This is the summary post of our Use Case Spotlight session on 11 April 2025, which covered AI Agents as Business Workflows.
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AI Agents as Business Workflows
AI agents represent the latest evolution in artificial intelligence, designed to autonomously perform tasks, make decisions, and interact with users or systems without constant human input. This approach leaps over SaaS, which delivers software over the cloud with user-friendly interfaces and scalable access. SaaS applications rely on users to input data, initiate actions, and interpret data, but AI agents anticipate and act on their own.
While SaaS follows pre-built workflows with significant human intervention, AI agents can own and orchestrate the workflow with minimal human intervention. They have the ability to adapt dynamically to the changes in input data, understand the context for making decisions, and learn from outcomes.
This session features the different ways in which AI agents can model various business workflows and presents a few historical perspectives, along with practical examples and demonstrations.
Key Takeaways and Webinar Recording
Check out the session recording and the key takeaways (along with timestamps in the recorded video).
Evolution of Business Workflows
If you trace back the automation of business workflows, there are three significant milestones over the last 20-odd years. The first one was the rules engines, followed by RPA (Robotic Process Automation), and then the AI agents arrived on the scene after the advent of generative AI.
Consider invoice processing as an example workflow. With the rules engine, the patterns for reading a structured invoice document were statically defined, leading to fixed rules and brittle logic. This was over two decades back when there was no scope for practical AI-based detection systems. As a result, the rules were not scalable to cover larger workflows.
Then came the era of RPA systems, which relied on partial AI capabilities based on detection, such as OCR (Optical Character Recognition), image object detection, and natural language understanding. RPA allowed business workflows to be semi-automated with partial intelligence but did not possess the ability to derive context and reasoning.
AI agents surpass the limitations of these earlier approaches to build a fully automated business workflow. It can accept structured or unstructured input, and figure out dynamic rules to derive the best path of progression for the workflow. It also possesses context and reasoning ability, and learns from the outcome to continuously improve the efficiency of workflow execution.
Usage Patterns in AI Agents
Due to the proliferation of AI agents across many business functions, there are distinct patterns emerging in their usage. Broadly, these can be categorized into four patterns of applying agentic AI for workflow automation.
AI assistant is the most basic form of AI agent which is primarily a knowledge query and retrieval assistance mechanism. AI automation takes this concept further to accomplish a task. A true AI agent is a system that can perform multiple tasks to achieve a goal, by following a multi-step workflow involving human stakeholders as well as integrations with third-party systems. Finally, a multi-agent system orchestrates two or more AI agents to achieve a cross-functional business goal across interdependent workflows, spanning multiple business functions.
Building Blocks of AI Agents
Building AI agents can be likened to having a few basic Lego block shapes that are mixed and joined in innumerable ways to build complex structures. In the same way, you can think of a few fundamental building blocks of an AI agent to build complex business workflows.
- 1Decision logic - To build decision control flows based on data patterns or outcomes of previous steps in the workflow.
- 2Data Ingest/Sink - To integrate with various databases and data stores for input/output data handling.
- 3API/Webhooks - To invoke external APIs or let external services trigger events via webhooks.
- 4Notifications - To integrate with notification services such as web push notifications, or app notifications.
- 5Reports - To provide reporting functionality for presenting the outcome of the workflow execution
- 6External Knowledge Query/Extraction - To extract or query knowledge from publicly available models, like OpenAI and others.
- 7Internal Knowledge Query/Extraction - To extract or query knowledge from internal sources such as documents and other textual repository using RAG or CAG.
- 8Remote Execution - To build custom interfaces for executing routines on a remote system via function calling, remote procedure call, or by logging in and executing commands.
- 9Human in the Loop - To build a prompt or UI interface for humans to query and control the agentic execution.
Use Cases of AI Agents
Using these building blocks, you can design simple to complex AI agents that mimic a typical business workflow. Here are the use cases covered in this session.
Cold email marketing campaign
Phishing simulation for red teaming
Web Scraping
AI Agents in Action
Using Everyday Series AI agent platform, you can build the above scenarios using a drag-and-drop visual workflow builder and host your agentic AI applications in a production-grade environment.
You can access the session recording to check the demonstration using the Everyday Series platform for:
- 1Simple language translation agent
- 2Email spam filtering agent
- 3Recruitment agent for sharing candidate profiles with the interview panel
Want to Automate Your Business Workflows?
Book a discovery call with Everyday Series and understand how to leverage the platform to build custom agents for your business requirements.
