AI Agents vs Workflows: Choosing the Right Pattern

Everyone wants to build AI agents. Most should build workflows instead.

Definitions

Workflow: Predefined steps. AI handles specific tasks within a fixed pipeline. Deterministic flow.

Agent: AI decides what to do next. Dynamic planning. The LLM controls the flow.

Workflow: Step A → Step B → Step C → Done
Agent:    Observe → Think → Act → Observe → Think → Act → ... → Done (maybe)

When Workflows Win

Predictable processes. Customer support triage, document processing, report generation.

# Workflow: Clear steps, predictable outcome
def process_support_ticket(ticket):
    category = classify(ticket)           # Step 1: Classify
    sentiment = analyze_sentiment(ticket)  # Step 2: Analyze
    priority = determine_priority(         # Step 3: Prioritize
        category, sentiment
    )
    response = generate_response(          # Step 4: Respond
        ticket, category, priority
    )
    return route_to_team(category, priority, response)  # Step 5: Route

Each step is an LLM call with a specific job. The flow is fixed. Debugging is straightforward.

Why this works: You can test each step independently. You can add guardrails at each stage. You know the cost per execution.

When Agents Win

Open-ended problems. Research tasks, complex analysis, multi-step problem solving where you can’t predict the path.

# Agent: Dynamic planning, unpredictable steps
def research_agent(question):
    while not satisfied:
        plan = think(question, context)    # What should I do?
        action = choose_action(plan)       # Search? Read? Calculate?
        result = execute(action)           # Do it
        context.add(result)                # Learn from it
        satisfied = evaluate(context)      # Am I done?
    return synthesize(context)

The agent decides its own path. Powerful but unpredictable.

Why this is risky: Costs are unbounded. Execution time is unpredictable. Debugging loops is hard. The agent might go in circles.

The Decision Framework

Ask these questions:

Can I define the steps upfront?

• Yes → Workflow
• No → Agent (maybe)

Is cost predictability important?

• Yes → Workflow
• No → Agent is okay

Do I need to debug failures easily?

• Yes → Workflow
• No → Agent is okay

Is the task well-defined?

• Yes → Workflow
• No → Agent

The Hybrid Approach

Most real systems are hybrids. A workflow with agent-like steps.

def smart_document_processor(doc):
    # Fixed step: Extract text
    text = extract_text(doc)

    # Fixed step: Classify document
    doc_type = classify(text)

    # Agent-like step: Handle based on type
    if doc_type == "invoice":
        return invoice_workflow(text)      # Fixed workflow
    elif doc_type == "contract":
        return contract_agent(text)        # Agent for complex analysis
    else:
        return general_summary(text)       # Simple LLM call

Use workflows for the structure. Use agents for the genuinely complex parts.

Common Mistakes

Building agents for workflow problems. If you can draw a flowchart, you don’t need an agent.

No guardrails on agents. Always set: max iterations, max cost, timeout, and fallback behavior.

Skipping the workflow stage. Build the workflow first. Only add agent capabilities where the workflow fails.

The Reality

Agents are exciting. Workflows are reliable.

In production, reliable wins. Every time.

Start with workflows. Graduate to agents only when you hit problems workflows can’t solve.

Your users don’t care if an agent solved their problem. They care that their problem got solved.