From human hands to autonomous agents: tracing the evolution of how work gets done
Workflows are the hidden engine behind every organization. Whether it’s resolving a security incident, provisioning a new hire, or onboarding a new client, these sequences of tasks are what turn intent into action. But workflows didn’t always look the way they do today.
Today, we’re exploring how workflows evolved from manual, human-led steps to powerful AI-driven systems. This is not a purely academic trip down memory lane. Understanding where we come from is critical for building modern workflows that take the best of what came before and applying new innovations in technology to build resilient, effective systems that can meet today’s challenges.
1. The human-led era (pre-2000s)
Before we get started, it’s important to define what we mean by a “workflow.” At Tines we define it as a set of tasks, decisions, and actions carried out to achieve an end goal.
And with that definition, it’s pretty easy to see that workflows are not bound by any specific automation or collaboration technology. The earliest enterprise workflows were fully manual, driven by human intuition, expertise, and documentation. Workers relied on standard operating procedures, collective team knowledge, and real-time communication to get things done.
Take, for example, the use of checklists and procedures. In his famous book The Checklist Manifesto, Atul Gawande describes how the World Health Organization’s Surgical Safety Checklist helped dramatically reduce complications and deaths during surgery (by a third and nearly half, respectively). Even centuries-old workflow tools like standard operating procedures and checklists have long been essential in codifying best practices, improving coordination, and ensuring consistency.
Whether in a hospital, a financial institution, or a factory floor, these methods worked because they emphasized clear roles, structured escalation paths, and deep contextual understanding.
What made these workflows work:
Adaptability to new or changing situations
Clear escalation paths
Deep context and judgment
Well-documented roles and responsibilities
Where they fell short:
The effectiveness of these workflows often depend heavily on the people executing them. Operators can be slow, overwhelmed, or prone to error, especially as the volume and complexity of tasks grow. Burnout, shift limitations, and inconsistency became real barriers to scale.
In response, teams began exploring ways to codify parts of these workflows into software, recognizing that what worked in a checklist could, with the right structure, be translated into code. This insight laid the foundation for the next era of workflow evolution.
2. The deterministic era (2000s–late 2010s)
As organizations expanded digitally, manual processes began to buckle under the pressure. The 2000s brought a wave of transformation: the rise of the internet, mobile devices, and cloud infrastructure amplified both the volume and complexity of tasks. At the same time, software architects and platform builders began to expose APIs. Salesforce, for example, launched its web API in 2000, enabling external developers to programmatically interface with its CRM.
This move signaled a shift: workflows could now extend beyond human operators into systems and services, enabling scripts to parse data, trigger actions across systems, and automate coordination in entirely new ways.”
Additionally, when you look at teams like IT or Engineering, they weren’t just managing on-prem infrastructure anymore. They were fielding alerts from cloud-native tools, SaaS apps, and remote endpoints. The need for faster, more reliable execution pushed teams to script their way out of manual bottlenecks.
Early collaboration tools focused on the task, essentially converting spreadsheet use cases into software. Meanwhile, automation emerged through tools like scripts and macros to replicate human behavior and reduce muckwork. These laid the groundwork for Robotic Process Automation (RPA), which gained popularity in the 2010s by mimicking user actions in a GUI.
Eventually, these approaches evolved into more scalable, flexible solutions: low-code and no-code platforms. These platforms provided a visual way to build, allowing security and IT teams to construct deterministic workflows using drag-and-drop components rather than raw code.
What made deterministic workflows powerful:
Fast, consistent, and scalable
Operated 24/7 with low overhead
Easy to audit, monitor, and version
Ideal for high-volume, repeatable processes
Challenges that emerged:
Required upfront technical skill
Brittle when systems or logic changed
Struggled with ambiguity or edge cases
Often seen as rigid or overly simplistic
Workflows in this era powered critical tasks ranging from employee onboarding and access provisioning to ticket routing, data enrichment, and customer support automation. In the world of information security, these same approaches were applied to incident triage, phishing response, and threat analysis. Yet despite their efficiency, they lacked flexibility and still needed human oversight in key moments.
3. The AI era (2020–present)
The launch of foundation models and natural language interfaces in the early 2020s has ushered in the next phase: agentic workflows. These AI-powered systems go beyond just following steps into reasoning, adaption, and autonomous action.
With copilots and agents, workflows can dynamically fetch data, analyze context, make decisions, and even engage with users—without rigid preprogrammed logic.
Yet even with these new capabilities, one principle remains essential: the human-in-the-loop. The most effective workflows don’t remove people from the process, but instead empower them to guide, validate, and enhance automation. A human might initiate a workflow, provide contextual input, or approve key actions before execution.
This balance between human judgment and deterministic certainty has long been at the heart of Tines’ philosophy, and it remains critical as AI takes on a greater role.
Why AI-powered workflows are game-changing:
Easy to start with minimal training or scripting
Incredibly flexible and adaptive
Capable of handling ambiguity and nuance
Unlock new use cases with minimal effort
But the challenges are real:
Unpredictable outcomes
Difficult to test, validate, and audit
Not inherently secure
Still expensive and immature for mission-critical paths
That’s why most modern teams aren’t replacing deterministic or human-led workflows. Instead, they’re blending them with AI to create something entirely new and powerful…
Looking back to build forward
Workflow evolution is a story of tradeoffs between flexibility and speed, scale and control, simplicity and nuance. It’s also a story of constant building. Each time we’ve entered a new era of workflows, we’ve kept the best parts of the old era and applied them in new ways.
Each time we’ve entered a new era of workflows, we’ve kept the best parts of the old era and applied them in new ways.
Today, the best workflows aren’t one-size-fits-all. They intelligently combine human judgment, deterministic reliability, and agentic adaptability. And in the next post in this series, we’ll explore what they look like and how leading teams are leveraging them to build resilient systems for whatever comes next.
Read the second post in this series, "What is an intelligent workflow platform, and why does it matter?" here.