Agentic AI Is Just Automation With Better Marketing

Stop paying "revolutionary AI" prices for rebranded workflow automation. The CTO who won't approve $50K for "workflow automation" will approve $500K for "autonomous AI agents" - for functionally the same thing. According to Deloitte's 2025 Tech Value Survey, only 28% of companies have mature AI agent capabilities despite 80% having mature basic automation. Same technology. Different marketing. 10x the price.

Here's the truth: if you've built workflow automation, you've built "agentic AI." I've been building workflow automation systems for 30 years, and the gap between what vendors are selling and what the technology actually does is wide enough to drive a truck through.

What "Agents" Actually Are

An AI agent, stripped to its core components:

1. An LLM - makes decisions about what to do next
2. Tools - functions the agent can call (APIs, databases, file systems)
3. A loop - observe, decide, act, repeat until done
4. Memory - context from previous steps (though AI agents can't really remember the way vendors suggest)

That's it. An LLM decides which function to call. It calls the function, examines the result, decides what to do next. Repeat until the task is complete or you hit a stopping condition.

Here's the thing: we've been building systems like this forever. We just called them different things.

The Names We Used Before

Workflow automation: Zapier, IFTTT, Microsoft Power Automate. Trigger, action, condition, repeat. The decision logic was rules-based instead of LLM-based. But the pattern is identical.

Orchestration: Apache Airflow, Prefect, Dagster. Define a DAG of tasks, execute them in order, handle failures, retry. Sound familiar?

RPA (Robotic Process Automation): UiPath, Automation Anywhere. Bots that click through UIs, extract data, make decisions, take actions. As TechTarget notes, RPA has been around for 15 years and is better established in the enterprise than AI agents. The "robots" of 2018 are the "agents" of 2025.

Expert systems: Rule-based systems that made decisions and took actions. The AI of the 1980s. We're back to the same concept with better decision engines.

State machines: Observe state, decide transition, execute action, update state. The computer science fundamentals behind every "autonomous agent."

The innovation isn't the pattern. It's using an LLM as the decision function instead of hand-coded rules. That's genuinely useful. It's not the revolution marketing suggests.

What LLMs Add (And Don't Add)

What LLMs add:

• Flexible parsing: Understanding natural language inputs without rigid formats
• Fuzzy decision-making: Handling cases that don't fit predefined rules
• Natural language output: Generating human-readable responses
• Zero-shot generalization: Handling new situations without explicit programming

What LLMs don't add:

• Reliability: LLMs hallucinate, misunderstand, make mistakes. Rule-based systems are predictable.
• Speed: An LLM call takes 500ms-5s. A rule evaluation takes microseconds.
• Cost efficiency: Every decision costs tokens. Rules are free after development.
• Auditability: Why did the agent do that? With rules, you can trace the logic. With LLMs, you get probability distributions.
• Determinism: Same input, same output? Not with LLMs unless you sacrifice capability with temperature=0.

The Deterministic Cliff

Here's the physics that determines which approach wins:

Traditional automation is deterministic: same input produces same output, every time, forever. You can prove correctness. You can guarantee behavior. You can sleep at night.

LLM-based agents are probabilistic: same input produces different outputs on different runs. Sometimes subtly different. Sometimes catastrophically different. You can't prove anything. You can only sample and hope.

This isn't a limitation that better models will fix. It's inherent to how LLMs work. Temperature, sampling, and attention mechanisms all introduce variance by design. The creativity that makes LLMs useful is the same mechanism that makes them unpredictable.

For workflows that run once, this variance is tolerable. For workflows that run 10,000 times per day, it's poison. A 1% error rate means 100 failures daily. At enterprise scale, "usually correct" becomes "constantly failing somewhere."

The deterministic cliff is where agent dreams go to die: the point where variance accumulates faster than value.

For many workflows, LLM flexibility isn't worth the reliability sacrifice. In my experience, the best "agentic" systems are hybrids. LLMs handle the fuzzy parts. Deterministic logic handles everything else. I learned this the hard way building voice AI systems - the reliable parts were always the deterministic state machines, not the AI inference.

The Vendor Incentive

Why the rebranding? Follow the money:

Automation is mature. Zapier has been around since 2011. When I was building workflow systems at ZettaZing in 2015, we called them "orchestrators" and "automation pipelines" - the same patterns now being rebranded as agents. The workflow automation market is competitive and commoditized. Hard to charge premium prices for well-understood technology.

"AI agents" are new and exciting. New category, new budgets, new buyers. The CTO who won't approve $50K for "workflow automation" will approve $500K for "autonomous AI agents."

Usage-based pricing loves loops. Agents that iterate burn tokens. Every decision, every tool call, every retry - that's API revenue. A workflow that runs once costs less than an agent that loops.

Complexity justifies consultants. "We'll help you build your AI agent strategy" is a more lucrative engagement than "we'll set up your Zapier workflows."

The technology is real. The hype serves vendor interests more than customer interests. And vendors have strong incentives to exaggerate capabilities.

Agent vs Script Calculator

Should you use an LLM agent or traditional automation? Answer these questions:

When Agents Make Sense

Despite the hype, there are genuine use cases where LLM-based agents outperform traditional automation. According to Automation Anywhere's 2025 analysis, the strongest results come from a hybrid model where RPA handles routine execution and agentic AI manages complexity and exceptions.

Unstructured inputs: When you can't predict the format of incoming data. Customer emails, support tickets, documents with variable layouts. LLMs parse what rules can't.

Long-tail decisions: When you have thousands of edge cases that would require thousands of rules. LLMs handle the long tail. Rules handle common cases.

Human-in-the-loop workflows: When an agent needs to interact naturally with humans. Ask clarifying questions, explain reasoning. LLMs do this well.

Exploratory tasks: Research, investigation, open-ended analysis. When you don't know in advance what steps will be needed.

One-off automation: When building custom rules isn't worth the effort but you need automation. LLMs provide quick, flexible solutions for low-volume tasks.

When Traditional Automation Wins

And cases where you should skip the agent hype:

High-volume, predictable workflows: Processing 10,000 invoices with consistent format? Rules are faster, cheaper, and more reliable than agents.

Compliance-critical processes: When you need to explain exactly why a decision was made. Audit trails for LLM decisions are... challenging.

Real-time requirements: When decisions need to happen in milliseconds. LLM latency kills real-time applications.

Cost-sensitive applications: When you're processing millions of items and can't afford $0.01 per decision.

Deterministic requirements: When the same input must always produce the same output. Financial calculations, regulatory compliance, safety-critical systems.

The Hybrid Reality

The best production systems aren't "agents" or "automation" - they're both:

Rules for the predictable. If input matches pattern X, do action Y. Fast, cheap, reliable, auditable.

LLMs for the unpredictable. If input doesn't match any pattern, ask the LLM to figure it out. Flexible, slower, more expensive, good enough.

Human escalation for the important. If the LLM's confidence is low or the stakes are high, escalate to a human. The agent assists rather than replaces.

This isn't as exciting as "autonomous AI agents that handle everything." It's also what actually works in production. The reality is that every production AI system I've seen succeeds because of the deterministic scaffolding around it, not despite it.

What To Ask Vendors

When someone pitches you an "AI agent solution":

"What happens when the LLM makes a mistake?" Every LLM-based system makes mistakes. What's the error handling? What's the blast radius? How do you detect and correct errors?

"What's the cost per decision?" Agents that loop can burn tokens fast. Get specific numbers for your volume.

"Can I audit why a decision was made?" If you need compliance or explainability, understand what logs and traces the system provides.

"What could I do with rules instead?" For how much of your workflow could you write explicit rules? Those parts don't need an LLM.

"What's the latency?" If you need real-time, can the system deliver? What's p50, p95, p99?

"How does it fail?" Not if - how. What happens when the LLM is confused, the API is down, the loop doesn't terminate?

Building Effective Agents

If you do need LLM-based agents, here's what actually works:

Constrain the action space. The fewer tools an agent can use, the less it can screw up. Start narrow. Expand carefully.

Build in checkpoints. Don't let agents run indefinitely. Set iteration limits. Set confidence thresholds. Add human review triggers.

Log everything. Every decision, every tool call, every reasoning step. You'll need it for debugging and improvement.

Test adversarially. What happens with malformed input? Malicious input? Unexpected API responses? Agents fail in creative ways. Test for that.

Measure end-to-end. Not just "did the agent complete?" but "did it complete correctly?" Success metrics should reflect actual outcomes.

Plan for human backup. Agents should know when they're stuck and how to escalate. The human-in-the-loop isn't a failure mode. It's a feature.

"Useful automation is useful, whatever you call it. Just don't pay "revolutionary AI" prices for "workflow automation with an LLM" capabilities."