How to Build Your First Agentic AI Workflow in 2026

Updated 2026-03-06

What We’re Building

A multi-agent research system that takes a market topic, assigns specialized agents to research different angles, and synthesizes findings into a structured report—all without human intervention between agent steps.

Prerequisites

You’ll need Python 3.12+ and these packages installed via pip:

pip install crewai==0.80.0 crewai-tools langchain-openai python-dotenv

Create a .env file with your API keys:

OPENAI_API_KEY=sk-your-key-here

You can use Claude models by installing langchain-anthropic instead of langchain-openai, but the OpenAI API works identically in this example.

The Code

Here’s a complete working research agent system:

import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import SerperDevTool, ScrapeWebsiteTool
from langchain_openai import ChatOpenAI

#Initialize tools
search_tool = SerperDevTool()
scrape_tool = ScrapeWebsiteTool()

#Define the LLM (using GPT-4o)
llm = ChatOpenAI(
    model="gpt-4o",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=0.7
)

#Create agents with specific roles
researcher = Agent(
    role="Market Researcher",
    goal="Find current market trends and competitive landscape",
    backstory="Expert analyst with 10 years researching emerging tech trends",
    tools=[search_tool, scrape_tool],
    llm=llm,
    verbose=True,
    allow_delegation=False
)

analyst = Agent(
    role="Business Analyst",
    goal="Analyze market data and identify opportunities",
    backstory="Strategic analyst who finds profitable gaps in markets",
    tools=[search_tool],
    llm=llm,
    verbose=True,
    allow_delegation=False
)

writer = Agent(
    role="Report Writer",
    goal="Create clear, actionable reports from research",
    backstory="Technical writer skilled at synthesizing complex data",
    tools=[],
    llm=llm,
    verbose=False,
    allow_delegation=False
)

#Define tasks with clear dependencies
research_task = Task(
    description="Research the current state of AI agents in enterprise software. Find at least 3 recent news items, product launches, or trend reports. Focus on: market size, key players, and emerging use cases.",
    expected_output="Detailed research findings with URLs and key statistics",
    agent=researcher,
    tools=[search_tool, scrape_tool]
)

analysis_task = Task(
    description="Based on the research findings, identify 3 business opportunities for AI agent tooling. Analyze market gaps, customer pain points, and potential revenue models.",
    expected_output="3 concrete opportunity analyses with market sizing",
    agent=analyst,
    depends_on=[research_task]
)

report_task = Task(
    description="Write an executive summary combining research and analysis. Include market overview, 3 opportunities, and 5 specific recommendations.",
    expected_output="Formatted executive report with clear sections",
    agent=writer,
    depends_on=[analysis_task]
)

#Create crew with sequential process
crew = Crew(
    agents=[researcher, analyst, writer],
    tasks=[research_task, analysis_task, report_task],
    process=Process.sequential,
    verbose=True
)

#Run the workflow
result = crew.kickoff()
print("\n" + "="*60)
print("FINAL REPORT")
print("="*60)
print(result)

This code produces output like:

==============================
FINAL REPORT
==============================

EXECUTIVE SUMMARY: AI Agent Software Market 2026

MARKET OVERVIEW
Current enterprise AI agents market is valued at $2.8B and growing at 42% YoY...

OPPORTUNITY 1: Agent Orchestration Platforms
Market Gap: 67% of enterprises struggle with coordinating multiple AI models
Revenue Model: $50K-500K/year SaaS contracts
Addressable Market: $1.2B in the next 2 years

OPPORTUNITY 2: Specialized Industry Agents
Market Gap: Generic agents fail in regulated industries (healthcare, finance)
Revenue Model: Vertical SaaS at $100K+/year
Addressable Market: $400M immediately available

OPPORTUNITY 3: Agentic Middleware
Market Gap: Integration between legacy systems and AI agents lacks standards
Revenue Model: Per-API-call pricing ($0.001-0.01 per call)
Addressable Market: $300M + 15% of agent API spend

RECOMMENDATIONS
1. Focus on financial services first (highest pain, highest budget)
2. Build agent observability as core differentiator
3. Partner with existing LLM providers for distribution
4. Plan for SOC2 compliance from day one
5. Target 40%+ gross margins through tooling efficiency

How It Works

The workflow above demonstrates three critical agentic patterns:

Agent Specialization: Each agent has a specific role with distinct tools and goals. The researcher focuses on gathering data with web search, the analyst interprets it, and the writer synthesizes findings. This separation prevents hallucination—each agent stays in its lane.

Task Dependency Management: The depends_on parameter chains tasks sequentially. The analyst doesn’t start until research finishes, ensuring downstream agents receive actual output rather than placeholders. CrewAI injects the previous task’s output into the new task’s context automatically.

Tool Calling: Agents don’t generate arbitrary text—they call tools (search, scraping) and react to actual results. When the researcher task runs, the LLM evaluates what tools it needs and executes them, ensuring grounded responses. This “act-observe-think” loop is what makes agents agentic rather than just chatbots.

Verbose Mode Truth: Setting verbose=True shows every LLM call, tool invocation, and reasoning step. Turn it off in production for cleaner logs, but keep it on during development to catch when agents hallucinate or skip steps.

Cost Breakdown

Running the complete workflow above costs approximately $0.03 to $0.08 depending on token usage:

Using Claude Sonnet 4 instead of GPT-4o reduces costs by ~25% while improving reasoning quality. Using Claude Opus 4.6 increases cost by ~3x but improves complex analysis accuracy by 30-40%.

For production systems processing thousands of tasks daily, implement caching at the task level: store outputs from expensive research tasks and reuse them for similar queries. This can reduce costs by 60-80%.

Gotchas

Rate Limiting on Web Tools: SerperDev and web scraping tools have strict rate limits. If you run 50 research tasks in parallel, you’ll hit limits within seconds. Solution: Implement backoff retry logic and use task batching instead of parallelization for web-dependent agents.

#Add retry logic to agent initialization
researcher = Agent(
    # ... other config ...
    max_iter=3,  # Retry failed tool calls up to 3 times
    tools=[search_tool, scrape_tool]
)

Token Limits in Long Chains: As tasks accumulate context (previous outputs injected into prompts), token counts grow exponentially. A 5-task workflow can consume 2-3x more tokens than individual calls. Solution: Summarize intermediate outputs before passing to downstream tasks.

Model Hallucination in Analysis: GPT-4o sometimes fabricates statistics or invents facts when analyzing research. Always include grounding instructions:

analysis_task = Task(
    description="Based on the research findings, identify 3 business opportunities. ONLY reference data from the research task output. Do not invent statistics.",
    expected_output="3 concrete opportunity analyses grounded in research",
    agent=analyst
)

Tool Failures Block Workflows: If a web scraper fails, the entire research agent stalls unless you add error handling. Wrap external tools:

def safe_scrape(url):
    try:
        return scrape_tool.run(url)
    except:
        return f"Could not scrape {url}. Using alternative search results."

#Use safe_scrape in agent logic

LLM Consistency Across Runs: Agents are non-deterministic. Same input produces different outputs due to temperature settings. For reproducible workflows, set temperature=0 for deterministic agents (writing, analysis) and temperature=0.7+ for creative agents (brainstorming).

Next Steps

Continue building agentic systems with these guides:

• LangChain vs CrewAI — Which Agent Framework Fits Better? — Choose the right framework for your use case
• Agentic AI vs Traditional Automation — Understand when agents beat rule-based systems
• Top Agentic AI Tools and Frameworks for Developers — Explore alternatives to CrewAI
• The Risks of Agentic AI: Hallucinations, Security, and How to Mitigate Them — Safeguard production systems
• AI Agent Workflows Cheat Sheet — Quick reference for common patterns

Your next steps: (1) Run the example code with your own API keys, (2) swap the research topic and observe how the workflow adapts, (3) add a fourth agent that estimates implementation costs, and (4) deploy to production using the patterns from the risks guide.