What Is an Agentic Workflow?

Quick Definition#

An agentic workflow is an automated business or technical process where an AI agent makes real-time decisions, selects appropriate tools, and adapts its path based on observations — rather than following a fixed, pre-defined sequence of steps. The "agentic" qualifier specifically refers to the decision-making layer: the workflow can reason about the current state, interpret unstructured inputs, handle exceptions through natural language understanding, and combine tools in ways that were not explicitly programmed.

For foundational context, see The Agent Loop and Agent Planning before exploring workflow patterns. Browse the full AI Agents Glossary to explore related architecture terms.

Agentic Workflows vs. Traditional Automation#

The clearest way to understand agentic workflows is by comparing them to traditional automation tools like Zapier, Make (formerly Integromat), or n8n.

Traditional Automation#

Traditional automation executes deterministic workflows: every branch, every condition, every exception must be explicitly defined by the developer at design time. The workflow engine follows rules.

What it does well:

• Reliable and predictable execution
• Low cost per run
• Transparent — every step and condition is visible
• Easy to audit and debug

What it cannot handle:

• Unstructured inputs that require interpretation
• Novel exceptions not anticipated at design time
• Tasks requiring judgment about ambiguous situations
• Combining steps in ways that depend on the content of earlier steps

Agentic Workflows#

Agentic workflows use an LLM as the decision-making layer. Instead of every branch being pre-programmed, the model evaluates the current state and determines the next action at runtime.

What it does well:

• Handles unstructured inputs (emails, documents, natural language requests)
• Adapts to novel situations without requiring developer intervention
• Manages exceptions through reasoning rather than explicit error handling
• Combines tools flexibly based on what the task actually requires

What it trades off:

• Less predictable — the model may choose different paths for similar inputs
• Higher cost per run due to LLM inference
• Harder to audit — decisions are probabilistic, not rule-based
• Requires more careful safety design to prevent unintended actions

See Zapier vs AI Agents for a detailed comparison of when each approach is appropriate.

The Four Types of Agentic Workflows#

1. Sequential Workflows#

Tasks execute in a fixed order where each step consumes the output of the previous step. The agent's role is to handle each step intelligently, not to determine the order.

Example: Content production pipeline

1. Research agent searches for sources on a topic
2. Extraction agent pulls key facts from each source
3. Writing agent drafts content using the extracted facts
4. Review agent evaluates quality and flags issues
5. Formatting agent applies style and structure requirements

Each step is defined, but the agent decides how to execute each step based on the actual content it receives.

2. Parallel Workflows#

Independent subtasks execute simultaneously, reducing total latency. The orchestrator decomposes the goal, distributes workstreams to parallel agents, then aggregates results.

Example: Competitive analysis

• Agent A researches competitor pricing simultaneously with
• Agent B analyzing competitor features simultaneously with
• Agent C reviewing competitor customer reviews

All three complete in parallel. The orchestrator then synthesizes their findings into a unified analysis.

3. Conditional Workflows#

The workflow path depends on decision points the agent evaluates at runtime based on the content and context.

Example: Customer support triage

Inbound email
    ↓
Triage agent classifies: billing | technical | general
    ├── billing → Billing specialist agent
    ├── technical → Level 1 support agent
    │       ├── Resolved? → Close ticket
    │       └── Unresolved? → Escalate to Level 2
    └── general → FAQ matching agent
            ├── Match found? → Auto-respond
            └── No match? → Queue for human review

The routing decisions depend on the content of each email, not pre-defined keywords.

4. Loop-Based Workflows#

The agent repeats a step until a quality criterion is satisfied or a resource limit is reached. This is common for research, code generation, and refinement tasks.

Example: Research loop

while confidence_score < 0.8 and iterations < 5:
    search for additional sources
    extract and integrate new information
    evaluate confidence in current answer
    if confidence_score >= 0.8: break
return answer with confidence score

The loop continues until the agent's internal evaluation determines the answer is good enough, rather than running a fixed number of times.

Orchestration Patterns#

Most production agentic workflows use one of these orchestration architectures:

Single Agent with Tools#

A single agent has access to multiple tools and decides which to use at each step. Appropriate for tasks with moderate complexity that don't benefit from specialization.

Orchestrator-Worker#

A central orchestrator agent decomposes the goal and delegates subtasks to specialized worker agents. The orchestrator coordinates, integrates results, and manages dependencies. See Agent Handoff for handoff patterns.

Hierarchical Multi-Agent#

Multiple levels of orchestration for complex enterprise workflows. A top-level orchestrator manages mid-level orchestrators, each of which manages their own worker agents. See AI Agent Orchestration for architecture patterns.

Event-Driven#

Agents activate in response to external events rather than being triggered sequentially. An inbound email triggers the email triage agent. A new database record triggers the analysis agent. A time-based trigger initiates the daily report agent.

Failure Handling#

Agentic workflows fail differently than deterministic automation because failure modes are probabilistic, not just technical:

Model reasoning failures: The agent takes a wrong path because it misunderstood the goal, misinterpreted tool output, or made an incorrect inference. Unlike a code bug with a stack trace, reasoning failures require examining the full decision trace to diagnose.

Tool execution failures: External APIs return errors, rate limits are hit, or services are unavailable. Robust agentic workflows handle tool failures gracefully — retrying with backoff, falling back to alternative tools, or escalating to human review rather than silently failing or infinitely retrying.

Resource exhaustion: The agent exhausts its token budget, API quota, or time limit before completing the task. Set explicit limits and define graceful degradation behavior — return partial results with clear status rather than producing nothing.

Stale context: Long-running workflows accumulate context that becomes outdated or contradictory. Checkpoint patterns and explicit context refreshes prevent the agent from operating on stale information.

Cascading failures: An error in an early step corrupts the state for subsequent steps, producing incorrect results that look correct. Explicit validation checkpoints between major workflow phases catch these before they propagate.

See AI Agent Guardrails for defense patterns and Human-in-the-Loop AI for escalation architecture.

Real Business Workflow Examples#

Lead Research and Enrichment#

A sales automation agentic workflow:

1. Receives a new lead name and company from CRM
2. Searches the web for the company's latest news, funding, and headcount
3. Searches LinkedIn data for the lead's role, tenure, and background
4. Synthesizes findings into a structured enrichment record
5. Writes a personalized outreach email based on the enrichment
6. Routes the email to human review before sending

This replaces 20-30 minutes of manual research per lead. See AI Agent Examples for Sales Teams for similar patterns.

Document Processing#

A contract review workflow:

1. Extracts text from incoming contracts
2. Identifies key clauses (payment terms, liability, termination conditions)
3. Compares against standard template to flag deviations
4. Scores risk level based on deviation severity
5. Routes high-risk contracts to legal review, low-risk to auto-approval
6. Generates a summary for the account manager

Incident Response#

An operations workflow:

1. Receives alert from monitoring system
2. Queries relevant logs and metrics
3. Identifies probable root cause
4. Checks runbook for known remediation procedures
5. Executes safe auto-remediations (restart service, clear cache)
6. Escalates to on-call engineer for anything requiring human judgment

When to Use Agents vs. Traditional Automation#

Use traditional automation when:

• Inputs are structured and predictable
• Every possible path can be defined in advance
• Reliability and auditability are paramount
• Cost per execution must be minimized
• The workflow has already been designed and proven

Use agentic workflows when:

• Inputs are unstructured (documents, emails, natural language)
• The long tail of edge cases cannot be fully enumerated
• The workflow requires combining information from multiple sources with judgment
• The task benefits from adaptive behavior as context accumulates
• The cost of human handling exceeds the cost of LLM inference per case

Related Concepts and Further Reading#

• Agent Loop
• Agent Planning
• AI Agent Orchestration
• Task Decomposition
• Agent Handoff
• Zapier vs AI Agents
• AI Agent Workflow Examples
• How to Deploy AI Agents in Your Company

Frequently Asked Questions#

What is an agentic workflow?#

An agentic workflow is an automated process driven by an AI agent that can reason, make decisions, and adapt its actions based on real-time observations — rather than following a fixed, pre-programmed sequence of steps. The agent determines the path at runtime based on the goal, current state, and available tools.

When should I use an agentic workflow instead of Zapier or Make?#

Use traditional automation when inputs are predictable and every path can be defined in advance. Use agentic workflows when inputs are unstructured, when the task requires interpreting ambiguous situations, or when the combination of steps that is needed cannot be fully anticipated at design time.

What are the main types of agentic workflows?#

The four main types are sequential (fixed order, intelligent execution), parallel (independent subtasks run simultaneously), conditional (runtime decision-based routing), and loop-based (repeat until quality threshold is met). Most production workflows combine all four types within a single orchestration architecture.