Agentic Workflows: What They Are, How They Work, and How to Build Them

Last updated: February 2026

Agentic workflows represent the next evolution of automation. Instead of following rigid, predefined rules, AI agents can reason about goals, plan approaches, use tools, evaluate results, and adapt—handling tasks that previously required human judgment.

This guide covers what agentic workflows are, how they differ from traditional automation, the key patterns that make them work, frameworks for building them, and practical implementation strategies.

What Is an Agentic Workflow?

An agentic workflow is an AI-driven process where autonomous agents execute tasks to achieve goals with minimal human intervention. The agent:

• Reasons about what needs to be done
• Plans a sequence of actions
• Uses tools to gather information and take actions
• Evaluates results and adjusts approach
• Persists memory across interactions

The Agent Loop

Agentic workflows follow a Thought → Action → Observation loop:

1. THOUGHT: Agent reasons about the current state and goal
   "I need to find the customer's order status. I should query the orders database."

2. ACTION: Agent executes an action (tool call, API request)
   → Call orders_api.get_order(customer_id="12345")

3. OBSERVATION: Agent receives the result
   "Order #789 shipped yesterday, tracking: 1Z999..."

4. THOUGHT: Agent reasons about what to do next
   "I have the tracking info. Now I should compose a response to the customer."

5. ACTION: Continue until goal is achieved...

This loop continues until the agent determines the goal is achieved or it needs human assistance.

Agentic vs Traditional vs AI Workflows

Understanding the differences helps you choose the right approach.

When to Use Each

Traditional Automation:

• Highly predictable, repeatable processes
• No ambiguity in inputs or decisions
• Speed and cost are primary concerns
• Example: Sending invoice when order ships

AI Workflow (Non-Agentic):

• Process benefits from AI at specific steps
• Flow is still deterministic
• AI handles data extraction, classification, generation
• Example: Extract data from documents → route based on type → process

Agentic Workflow:

• Goal-oriented tasks requiring judgment
• Ambiguous inputs requiring interpretation
• Need to use multiple tools based on context
• Iterative refinement needed
• Example: "Research this company and draft an outreach email"

Key Patterns in Agentic Workflows

1. Planning

Agents break complex goals into subtasks and create execution plans.

Simple Planning:

Goal: "Book a flight from NYC to London for next Tuesday"

Agent Plan:
1. Search for flights NYC → London on [date]
2. Filter by preferences (direct, morning departure)
3. Compare prices and times
4. Select best option
5. Complete booking with stored payment info
6. Send confirmation to user

Adaptive Planning: The agent revises plans based on results:

Step 1 result: No direct flights available

Revised Plan:
1. ✓ Search for flights (no direct available)
2. Search for one-stop flights
3. Alternatively, check adjacent dates for direct options
4. Present options to user for decision
5. ...

Hierarchical Planning: For complex goals, agents create high-level plans, then decompose into detailed steps:

High-level: "Create a market analysis report"
  → Research industry trends
  → Analyze competitor landscape
  → Identify market opportunities
  → Synthesize findings into report

Detailed (for "Research industry trends"):
  → Search for recent industry reports
  → Query news APIs for last 6 months
  → Extract key statistics and trends
  → Summarize findings

2. Tool Use

Agents extend their capabilities by calling external tools.

Common Tool Categories:

How Tool Use Works:

Agent receives: "What's the current stock price of AAPL?"

Agent reasoning: "I need real-time stock data. I should use the stock_price tool."

Agent action: stock_price(symbol="AAPL")

Tool response: {"symbol": "AAPL", "price": 187.43, "change": +1.2%}

Agent response: "Apple (AAPL) is currently trading at $187.43, up 1.2% today."

Tool Selection: Agents decide which tools to use based on the task. With many tools available, agents reason about which is appropriate:

Available tools: [web_search, database_query, send_email, calendar_check, calculator]

Query: "Do I have any meetings tomorrow?"
Agent selects: calendar_check

Query: "What's 15% tip on $84?"
Agent selects: calculator

Query: "Find recent news about Tesla"
Agent selects: web_search

3. Reflection

Agents evaluate their outputs and self-correct.

Output Verification:

Agent generates: Draft email response

Reflection: "Let me verify this email:
- Does it address the customer's question? Yes
- Is the tone appropriate? Yes
- Are there any factual errors? Let me check...
- Is it concise? Could be shorter.

Revision: [Shortened version]"

Error Detection and Retry:

Agent action: api_call(endpoint="/orders")
Result: Error 500 - Server unavailable

Reflection: "The API call failed. I should:
1. Wait briefly and retry
2. If still failing, try alternative data source
3. If no alternative, inform user of issue"

Action: Wait 5 seconds, retry...

Self-Critique:

Agent generates: Analysis report

Self-critique: "Reviewing my analysis:
- Claim: 'Revenue grew 50%' - Need to verify source
- Claim: 'Market leader' - This seems unsupported
- Structure: Missing executive summary

Revision: Add sources, remove unsupported claim, add summary"

4. Multi-Agent Collaboration

Multiple specialized agents work together on complex tasks.

Agent Roles Example:

Task: "Create a marketing campaign for product launch"

Researcher Agent:
  → Analyze target market
  → Research competitor campaigns
  → Identify key messaging themes

Writer Agent:
  → Create campaign copy
  → Draft social media posts
  → Write email sequences

Critic Agent:
  → Review outputs for quality
  → Check brand consistency
  → Suggest improvements

Coordinator Agent:
  → Orchestrate workflow
  → Manage handoffs
  → Compile final deliverables

Collaboration Patterns:

5. Human-in-the-Loop

Agents escalate to humans when needed.

Escalation Triggers:

• Confidence below threshold
• High-stakes decisions
• Policy violations detected
• Ambiguous instructions
• User preferences unknown

Implementation:

Agent processing claim...

Confidence: 45% (below 70% threshold)
Reason: Unusual claim pattern, possible fraud indicators

Action: Escalate to human reviewer
  → Provide case summary
  → Highlight concerns
  → Include relevant documents
  → Await human decision

Human decision: Approve / Deny / Request more info

Agent continues: Based on human input...

6. Memory and Context

Agents maintain memory across interactions.

Memory Types:

Memory in Action:

User (Monday): "I prefer morning meetings"
→ Stored to long-term memory

User (Thursday): "Schedule a call with the team"
→ Agent recalls preference
→ Schedules for morning slot

User: "Like last time"
→ Agent retrieves episodic memory of previous meeting format

Agentic Workflow Architecture

Core Components

┌─────────────────────────────────────────────────┐
│                  AGENT CORE                      │
│  ┌───────────┐  ┌───────────┐  ┌───────────┐   │
│  │  Planner  │  │  Reasoner │  │  Executor │   │
│  └───────────┘  └───────────┘  └───────────┘   │
└─────────────────────────────────────────────────┘
         │              │              │
         ▼              ▼              ▼
┌─────────────┐  ┌─────────────┐  ┌─────────────┐
│    LLM      │  │   Memory    │  │    Tools    │
│  (GPT-4,    │  │  (Vector DB,│  │  (APIs,     │
│   Claude)   │  │   Redis)    │  │   Search)   │
└─────────────┘  └─────────────┘  └─────────────┘
         │              │              │
         ▼              ▼              ▼
┌─────────────────────────────────────────────────┐
│              ORCHESTRATION LAYER                 │
│  (Workflow engine, state management, logging)   │
└─────────────────────────────────────────────────┘
         │
         ▼
┌─────────────────────────────────────────────────┐
│           HUMAN-IN-THE-LOOP INTERFACE           │
│  (Escalations, approvals, feedback)             │
└─────────────────────────────────────────────────┘

Component Details

Agent Core:

• Planner: Decomposes goals into tasks
• Reasoner: Decides next actions based on state
• Executor: Carries out actions, processes results

LLM Layer:

• Powers reasoning, planning, and language understanding
• May use multiple models (fast model for simple tasks, powerful for complex)

Memory Layer:

• Vector database for semantic search over past context
• Key-value store for structured state
• Conversation history management

Tools Layer:

• API integrations
• Database connectors
• External service clients
• Code execution environments

Orchestration Layer:

• Manages workflow state
• Handles retries and error recovery
• Provides observability and logging
• Coordinates multi-agent systems

Frameworks for Building Agentic Workflows

LangChain / LangGraph

Best for: Python developers building custom agents

from langchain.agents import create_react_agent
from langchain_openai import ChatOpenAI

# Define tools
tools = [search_tool, calculator_tool, database_tool]

# Create agent
agent = create_react_agent(
    llm=ChatOpenAI(model="gpt-4"),
    tools=tools,
    prompt=agent_prompt
)

# Run agent
result = agent.invoke({"input": "Research competitors and summarize"})

Strengths: Flexible, large ecosystem, good for prototyping Weaknesses: Can be complex, requires coding expertise

CrewAI

Best for: Multi-agent systems with defined roles

from crewai import Agent, Task, Crew

researcher = Agent(
    role="Research Analyst",
    goal="Find comprehensive market data",
    tools=[search_tool, web_scraper]
)

writer = Agent(
    role="Content Writer",
    goal="Create compelling reports",
    tools=[writing_tool]
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task]
)

result = crew.kickoff()

Strengths: Easy multi-agent setup, role-based design Weaknesses: Less flexible than LangChain

Microsoft AutoGen

Best for: Conversational multi-agent systems

from autogen import AssistantAgent, UserProxyAgent

assistant = AssistantAgent("assistant", llm_config=llm_config)
user_proxy = UserProxyAgent("user", code_execution_config={"work_dir": "coding"})

user_proxy.initiate_chat(
    assistant,
    message="Create a plot of stock prices for AAPL"
)

Strengths: Good for code generation, conversation-based Weaknesses: Microsoft ecosystem focused

Semantic Kernel

Best for: Enterprise .NET/Python applications

Strengths: Enterprise integration, Microsoft support Weaknesses: Smaller community than LangChain

Workflow Platforms with AI Agents

Best for: Business users, integration-heavy workflows

Platforms like Miniloop combine agentic AI with workflow automation:

• Visual workflow builders
• Pre-built integrations
• Human-in-the-loop built in
• Enterprise security and governance

Strengths: No coding required, business-ready Weaknesses: Less flexibility than code-first approaches

Framework Comparison

Common Use Cases for Agentic Workflows

Customer Support Agent

Goal: Resolve customer inquiry autonomously

Agent capabilities:
  → Understand customer question (NLU)
  → Search knowledge base for answers
  → Query order/account systems
  → Take actions (refunds, updates, escalations)
  → Generate personalized responses

Flow:
1. Customer: "Where's my order?"
2. Agent: Query order system → "Order shipped, arriving Tuesday"
3. Customer: "Can I change the address?"
4. Agent: Check if changeable → Update address → Confirm
5. Customer: "Thanks!"
6. Agent: Close ticket, log interaction

Research and Analysis Agent

Goal: "Research [company] and create investment summary"

Agent plan:
1. Search for recent news about company
2. Pull financial data from APIs
3. Analyze competitor landscape
4. Identify risks and opportunities
5. Synthesize into structured report

Tools used:
  → Web search, SEC filings API, financial data API
  → Document analysis for annual reports
  → Comparison with industry benchmarks

Code Generation Agent

Goal: "Implement user authentication for the app"

Agent plan:
1. Understand codebase structure
2. Research authentication best practices
3. Generate implementation code
4. Write tests
5. Review for security issues
6. Create documentation

Reflection loop:
  → Generate code
  → Run tests
  → If tests fail, debug and fix
  → Self-review for security
  → Iterate until quality threshold met

Sales Outreach Agent

Goal: "Research lead and draft personalized outreach"

Agent steps:
1. Lookup company (website, LinkedIn, news)
2. Identify key decision makers
3. Research recent company events
4. Find connection points to our solution
5. Draft personalized email
6. Review against best practices
7. Queue for human approval

Human-in-loop:
  → Agent presents draft
  → Human approves, edits, or rejects
  → Agent learns from feedback

Document Processing Agent

Goal: Process incoming contract and extract key terms

Agent workflow:
1. Classify document type
2. Extract key fields (parties, dates, amounts, terms)
3. Compare against standard templates
4. Flag unusual clauses
5. Route for appropriate review
6. Update contract management system

Adaptive behavior:
  → If OCR quality low: Try alternative extraction
  → If clause unusual: Flag for legal review
  → If missing signatures: Request completion

IT Operations Agent

Goal: Respond to monitoring alert

Agent workflow:
1. Receive alert (high CPU on server-12)
2. Query monitoring system for details
3. Check recent deployments
4. Analyze logs for root cause
5. Determine if known issue with fix
6. Execute remediation or escalate

Actions available:
  → Restart service
  → Scale resources
  → Rollback deployment
  → Page on-call engineer

Implementing Agentic Workflows

Step 1: Define the Goal and Scope

Goal: What should the agent accomplish?
  → Be specific: "Resolve tier-1 support tickets"
  → Not vague: "Help customers"

Scope: What can the agent do?
  → Actions allowed: Query systems, send responses, process refunds
  → Actions prohibited: Delete accounts, access sensitive data without auth
  → Escalation triggers: High-value customers, complex issues

Step 2: Design the Tool Set

Required tools:
  → Knowledge base search (answer questions)
  → Order system API (check status, modify orders)
  → Customer database (lookup account info)
  → Email system (send responses)
  → Ticketing system (update, close, escalate)

Tool definitions:
  → Name, description, parameters, return format
  → Clear documentation for agent understanding

Step 3: Create the Agent Prompt

System prompt components:
  → Role definition: "You are a customer support agent for Acme Corp"
  → Available tools: List with descriptions
  → Guidelines: Tone, policies, limitations
  → Escalation criteria: When to involve humans
  → Output format: How to structure responses

Step 4: Implement Memory

Working memory:
  → Current conversation context
  → Active task state

Long-term memory:
  → Customer interaction history
  → Learned preferences
  → Successful resolution patterns

Step 5: Build the Orchestration

Workflow components:
  → Input processing (receive, validate)
  → Agent loop (think, act, observe)
  → Tool execution (with error handling)
  → State management (track progress)
  → Output handling (format, deliver)
  → Human escalation (when triggered)

Step 6: Add Guardrails

Safety measures:
  → Input validation (reject malicious inputs)
  → Output filtering (block inappropriate content)
  → Action limits (max API calls, spend limits)
  → Confidence thresholds (escalate uncertain decisions)
  → Audit logging (track all actions)

Step 7: Test and Iterate

Testing approach:
  → Unit test individual tools
  → Test agent on sample scenarios
  → Evaluate edge cases and failures
  → Measure success rate, accuracy, latency
  → Gather human feedback
  → Iterate on prompts, tools, guardrails

Challenges and Considerations

Reliability

Problem: Agents can hallucinate, make mistakes, or get stuck in loops.

Mitigations:

• Structured outputs with validation
• Confidence scoring with thresholds
• Maximum iteration limits
• Human review for high-stakes actions
• Extensive testing on edge cases

Cost

Problem: LLM calls for reasoning add up, especially with reflection and retries.

Mitigations:

• Use smaller models for simple tasks
• Cache frequent queries
• Limit reasoning steps
• Batch similar requests
• Monitor and optimize token usage

Latency

Problem: Multi-step reasoning takes time—seconds to minutes per task.

Mitigations:

• Parallelize independent subtasks
• Use faster models where appropriate
• Cache tool results
• Stream intermediate results to users
• Set user expectations appropriately

Observability

Problem: Debugging agent behavior is hard—"why did it do that?"

Mitigations:

• Log all reasoning steps
• Trace tool calls and results
• Build visualization of agent execution
• Enable replay of agent sessions
• Track metrics (success rate, steps taken, errors)

Security

Problem: Agents with tool access can take harmful actions.

Mitigations:

• Principle of least privilege
• Sandboxed execution environments
• Rate limiting and quotas
• Approval workflows for sensitive actions
• Regular security audits

Evaluation

Problem: How do you know if the agent is doing well?

Mitigations:

• Define success criteria upfront
• Create evaluation datasets
• Track key metrics over time
• Gather user feedback
• Compare against human baselines

FAQs About Agentic Workflows

What is an agentic workflow?

An agentic workflow is an AI-driven process where autonomous agents plan, reason, use tools, and execute tasks to achieve goals with minimal human intervention. Unlike traditional automation, agents adapt dynamically based on context and outcomes.

What is the difference between agentic workflows and traditional automation?

Traditional automation follows predefined rules and sequences. Agentic workflows use AI agents that reason, plan, adapt, and make decisions dynamically. Agents handle unexpected situations, use tools intelligently, and adjust their approach—capabilities traditional automation lacks.

What are the key patterns in agentic workflows?

Key patterns include: Planning (breaking goals into subtasks), Tool Use (calling APIs, databases, services), Reflection (evaluating outputs, self-correcting), Multi-Agent Collaboration (specialized agents working together), and Human-in-the-Loop (escalating when needed).

What frameworks are used to build agentic workflows?

Popular frameworks include LangChain/LangGraph for Python agents, CrewAI for multi-agent systems, AutoGen (Microsoft) for conversational agents, and workflow platforms like Miniloop that combine AI agents with business automation.

What are common use cases for agentic workflows?

Common use cases include customer support (autonomous resolution), research and analysis (gathering, synthesizing information), code generation and review, document processing, sales outreach, and IT operations—any task requiring reasoning and adaptive decision-making.

How do AI agents use tools in agentic workflows?

Agents have access to tools—APIs, databases, search engines, code interpreters. When an agent needs external information or capabilities, it calls the appropriate tool, processes the result, and continues reasoning. Tools extend agents beyond pure language capabilities.

What are the challenges of implementing agentic workflows?

Key challenges include reliability (agents can make mistakes), cost (LLM calls add up), latency (reasoning takes time), observability (debugging is hard), security (tool access needs guardrails), and evaluation (measuring agent performance).

Building Agentic Workflows with Miniloop

Agentic workflows combine the reasoning power of AI with the reliability of workflow automation. The key is balancing agent autonomy with appropriate guardrails and human oversight.

Miniloop provides a platform for building agentic workflows that connect to your business systems. Define goals in natural language, give agents access to your tools and data, build in human-in-the-loop for sensitive decisions, and deploy workflows that adapt intelligently to achieve outcomes.

Whether you're automating customer support, research tasks, or complex business processes, agentic workflows offer a new paradigm—AI that doesn't just respond, but plans, acts, and achieves.

Related Reading

• What Is AI Automation?
• Enterprise AI Governance
• Software Integration Guide