A gentle introduction to workflow automation and agentic AI

If you’d like to dive deeper into this topic, you can watch the full webinar here : A gentle introduction to workflow automation and agentic AI.

Why workflow automation and agentic AI are essential today

Artificial Intelligence (AI) has become one of the fastest-growing technology trends. Two concepts in particular, workflow automation and agentic AI, are transforming how businesses streamline their operations, improve productivity, and unlock new opportunities.

According to a McKinsey report published in June 2025 [1], generative and agentic AI could unlock between $2.6 and $4.4 trillion in additional value beyond traditional analytical AI. Yet most organizations are still struggling: 78% of companies report deploying generative AI, but 80% see no tangible results, and only 1% consider their AI strategy mature.

This paradox highlights the gap between experimentation and real business impact, a gap that workflow automation and agentic AI can help close.

What is an AI agent?

The term AI agent is widely used but rarely defined consistently. At its core, an AI agent is a system capable of:

1. Perceiving its environment (input)
2. Making decisions (reasoning or planning)
3. Acting to achieve a goal
4. Adapting through feedback and learning

AI agents can be placed along a spectrum:

• Specialized agents: very narrow scope (e.g., email classifier, sentiment detector)
• Autonomous agents: able to act on goals without step-by-step instructions (e.g., an AI assistant managing a calendar)
• Multi-agent systems: networks of agents collaborating to solve complex tasks such as supply chain optimization

The automation landscape: where to start

Before diving into AI-enhanced automation, it’s often recommended to begin with traditional workflow automation tools such as:

• Zapier
• Make (formerly Integromat)
• n8n
• Microsoft Power Automate

These platforms let you connect apps and services with “if this, then that” logic, reducing repetitive manual work.

Pros: easy to use, little or no coding required, accelerates structured workflows.
Cons: rigid rules, fragile when inputs change, unable to process unstructured data like PDFs or free-text emails.

What AI brings to workflow automation

Adding AI components makes automation smarter, more flexible, and closer to human behavior. Key advantages include:

• Understanding unstructured data (emails, documents, images, videos)
• Context-aware decision making
• Dynamic adaptation to new information
• Natural language understanding and generation
• Multi-step reasoning and autonomy

Concrete examples:

1. Email triage with NLP: AI detects intent and routes automatically to the right person or system.
2. Smart document processing: AI extracts and validates data from PDFs or scanned forms.
3. AI-generated customer replies: drafts personalized responses that can be reviewed before sending.

From simple automation to agentic AI

The path to agentic AI follows a maturity progression:

1. Traditional automation: structured, rule-based workflows
2. Automation + AI: AI modules integrated into workflows (chatbots, NLP, intelligent routing)
3. Agentic AI: systems able to generate their own action plans, reason across steps, and act autonomously

For example, while Make.com can automate email logging and replies, next-generation platforms like Manus or ChatGPT’s Agent Mode showcase autonomous agents able to plan and execute tasks independently.

Next steps: going more technical

Once you’ve experimented with no-code automation, the next step is to explore agent frameworks and protocols such as:

• LangChain, LangGraph, LlamaIndex: to build advanced AI-powered workflows.
• MCP (Model Context Protocol) and A2A (Agent-to-Agent Protocol): to enable agents to collaborate with each other or with traditional systems.

These tools pave the way for scalable and maintainable AI systems beyond simple prototypes.

Conclusion

Workflow automation and agentic AI mark an evolution: from rigid processes to intelligent and adaptive systems. Whether you start with Zapier or Make, or explore multi-agent systems, the key is to experiment while keeping business value and scalability in mind.

However, before diving headfirst into agentic AI, take the time to anchor a clear strategic vision and build leadership awareness, as explained in our guide on integrating AI into business in 2025.

To go further, watch the full webinar : A gentle introduction to workflow automation and agentic AI.

References

[1] https://www.mckinsey.com/capabilities/quantumblack/our-insights/seizing-the-agentic-ai-advantage