According to Gartner By 2028, 33% of enterprise software will use agentic AI, and 65% of daily work decisions will be made autonomously.
That’s not a distant future—it’s happening now.
Think about customer onboarding, lead management, or banking customer service. These processes involve multiple steps, require precision, and often slow teams down with repetitive tasks. What if they could run on autopilot?
Agentic workflows make this possible. Unlike basic automation, they combine artificial intelligence with automation to manage complex, multi-step processes—seamlessly handling customer verifications, routing qualified leads, and resolving banking queries in real time.
This shift isn’t just about efficiency. It’s about freeing teams to focus on strategy, innovation, and high-value work while AI-powered agents take care of the rest.
Let’s dive into how agentic workflows work, their impact, and why businesses are adopting them today.
What are agentic workflows?
Agentic workflows build on a concept called autonomic computing, first developed by IBM in the 1990s. This approach follows a simple cycle: Monitor, Analyze, Plan, and Execute (MAPE).
AI agents still follow this loop today—but with much more intelligence, thanks to the careful selection of appropriate AI technologies.
With machine learning and generative AI, agentic workflows can now handle more complex tasks. Unlike older AI systems that just take input and produce output, these workflows adjust, refine, and improve their approach to get better results.
Autonomous systems play a crucial role in this evolution, transitioning from supportive roles to fully independent decision-making capabilities, thereby enhancing productivity across various fields.
For example, when ChatGPT 3.5 was used in an agentic workflow instead of a simple one-shot response, its coding accuracy jumped from 48.1% to 95.1%. AI expert Andrew Ng highlighted this as proof that agentic workflows help AI reason more like humans and act with greater independence.
What does this mean for businesses?
- Automate more
- Optimize faster
- Improve processes
- Reduce manual errors
- Scale smarter
Sounds complex? Let’s understand with a simpler example
Customer Support and Agentic Workflows
Agentic workflows take on repetitive business tasks so your team doesn’t have to. You’ve probably seen this in action with customer service chatbots, supply chain tracking, data analysis, or fraud detection.
Take customer support, for example. Instead of answering every inquiry manually, an agentic workflow can step in.
An AI agent can understand customer questions, perform tasks like responding instantly, and even analyze sentiment to gauge frustration or urgency. If an issue needs human attention, it seamlessly hands it off to the right person—learning and improving over time.
What are the Three Pillars of Agentic Workflows?
Agentic workflows work independently and efficiently because of three key elements: autonomy, adaptability, and optimization. These elements work together to help businesses streamline tasks, make smarter decisions, and continuously improve.
1. Autonomy
Agentic workflows handle tasks with little to no human input, significantly improving task execution. They can analyze situations, make decisions, and adjust their actions to meet goals. This means businesses can save time, reduce manual work, and focus on bigger priorities.
2. Adaptability
Every business has unique needs, and agentic workflows adjust accordingly. They use multiple AI agents to complete tasks, responding to relevant data and changing conditions in real-time. Whether it’s shifting market trends or new user behavior, these workflows refine their approach to stay effective.
3. Optimization
Agentic workflows don’t just complete tasks—they get better over time through the use of machine learning models. By analyzing past actions and results, they fine-tune their decision-making and improve efficiency. This helps businesses boost productivity and reduce costs.
Agentic Workflows Use Cases for Multi Agent Collaboration
Now that you know the benefits of agentic workflows let’s see where they can be used and how
1. Marketing
71% of consumers expect businesses to personalize their marketing messages, but analyzing customer behavior manually takes time. How can companies deliver personalization while increasing engagement and conversions?
Agentic workflows handle this by analyzing customer data and behavior patterns, creating highly tailored experiences.
Here’s how agentic workflows improve personalization:
- Content: Generates personalized emails and ads using brand messaging tailored to each customer.
- Product Recommendations: Learns from browsing and purchase history to suggest products customers are more likely to buy.
- Customer Interactions: AI chatbots use natural language processing to analyze past conversations and sales history, providing relevant, context-aware responses.
How Skott by Lyzr can help?
Skott isn’t just an AI tool—it’s an autonomous marketing agent that runs your entire content workflow end-to-end for 365 days a year, with zero manual effort.
From real-time research using Perplexity and Google News to content creation, design, and social media management, Skott operates like a full-scale marketing team—executing, optimizing, and scaling seamlessly.
With Skott, your marketing isn’t just automated—it’s agentic, adaptive, and always-on, ensuring consistent growth while saving time and costs.
Why Jazon for Marketing?
It is the world’s first truly agentic AI SDR
✔️ Easy Customization with Built-in Features: Jazon comes with sample emails, prompts, FAQs, designs, and a user manual. The Human-in-Loop feature lets your team adjust content as needed, giving you control over your marketing strategy.
✔️ Self-Improving System: Jazon uses Reinforced Learning Human Feedback to improve its responses over time, making it smarter and more effective with each interaction.
✔️ Centralized Management & Toxicity Control: Manage workflows, track performance, and flag toxic responses through Lyzr Agent Studio, that lets you set guidelines to prevent negative interactions.
2. HR management
With economic uncertainty, businesses need to attract, manage, and retain talent more efficiently. Agentic workflows in HR help streamline hiring, onboarding, and employee engagement by automating key processes and providing data-driven insights.
Exit Interview Agent by Lyzr agent Studio
- Employees can provide unbiased, direct feedback, giving HR teams actionable insights to improve workplace culture.
- The agent analyzes all exit interviews and delivers collective feedback to leadership, ensuring they stay informed about key organizational trends.
- In the short term, this helps reduce attrition, and in the long run, it positions the company as a top employer.
3. Customer support
Today’s customers expect immediate responses, with 64% wanting real-time support, regardless of time zone. Meeting these expectations while maintaining quality service can be challenging. That’s where agentic workflows come in, offering faster, more efficient solutions that enhance customer satisfaction by up to 35%.
Automated Inquiry Triage
- Smart Categorization: Agentic workflows, through multi-agent collaboration, help by automatically categorizing and prioritizing customer inquiries based on their type (e.g., billing, technical support, or product information) and urgency. This collaborative approach allows several AI agents to work together, leveraging their individual strengths to improve task performance.
- Efficient Routing: Each inquiry is then routed to the appropriate team or channel for a faster response, ensuring high-priority issues are addressed immediately.
Issue Resolution & Escalation
- Instant Resolution: Simple, repetitive queries are handled right away by AI agents, freeing up human agents to focus on more complex issues.
- Contextual Escalation: For more intricate queries, the AI workflow seamlessly escalates the issue to a human agent, providing them with all necessary context for a quicker resolution.
But what are the major benefits of agentic workflows?
1. Boosting Productivity—AI Could Add $200–$340 Billion Annually
AI-driven workflows break down complex tasks, making work faster and more efficient. They handle repetitive, multi-step processes, allowing teams to focus on high-priority work that drives impact.
McKinsey estimates that AI could contribute an extra $200 billion to $340 billion annually through productivity improvements. When humans and AI-driven processes work together, productivity soars.
2. Smarter Decisions—Saving Up to $30 Million Annually
Agentic workflows make real-time decisions without human intervention, cutting manufacturing costs by 10–15%—up to $30 million a year.
Why?
- They adapt instantly to new data.
- They analyze information and respond in real time.
- They continuously improve, making better use of resources over time.
3. Continuous Improvement
Machine learning allows agentic workflows to refine themselves over time. They:
- Collect performance feedback after every task.
- Learn from user interactions to improve accuracy.
- Use self-reflection techniques like reinforcement learning to fine-tune decision-making.
Large language models (LLMs) play a crucial role in this continuous improvement by enabling complex task processes and iterative interactions, which enhance the efficacy of AI-driven operations.
This ongoing learning ensures businesses stay ahead and keep improving.
4. Better User Experience—Retaining Customers Costs 5–25x Less
Agentic workflows personalize interactions in real time, improving customer experience. AI agents learn from every exchange, leading to more relevant and helpful responses.
Better experiences = better retention. Since retaining a customer is 5–25 times cheaper than acquiring a new one, improving user experience directly benefits the bottom line.
5. Cost Savings—Automation Cuts Expenses by 20–30%
Agentic workflows optimize resources and reduce waste, leading to 20–30% cost savings. Automating repetitive tasks reduces labor costs and minimizes human errors.
Lower costs mean more capital available for strategic investments that drive growth.
By making businesses faster, smarter, and more cost-efficient, agentic workflows are shaping the future of work.
AI Agent Frameworks that will help you build Agentic Workflows
You know the benefits, you know the use cases but now you must be wondering how do you build an agentic workflows right? Let’s take a look at some of the agent frameworks that can help you get started
| Feature | Lyzr.ai | Langchain | Microsoft AutoGen | Crew AI | LlamaIndex |
|---|---|---|---|---|---|
| No-code/Low-code | ✅ ✅ | ⚠️ | ⚠️ | ⚠️ | ⚠️ |
| Agent-to-agent communication | ✅ ✅ | ✅ | ✅ | ✅ | ⚠️ |
| Tool integration | ✅ ✅ | ✅ | ✅ | ✅ | ✅ |
| Code execution | ✅ | ✅ | ✅ | ⚠️ | ⚠️ |
| Enterprise integration | ✅ ✅ ✅ | ✅ | ✅ | ⚠️ | ⚠️ |
| Human-in-the-loop support | ✅ | ⚠️ | ✅ | ✅ | ⚠️ |
| Knowledge-based agent support | ✅ | ⚠️ | ⚠️ | ⚠️ | ✅ |
1. Lyzr.ai
Lyzr.ai is a low-code platform for building custom AI agents, enabling rapid creation of workflows and integration with enterprise systems. It focuses on safe and responsible AI development.
Benefits:
- No-code interface for quick agent development.
- Enterprise-grade security and scalability.Use Cases:
- Automating customer support for banking and financial institutions.
- Simplifying internal workflow automation for large enterprises.
2. Langchain
Langchain is a framework for building applications with large language models, designed to enable complex workflows and robust integrations with APIs.
3. Microsoft AutoGen
An open-source framework for building multi-agent AI systems tailored for complex tasks involving multiple agents working together. It enables agent-to-agent communication, task delegation, tool integration, and code execution. The framework supports both fully autonomous and human-in-the-loop interactions.
4. Crew AI
Crew AI offers a platform for creating collaborative, multi-agent systems focused on task delegation and teamwork with specialized agents. It emphasizes communication between agents to enhance workflow efficiency.
LlamaIndex—Leveraging Large Language Models
LlamaIndex is an open-source framework that enables the development of knowledge-based agents by indexing data sources and improving information retrieval across various systems.
Get Started with Lyzr.ai
Get Started with Lyzr.ai
The reliable way to build safe and responsible agentic workflows
Lyzr Agent Studio is the only platform you need to create, deploy, and manage AI agents designed for your specific business needs. Whether you’re automating complex workflows, improving customer experiences, or simplifying internal processes, Lyzr helps you to build smarter, faster, and scalable AI solutions without the hassle of traditional development.
Why Lyzr Agent Studio?
- No-Code to Pro-Code: Whether you’re a developer looking for full control or a business leader who needs AI solutions without coding, Lyzr adapts to your skillset.
- End-to-End Agent Lifecycle: Design, test, deploy, and monitor AI agents easily—all from one platform.
- Safe and Responsible AI: Built with enterprise-grade security, ensuring compliance and ethical AI deployment.
- Real-Time Adaptability: AI agents powered by Lyzr continuously learn and adapt to changing business environments.
Who is Lyzr for?
- Developers who want to build, fine-tune, and deploy custom AI agents effortlessly.
- Business leaders searching for ready-to-use AI solutions that drive efficiency and innovation.
- Organizations aiming to scale AI adoption across departments without technical bottlenecks.
Your AI strategy starts here. Build with Lyzr Agent Studio today.
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