What Comes After the First Wave of AI Agentic Workflow Builders & Automation Platforms?

Over the past two years, agentic AI workflows and AI automation platforms have exploded across the technology landscape. Nearly every new product promises AI agents that can complete tasks, make decisions, and automate work with minimal human input.

But when you look closer, most tools still follow a familiar pattern:

• A chat box 
• A flow-chart canvas 
• Some integrations 
• A sprinkle of “memory” and RAG 
• A lot of screenshots in purple gradients

Plenty of these tools are powerful. But many of them are really workflow builders wearing an “agentic” trench coat. So if that’s wave one… what does wave two actually look like?

This article explores:

1. The evolution from workflow automation to autonomous AI agents
2. The emergence of multi-agent systems and AI teams
3. The development of the agentic AI economy
4. The rise of agent-of-agents architectures
5. How industries like recruitment are already experimenting with AI-driven orchestration

Wave One: Agentic Workflow Builders

The first wave of agentic automation platforms introduced tools that combine traditional workflow automation with large language models.

Platforms like UiPath and Automation Anywhere have expanded classical robotic process automation (RPA) with AI capabilities.

Typical features include:

• Low-code or no-code workflow builders
• LLMs that interpret instructions and natural language
• API integrations with platforms like Slack, Notion, and HubSpot
• Triggers based on schedules, events, or user prompts

These tools represent a major improvement over rigid automation pipelines, allowing businesses to automate tasks faster and with less technical complexity.

Wave Two: Autonomous AI Agents and Multi-Agent Systems

The next generation of AI systems moves beyond single-agent automation toward collaborative AI teams.

Research from organizations like Google DeepMind highlights how multi-agent systems can coordinate to solve complex problems.

Instead of a single workflow, these systems involve multiple specialized agents working together.

For example:

Planner Agent

• Defines the strategy and breaks down objectives into tasks.

Execution Agents

• Perform specific functions such as data analysis, writing, coding, or research.

Coordinator Agent

• Oversees progress, resolves conflicts, and ensures alignment with goals.

Wave Comparison: Automation vs Agentic AI

Wave	Technology Model	Human Role	Examples
Wave 1	Workflow automation	Humans design flows	Zapier AI, RPA tools
Wave 2	Multi-agent systems	Humans set objectives	AI teams and agent orchestration
Wave 3	Agentic AI economy	Humans define rules	AI agents transacting in markets

The Rise of the Agentic AI Economy

Researchers and infrastructure builders are now outlining the blueprint for an economy where agents transact, negotiate, and optimize in real-time:

• Agents making payments under constraints 
• Infrastructure like Sei is positioned as rails for a machine-speed AI agent economy 
• New risks: collusion, runaway loops, fraud vectors 
• Trust and identity are emerging as the core problem

This is when agents stop living inside your company and start participating in markets.

THE “AGENT OF AGENTS” ARCHITECTURE 

As agents proliferate, the emerging pattern is an AI layer that manages other agents – a kind of AI COO.

This manager-agent:

• Spins up or retires specialized agents 
• Allocates budget and compute 
• Enforces rules and oversight 
• Uses guardian agents to catch bias or risky actions before execution

Agentic AI in Recruitment

Ai Recruitment is one of the first domains transforming through agentic automation:

Platforms like Gen6, X0PA, Zappyhire, and others show early versions of:

• Forecasting agents 
• Screening agents 
• Candidate engagement agents 
• Compliance and fairness agents 
• Multi-agent recruiters orchestrated by a top-level “recruitment conductor.”

Done responsibly, it increases fairness and frees humans for relationship-building.

How Organizations Should Prepare for Agentic AI

1. Replace “What can we automate?” with “What outcomes could an agent own?” 
2. Design agents like roles: mission, powers, guardrails 
3. Assume you are entering a multi-agent world 
4. Build governance early — logging, observability, guardian agents 
5. Start small but architect for wave two 

THE SHIFT UNDER THE HYPE 

Wave one gave us better workflows. Wave two reshapes how work and value move through organisations:

• Autonomous AI teams 
• An agentic economy 
• Agent-of-agents governance 

We are moving from “AI as a tool” to “AI as a colleague who manages other AI colleagues”.

The real question is: 

What roles are you ready to hand to agents — and what guardrails will shape their decisions?

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