After nearly two years of working intensively with Agentic AI—first as Sr CSA Manager Data & AI at Microsoft, working with strategic enterprise customers, and now as CEO of Scrydon—I’ve come to a clear conclusion: the future of Agentic AI is flow-based.

The initial promise of fully autonomous agents—AI systems that could independently reason, plan, and execute complex tasks—has given way to a more nuanced reality. As McKinsey notes in their analysis “One year of Agentic AI: Six lessons from the people doing the work”:

It’s not about the agent; it’s about the workflow.

This insight resonates deeply with what I’ve observed across dozens of enterprise deployments. Let me explain why flow-based agentic AI represents the path forward.

The Problem with Fully Autonomous Agents

The allure of autonomous AI agents is understandable. The idea of deploying an AI that can independently handle complex tasks sounds transformative. However, organizations that rush into fully autonomous agent deployments often encounter:

  • Unpredictable behavior: LLM-based agents can produce vastly different outputs for similar inputs, making quality assurance challenging
  • “AI slop”: Low-quality outputs that frustrate users and erode trust quickly
  • Black-box decision making: When something goes wrong, it’s nearly impossible to understand why
  • Compliance nightmares: Regulators and auditors struggle to verify AI-driven decisions

McKinsey’s research confirms these challenges, noting that “many companies are finding it challenging to see value from their investments. In some cases, they are even retrenching—rehiring people where agents have failed.”

Why Flow-Based is the Answer

Flow-based agentic AI takes a fundamentally different approach. Instead of letting agents roam freely, you design visual workflows that orchestrate AI agents, integrations, and business logic. Think of it as giving your AI agents clear job descriptions, defined handoff points, and structured collaboration patterns.

Here are the three key advantages:

1. Deterministic Execution

In a flow-based system, while individual LLM calls may be non-deterministic, the overall workflow is predictable. You define:

  • Which agent handles which task
  • What conditions trigger specific branches
  • How data flows between steps
  • What validation checkpoints exist

This determinism is crucial for enterprise adoption. When a CFO asks “what will this AI do?”, you can show them the flow—not just hope for the best.

Platforms like Scrydon make agents predictable and auditable with full control over LLMs, prompts, and tool usage. Every decision, reasoning path, and tool invocation is logged for regulatory audits.

2. Humans Stay in Control

McKinsey emphasizes that “humans remain essential” and that companies should “be deliberate in redesigning work so that people and agents can collaborate well together.” Flow-based design makes this natural:

  • Visual oversight: You can see the entire workflow at a glance
  • Human-in-the-loop: Insert approval steps, review gates, or escalation triggers anywhere in the flow
  • Easy iteration: Adjust the flow based on real-world feedback without retraining models
  • Clear boundaries: Define exactly what agents can and cannot do

This isn’t about limiting AI capability—it’s about deploying AI responsibly. In my experience, workflows where lawyers still sign documents, where analysts approve recommendations, and where edge cases get human attention are the ones that actually make it to production.

3. Flexibility to Add Tools

One of the most practical advantages of flow-based design is composability. Modern platforms provide extensive integration libraries—Scrydon, for example, offers 136+ pre-built triggers, blocks, and tools—allowing you to:

  • Connect agents to databases, APIs, and enterprise systems
  • Add new capabilities without redesigning the entire system
  • Mix AI with traditional automation (rule-based systems, analytical models, etc.)
  • Extend with custom functions when needed

This flexibility is essential because, as McKinsey points out, “agents aren’t always the answer.” Sometimes a simple rule-based check is more reliable than an LLM call. Flow-based systems let you deploy “the right technology at the right point.”

Comparison with Other Approaches

The landscape of agentic AI tooling has exploded. Beyond code-centric frameworks like AutoGen (which I wrote about previously), CrewAI, and LangGraph, we’re seeing more visual, flow-based approaches:

  • Power Automate has added AI Builder and Copilot capabilities, bringing flow-based AI to the Microsoft ecosystem
  • Microsoft AI Foundry offers orchestration capabilities for enterprise AI development
  • Scrydon provides a sovereignty-first, flow-based Agentic AI platform

There’s also a growing category of “vibe coding” tools—platforms like Lovable, Google AI Studio, Bolt, and similar—where you describe what you want and AI generates the application. These have their place for quick prototyping and experimentation, but they typically lack the production-grade features enterprises require: version control, staging environments, audit trails, rollback capabilities, and the ability to deploy on-premise or in sovereign environments. When your business processes are critical, the gap between a working prototype and a production system becomes significant.

The trend is clear: the market is moving toward visual orchestration. Even organizations that started with code-first frameworks are increasingly looking for ways to make their agent workflows more visible, auditable, and manageable.

Practical Recommendations

Based on my experience, here’s how to approach flow-based agentic AI:

  1. Map your workflows first: Before building agents, understand the end-to-end process. Identify pain points, decision points, and where variance naturally occurs.

  2. Start with human-in-the-loop: Don’t aim for full autonomy immediately. Build trust by keeping humans involved, then gradually automate as confidence grows.

  3. Invest in observability: Build monitoring and evaluation into every step. When mistakes happen—and they will—you need to quickly identify what went wrong.

  4. Think reusability: McKinsey notes that “the best use case is the reuse case.” Design agents and agent components that can be reused across different workflows.

  5. Choose platforms wisely: Your business processes are critical—choose a platform that can work anywhere. Look for the ability to deploy on-premise, in sovereign cloud environments, or air-gapped when needed, without sacrificing functionality.

Conclusion

The hype around autonomous AI agents is giving way to pragmatic, workflow-centric approaches. Flow-based agentic AI offers the best of both worlds: the power of LLMs and intelligent agents, combined with the predictability, control, and flexibility that enterprises require.

At Scrydon, we’re building exactly this vision—transforming AI from isolated tools into true autonomous team members, while ensuring organizations retain full sovereignty over their data and workflows.

The future of agentic AI isn’t about replacing humans with autonomous agents. It’s about designing workflows where humans and AI collaborate effectively, with clear oversight, predictable behavior, and the flexibility to evolve. That future is flow-based.