:

• Task requires judgment calls or value decisions
• Stakes are high enough that errors have serious consequences
• You do not have a way to validate the output

Official Resources

• Claude Code Multi-Agent Docs — Official Claude Code agent documentation
• Gemini CLI Repository — Open-source Gemini CLI
• CrewAI Documentation — Multi-agent framework
• LangGraph Documentation — State machine agents
• GitHub Copilot Coding Agent — Autonomous issue resolution