From Rigid Workflows to Agentic Automation: The AgentOS Advantage

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From Rigid Workflows to Agentic Automation: The AgentOS Advantage
Shashank Singh
Shashank Singh

In previous editions about AgentOS, we've talked about why enterprises need an AI-native platform to effectively implement AI solutions. We've also covered what AgentOS is and what it can do. In this blog, we're diving into the workflow engine of AgentOS and why it's a key component that makes it so powerful.

Yes, you need automation that works

Automation is everywhere—from airport kiosks to smart home devices to customer support. It enhances convenience and efficiency for individuals, and in business, automation boosts productivity by handling complex tasks with minimal human effort. Looking forward, we are seeing AI and knowledge graphs analyze data and make real-time decisions for us.

If we look at customer support, automation speeds up responses, maintains service quality, and provides customer satisfaction. Without it, operations slow down, errors increase, and costs rise. Automation is essential for staying competitive in a fast-paced, tech-driven market.

The Nightmare of Implementing traditional workflows: A Reality Check

Your organization's productivity, employee experiences, and revenue are intrinsically tied to your processes and automations. You start imagining and sketching a workflow, unique to your team, on a whiteboard. Simple boxes and arrows, connected with english words, representing your ideal, efficient process.

But then, reality sets in when you try to translate this pristine vision into your CRM tool. Suddenly, you're hit with the limitations of your current workflow engine. Critical triggers are missing. You're confined to basic if/else conditions, and you can't drag actions where you need them. Visualizing and managing the lifecycle of a process becomes a nightmare, and there’s no feedback loop for improvement.

Traditional automation is fundamentally about going from point A to point B. You write code to automate this journey, or perhaps use a GUI to achieve the same result. However, as edge cases emerge, your system's complexity grows exponentially. You start adding conditions: if Y, do C; if Q, do D. Soon, your automation graph becomes a tangled web of conditions and edge cases.

The ideal workflow you envisioned is compromised, and improving it within the confines of your CRM becomes nearly impossible without extensive developer assistance and time-consuming modifications.

Now, you're left with a workflow that barely resembles your original plan. This not only hampers productivity but also affects your organization's efficiency and bottom line.

Potential of Agentic Automation

Imagine no longer needing to meticulously map out every step and if else, conditions of your workflow automation. With agentic automation, you define the end goals and their corresponding APIs, and the AI figures out the rest. This shifts the paradigm from rigid workflows to dynamic, adaptive processes.

With DevRev’s AgentOS built on top of the knowledge-graph, the agents becomes more and more better at reasoning, and deciding what to automate with the skills available.

Skills and its Dynamic Execution

"Skills" or sometimes called “tools”, refer to the specific capabilities these AI agents have that enable them to perform tasks and hence achieve goals. Each node, as shown above, in this new agentic system, represents a skill the agent needs to execute. The beauty of this approach lies in the LLM’s ability to plan how to execute these skills dynamically. Unlike traditional workflows, which require precise and well-defined inputs and the nodes needed to be stitched together, AI agents handle fuzzy inputs and you don’t need to know if the data is a string, integer, or float. The AI interprets and processes it in real-time, and figure out itself.

Embracing Fuzzy Logic

AI applications thrive on handling ambiguous and variable inputs. Think of ChatGPT, where users input varied text forms—whether tabular data, markdown, or plain text. The transformations are equally flexible, producing lists, paragraphs, or other formats based on context. This adaptability is what makes AI applications so powerful and relatable in our complex world.

By embracing the inherent fuzziness of real-world data, agentic automation offer unprecedented flexibility and efficiency. This is the essence of why AI tools like ChatGPT are so beloved—they resonate with the unpredictable nature of our daily experiences.

Designing Effective Skills for AI Agents

On DevRev’s AgentOS, you can create skills powered by the workflow engine which has:-

Clear Context and Description

Each skill, whether it's fetching accounts or executing a complete workflow, must have precise descriptions and instructions. This ensures agents know exactly when and how to use them.


Skills should adeptly handle fuzzy inputs, transforming them into actionable data that leads to successful API calls.


Agents must detect and respond to failures during skill execution, enabling retries, and making sure robustness.


Can relay granular states back to AI agent to course-correct, thereby making it difficult to create “poorly designed” agents.

Exciting Times Ahead

With these intelligent tools, the need for traditional, rigid workflows fades away, replaced by dynamic, adaptable systems, much like real life, which is messy. We will start to see ‌more tools emerging which will just enable users to define the end nodes, and everything else taken care of by the agents during runtime.

This is part 4 in a series of blogs, case- study, white-paper and podcasts on “AgentOS & its capabilities”. Stay tuned…

Shashank Singh
Shashank Singh

Shashank Singh is a Member of Product Management at DevRev.