How to evolve from basic automation to intelligent agentic workflows

Basic automation gets old fast. You set up a trigger in Make.com, connect a few apps, and suddenly you're processing form responses like clockwork. But what happens when your business logic gets complex? When you need to make judgment calls instead of following rigid if-then rules?

That's where agentic workflows come in. Instead of mindlessly executing the same sequence every time, these systems actually think through problems, adapt to context, and make intelligent decisions on your behalf.

What's the difference between automation and agentic workflows?

Traditional automation follows a predictable path: when X happens, do Y. Make offers a powerful, user-friendly platform to automate nearly any process, allowing you to connect thousands of different apps and services seamlessly. But these workflows break down when faced with unexpected inputs or scenarios that require context-aware decision making.

An AI agentic workflow is a mix of AI agents with traditional workflow automation. Unlike standard workflows that follow predefined steps, AI agentic workflows employ intelligent agents to make decisions, adapt to new situations, and autonomously achieve goals.

The key difference? Your workflow becomes a problem-solver instead of just a task-executor. Agentic AI workflows don't just follow a path. It evaluates. It makes judgment calls. It reflects and reroutes when outcomes don't align with the goal.

Why should you make the transition?

Simple automations work great until they don't. You've probably experienced this: your perfectly designed workflow encounters a weird edge case and either breaks silently or produces garbage output. Maybe a customer submits a form with unexpected formatting, or your lead scoring automation can't handle a prospect that doesn't fit your typical categories.

It works best in specific conditions, where there's repeatability, some level of complexity, and the need for decision-making across multiple steps.

Here's what agentic workflows give you that basic automation can't:

Context awareness: They understand the bigger picture, not just the immediate trigger
Adaptive responses: They adjust their approach based on the specific situation
Error recovery: They can recognize when something's wrong and try alternative approaches
Learning capability: They improve their decision-making over time

What makes a workflow "agentic"?

The magic happens when you combine traditional workflow building blocks with AI decision-making capabilities. Design patterns—reflection, tool use, planning, and multi-agent collaboration—guide agents in autonomous work, communication, and decision-making.

Instead of hardcoded logic, agentic workflows use:

Large language models to interpret context and make nuanced decisions
Tool calling to interact with APIs and services dynamically
Memory systems to maintain context across multiple interactions
Reflection loops to evaluate their own outputs and improve

Think about customer support. A basic automation might categorize tickets by keywords and route them to departments. An agentic workflow reads the full context, understands the customer's emotional state, checks their history, and decides on the best response approach—maybe escalating immediately for VIP customers or suggesting specific resources for common issues.

How do you build your first agentic workflow?

Start with Make.com as your foundation. Automate your work. Make allows you to visually build, scale and automate AI and agentic workflows. The platform now includes AI nodes that let you integrate language models directly into your workflows.

Here's a practical example to get you started:

Smart Content Processing Workflow

Trigger: New file uploaded to Google Drive
AI Analysis: Use ChatGPT to analyze the content type, extract key information, and determine the best processing approach
Dynamic Routing: Based on the AI's analysis, route to different paths (document summarization, image analysis, or data processing)
Contextual Actions: Take appropriate actions based on the content and analysis
Feedback Loop: Log results and use them to improve future decisions

The key is replacing static decision trees with AI-powered analysis. Make can handle complex decisions in your workflows. You can set up different paths that take different actions based on certain conditions, fix errors automatically, or pause for manual checks.

Which tools work best for agentic workflows?

For beginners and visual builders: Make.com provides the easiest entry point. Make accelerates this process, with 400+ pre-built integrations with AI apps. Make AI Agents open even more possibilities, with automation that thinks and acts in the moment.

For developers wanting more control: Modal offers a serverless platform specifically designed for AI workloads. Modal is an AI infrastructure platform that lets you: Run low latency inference with sub-second cold starts, using open weights or custom models · Scale out batch jobs to run massively in parallel · Train or fine-tune open weights or custom models on the latest GPUs · You get full serverless execution and pricing because we host everything and charge per second of usage. Notably, there's zero configuration in Modal - everything, including container environments and GPU specification, is code.

For advanced orchestration: To build a workflow or agent, you can use any chat model that supports structured outputs and tool calling. The following example uses Anthropic. LangGraph provides sophisticated state management and multi-agent coordination capabilities.

What are the common pitfalls to avoid?

Starting too complex: Don't try to build a multi-agent system on day one. So don't start wide — start small and stress-test everything. Run pilots in controlled environments: Small group of users, limited scenarios.

Ignoring error handling: Agentic workflows are more unpredictable than simple automations. Guardrails and hallucination layers are also essential for detecting if the LLM or AI agents have made a mistake in the output generation, so that it can be detected quickly and sent for a re-run. They can also highlight or trigger a human-in-the-loop review for scenarios where a particular LLM call is taking longer to run than expected.

Forgetting the human element: Importantly, the system is designed to augment human expertise, not make final decisions. It helps claims processors by surfacing relevant policy details and organizing information, while leaving all coverage and settlement decisions firmly in human hands.

How do you test and validate agentic workflows?

Testing gets more complex when your workflow can make different decisions based on context. Here's a systematic approach:

Include edge cases: Test weird promo code formats, unexpected questions, vague inputs, typo-ridden requests. Monitor metrics: Track task completion rate, customer satisfaction, and fallback rate (how often a human has to step in).

Create test scenarios that cover:

Happy path cases where everything works as expected
Edge cases with unusual inputs or formatting
Error conditions where external services fail
Adversarial inputs that might confuse the AI

Log everything. Unlike simple automations where you can predict the exact output, agentic workflows need comprehensive logging to understand their decision-making process.

What does the future hold for business automation?

Working with AI agents shouldn't have to be unpredictable. With the right planning and tools, these agents can quickly become a reliable part of your workflow and processes—boosting not only your own productivity, but your team's too.

The transition from automation to agentic workflows isn't just about adding AI to your existing processes. It's about reimagining how work gets done. Instead of building rigid pathways, you're creating intelligent systems that can adapt, learn, and improve over time.

With analytics professionals increasingly employing AI for documentation and comprehensive AI strategies delivering significant efficiency gains, adopting agentic workflows represents a critical competitive advantage for modern enterprises.

The companies that make this transition now will have a significant advantage over those still stuck in the world of simple if-then automation. Your workflows won't just be faster—they'll be smarter, more resilient, and capable of handling the complexity that defines modern business operations.