Deterministic vs Agentic Workflows: How to Choose What to Build

Here's the question I get asked most by business leaders evaluating AI:

"Should we build an AI agent or a traditional workflow?"

The hype says: Build AI agents for everything!

The reality says: Most problems don't need agents. And treating everything as an agent problem will cost you reliability, predictability, and money.

Let me show you the difference and give you a framework for making the right choice.

What's a Deterministic Workflow?

A deterministic workflow is a predefined sequence of steps that executes the same way every time.

Think: Traditional software, rules engines, automation scripts.

Example: Order Processing

1. Receive order
2. Validate payment
3. Check inventory
4. If in stock → Create shipment
5. If out of stock → Send backorder notification
6. Update database
7. Send confirmation email

Key characteristics:

• Predictable: Same input → same output, every time
• Fast: No reasoning overhead
• Testable: You can verify every branch
• Debuggable: Clear execution trace
• Reliable: 99.9%+ success rates

When it fails: When it encounters something outside its predefined rules.

What's an Agentic Workflow?

An agentic workflow uses an LLM to make decisions dynamically based on context and reasoning.

Think: AI agent that plans, selects tools, and adapts based on what it learns.

Example: Customer Support Agent

1. Receive customer message
2. Agent reasons: "This seems like an order inquiry"
3. Agent decides: "I should look up their order"
4. Agent calls tool: lookup_order(customer_email)
5. Agent receives result, reasons about it
6. Agent decides: "I need to check shipping status"
7. Agent calls tool: check_shipping(order_id)
8. Agent composes response based on findings

Key characteristics:

• Flexible: Can handle unexpected inputs
• Context-aware: Adapts to user's specific situation
• Natural: Feels conversational, not robotic
• Autonomous: Makes decisions without predefined rules

When it fails: When it makes wrong decisions, hallucinates, or gets stuck in loops.

The Critical Difference

Deterministic:

IF condition_A THEN action_1
ELSE IF condition_B THEN action_2
ELSE action_3

Agentic:

"Given this situation, what should I do?"
[LLM reasons] → [Makes decision] → [Takes action]

One is programmed. One is reasoned.

When Deterministic Workflows Win

Use Case #1: The Happy Path Is Clear

If 90% of cases follow the same pattern, use a deterministic workflow.

Example: Invoice Processing

1. Extract invoice data (OCR)
2. Validate required fields
3. Match to purchase order
4. Approve if amount matches
5. Flag for review if mismatch

This doesn't need an agent. The logic is clear and consistent.

Why deterministic wins:

• Faster (no LLM reasoning)
• Cheaper (no API calls)
• More reliable (no hallucinations)
• Easier to audit (clear paper trail)

Use Case #2: Compliance Matters

If you need to prove exactly what happened and why, deterministic workflows are better.

Example: Loan Approval

You need to document:

• What criteria were checked
• What data was used
• Why the decision was made

Deterministic: Every step is logged, every decision is traceable Agentic: "The AI decided..." (harder to explain to regulators)

Use Case #3: Reliability > Flexibility

If you need 99.9%+ uptime and can't afford surprises, go deterministic.

Example: Payment Processing

You can't have the AI "decide" whether to charge a credit card. That's a deterministic operation with strict rules.

When agentic fails:

• LLM is down → your workflow stops
• LLM hallucinates → incorrect action
• LLM rate limit → delayed processing

When deterministic fails: Almost never (unless code has bugs)

Use Case #4: Real-Time Performance

If you need sub-second response times, deterministic workflows are faster.

Response time comparison:

• Deterministic: 10-100ms
• Agentic (single LLM call): 1-3 seconds
• Agentic (multi-step): 5-15 seconds

Example: Fraud Detection

Credit card authorization happens in ~2 seconds. You can't wait 10 seconds for an AI agent to reason about whether it's fraud.

Solution: Deterministic rules + ML model (not agentic reasoning)

Use Case #5: Cost Sensitivity

Deterministic workflows cost almost nothing to run at scale.

Cost comparison (1,000 executions):

• Deterministic: $0.01 (compute cost)
• Agentic: $50-500 (LLM API costs)

If you're processing millions of transactions, that difference adds up fast.

When Agentic Workflows Win

Use Case #1: High Variability in Inputs

If every case is different, agentic workflows handle complexity better.

Example: Customer Support

Questions like:

• "My order arrived but one item is missing"
• "I was charged twice"
• "Can I change my delivery address?"

Each requires different tools, different data, different responses. An agent can adapt.

Why agentic wins:

• Don't need to program every possible scenario
• Agent reasons about what to do
• Handles edge cases gracefully

Use Case #2: Natural Language Understanding

If users interact in natural language, agents are better at understanding intent.

Example: Meeting Scheduler

User: "Can we meet sometime next week, preferably morning, but I have a doctor's appointment on Tuesday"

Deterministic: Struggles with ambiguity and constraints Agentic: Understands context, checks calendar, proposes options

Use Case #3: Multi-Step Research/Analysis

If the workflow involves gathering information from multiple sources and synthesizing it, agents excel.

Example: Competitive Analysis

Task: "Research competitor X and summarize their pricing strategy"

Agentic workflow:

1. Search for competitor's website
2. Find pricing page
3. Extract pricing tiers
4. Search for recent news about pricing changes
5. Synthesize findings into summary

Why agentic wins: The steps aren't always the same. The agent figures out what to do based on what it finds.

Use Case #4: Personalization at Scale

If you need to adapt responses to individual users dynamically, agents handle this naturally.

Example: Learning Assistant

Two students ask: "Explain photosynthesis"

Student A: 5th grader who struggles with science Student B: High school student taking AP Biology

Agentic: Tailors explanation to each student's level Deterministic: Same canned response for both

Use Case #5: Handling Ambiguity

When inputs are vague or incomplete, agents can ask clarifying questions.

Example: Travel Planning

User: "Book me a flight to Europe"

Agentic agent:

• "Which city in Europe?"
• "What dates?"
• "What's your budget?"

Deterministic workflow: Fails (missing required fields)

The Hybrid Approach (Best of Both Worlds)

Here's what smart companies are actually building: deterministic scaffolding with agentic steps.

Pattern: Use deterministic logic for structure, AI for flexibility.

Example: Refund Processing

Hybrid workflow:

1. [Deterministic] Validate order exists
2. [Deterministic] Check if eligible for refund (30-day window)
3. [Agentic] Analyze customer's reason for refund
   → Is it valid?
   → Does it suggest a product issue?
   → Should we offer alternatives?
4. [Deterministic] If approved → Process refund in payment system
5. [Deterministic] Update database
6. [Agentic] Generate personalized response email

Why this works:

• Critical operations (payment, database) are deterministic (reliable)
• Judgment calls (is reason valid?) are agentic (flexible)
• Best of both worlds: reliable + intelligent

Example: Document Analysis

Hybrid workflow:

1. [Deterministic] Extract text from PDF (OCR)
2. [Deterministic] Validate required fields are present
3. [Agentic] Classify document type
4. [Deterministic] Route to appropriate processing pipeline
5. [Agentic] Extract key information based on document type
6. [Deterministic] Store in database
7. [Agentic] Flag anomalies or issues for human review

Result: 90% automated, 10% human review, high accuracy.

Decision Framework: 7 Questions

Ask yourself these questions to decide:

1. How predictable are the inputs?

• Highly predictable → Deterministic
• High variability → Agentic

2. How critical is reliability?

• 99.9%+ required → Deterministic
• 90-95% acceptable → Agentic

3. What's your budget per execution?

• < $0.01 → Deterministic
• $0.10-$1.00 acceptable → Agentic

4. How fast does it need to be?

• Sub-second → Deterministic
• Few seconds OK → Agentic

5. Do you need to explain decisions?

• Must be auditable → Deterministic (or hybrid with logged reasoning)
• Black box OK → Agentic

6. How often does the logic change?

• Rarely changes → Deterministic
• Constantly evolving → Agentic (easier to update prompts than code)

7. What's the cost of failure?

• High (financial loss, safety risk) → Deterministic
• Low (user can retry) → Agentic

Real-World Examples

Good Use of Deterministic

Example: E-commerce Order Pipeline

• Orders follow same steps every time
• High volume (thousands per day)
• Need reliability and audit trail
• Clear business rules

Result: Traditional workflow with some AI-enhanced steps (fraud detection model)

Good Use of Agentic

Example: Customer Support Chatbot

• Every question is different
• Requires natural language understanding
• Needs to adapt to context
• 95% accuracy acceptable (can escalate to human)

Result: Full agentic workflow with tool access

Good Use of Hybrid

Example: Insurance Claims Processing

• Initial data validation: Deterministic
• Claims classification: AI model (not agentic)
• Fraud assessment: Agentic (requires reasoning)
• Approval/rejection logic: Deterministic
• Response generation: Agentic

Result: Reliable core with AI-enhanced judgment

Common Mistakes

Mistake #1: Using Agents for Everything

Wrong: "We'll build an AI agent to handle all our backend workflows!"

Why it fails: Agents are slower, more expensive, and less reliable than deterministic workflows for structured tasks.

Mistake #2: Using Deterministic Workflows for Open-Ended Tasks

Wrong: "We'll write IF/ELSE statements for every possible customer question!"

Why it fails: You'll never cover all cases. The code becomes unmaintainable.

Mistake #3: Not Considering Hybrid

Wrong: "We have to choose one or the other."

Right: Most real-world systems benefit from both. Use each for what it's good at.

Mistake #4: Ignoring Cost at Scale

Wrong: "Our agent costs $0.50 per execution. That's fine!"

Reality check: At 10,000 executions/day:

• $0.50 × 10,000 × 365 = $1,825,000/year

Maybe a deterministic workflow with a $0.001 execution cost makes sense?

Mistake #5: Not Planning for Failure

Agentic workflows will fail sometimes. Plan for it:

• Fallback to human escalation
• Error handling and retry logic
• Monitoring and alerts

The Bottom Line

Deterministic workflows:

• Fast, reliable, cheap, auditable
• Best for: Structured tasks, high-volume, critical operations

Agentic workflows:

• Flexible, context-aware, handles variability
• Best for: Open-ended tasks, natural language, personalization

Hybrid workflows:

• Combines reliability of deterministic with intelligence of agentic
• Best for: Most real-world applications

The right answer is usually not "all agents" or "no agents" — it's "agents where they add value, traditional code everywhere else."

Getting Started

Quick assessment:

1. List your top 5 automation opportunities
2. For each, answer the 7 decision framework questions
3. Classify as deterministic, agentic, or hybrid
4. Start with the highest ROI + lowest risk

Rule of thumb:

• If you can write clear IF/THEN rules → Deterministic
• If it requires judgment calls → Agentic or hybrid
• If it's mission-critical → Start deterministic, add AI cautiously

Need help deciding whether your use case needs an agent or a traditional workflow? We've built both and can help you choose the right architecture.

Get a free architecture consultation →