AI Agentic Workflows: How To Implement Them

Workflows used to mean fixed paths: Click A, then B happens. One step led to another, like clockwork — predictable but inflexible.

Now, AI agentic workflows plan their own work, select tools, learn from mistakes, and adapt to changing conditions.

Andrew Ng, founder of Deeplearning.AI, finds this game-changing. He says, “I think AI agent workflows will drive massive AI progress this year — perhaps even more than the next generation of foundation models.”

The business adoption rate is on the rise, too. With Gartner predicting that AI agents will be part of 33% of enterprise software apps, leading to 15% of day-to-day work done autonomously without human intervention, the question isn't if you'll use this technology, but when.

In this guide, we’ll discuss everything you need to know about AI-driven workflow automation.

What is an AI agentic workflow?

AI agentic workflow is a sequential process that uses large language models (LLMs) to perform complex tasks with the help of AI agents. At their core, these agents combine generative AI's cognitive abilities, natural language processing (NLP), and machine learning (ML). They make decisions based on context, learn from available data, communicate through plain language, and take specific actions to complete defined objectives.

Unlike standard automation, these workflows adapt as they run. They plan, assess progress, and change course when needed to complete tasks.

A quick look at how workflows have evolved

The concept traces back to IBM's MAPE control loop from the 1990s: monitoring, analysis, planning, and execution. Modern agentic workflows build on this foundation but with far more capability. Over the past few decades, workflows have undergone significant evolution. But here’s how it all began.

Traditional workflows operated like assembly lines. Each step happens in a fixed order with clear rules. Think of an expense report that moves from submission to manager approval to accounting in the exact same way every time. These systems can't handle exceptions well and break when faced with unexpected situations.

AI workflows added intelligence to the process.

Instead of just following rules, these workflows use machine learning models to handle certain steps. A text summarization workflow, for example, just takes in content and gives summaries.

Agentic workflows represent the next step forward. Beyond using AI for specific tasks, these systems let AI run the show. The agents decide what needs to happen next, choose which tools to use, and adjust plans based on results.

Today's most advanced systems use multi-agent workflows for more complex tasks with greater efficiency and reliability. These workflows distribute responsibilities across specialized agents working together rather than relying on a single AI agent for everything.

What makes a workflow agentic?

For an AI workflow to be truly agentic, you’ll need these four capabilities.

1. Task decomposition and planning

Agentic workflows first use the agents to divide larger tasks into smaller, manageable components. When faced with a challenging goal, the AI agent:

• Analyzes the overall task.
• Identifies logical subtasks.
• Map dependencies between steps.
• Creates a sequential priority list.

For example, when you perform tasks like processing insurance claims, an agentic system identifies the necessary steps: validating customer information, reviewing policy details, checking for fraud indicators, and calculating payouts.

It then creates an execution plan that accounts for dependencies between these steps.

2. Tool use and integration

At execution time, agentic workflows pull data from many sources (sensors, databases, APIs) and decide what to do next.

The concept was originally developed to help with computer vision challenges. Early language models couldn't process images, so developers created functions that linked them to visual APIs. This approach expanded as models like Generative Pre-trained Transformer (GPT) evolved.

Modern agentic workflows connect with external resources like:

• Web search engines to find current information.
• Code interpreters to run computations.
• APIs to interact with other services.
• Data stores to retrieve specialized knowledge.

The selection of tools can be predetermined or left to the agent's discretion. For complex tasks, allowing the agent to choose appropriate tools works best, while simpler workflows benefit from predefined tool selection.

3. Reflect and iterate

If you think the job is done after task execution, this is where it gets interesting. Agentic workflows improve through self-evaluation. Rather than delivering single-attempt outputs, they review their work, identify problems, and make refinements.

The workflows store context and feedback across interactions. This memory capability comes in two forms.

First, short-term memory keeps track of recent conversation history and current task progress, helping the agent maintain context and determine next steps. And second, long-term memory stores information across multiple sessions, enabling personalization and performance improvements over time.

Without memory, AI systems would restart from scratch with each interaction. Memory turns one-off interactions into ongoing, evolving relationships.

4. Distribute responsibilities

Complex tasks often require multiple types of expertise. Agentic workflows can distribute work across specialized AI agents, each handling different aspects of the work, much like how human teams collaborate on complex projects.

For example, this multi-agent collaboration in customer service automation would look like:

• One agent interprets customer requests.
• Another searches knowledge bases for relevant information.
• A third crafts personalized responses.
• A supervisor agent coordinates the entire process.

This division of labor enhances overall performance by leveraging each agent's strengths. It works particularly well for tasks requiring diverse skills or parallel processing.

Key components of agentic workflows

Under the hood, agentic workflows combine different technologies. When properly integrated, they create something far more powerful than any single component could achieve on its own.

AI agents

AI agents form the core intelligence within agentic workflows. Built on LLMs, these agents provide reasoning, planning, and reflection capabilities. The semantic engine (typically the LLM) provides both reasoning capabilities and a conversational interface. This allows agents to seek clarification or approval when needed while still working autonomously on routine tasks.

While traditional AI requires constant guidance, agentic AI evolves and adapts to new situations without much human guidance and training.

Robotic process automation (RPA)

RPA enables AI agents to handle rule-based, repetitive tasks across different applications. This technology creates software robots that mimic human actions like data entry, transaction processing, and report generation.

In an agentic workflow, RPA serves as the hands that perform structured work. For example, after an AI agent extracts information from unstructured invoice documents, RPA automatically enters that data into accounting systems, eliminating tedious manual work.

Natural language processing

NLP enables agentic workflows to understand and respond to human language. Here are a few critical functions that it takes care of:

• Interpreting user requests and questions.
• Understanding context and intent.
• Generating human-like responses.
• Processing text documents for information.

This component eliminates the need for specialized training or programming knowledge when working with AI agents. Users can simply express their needs in everyday language.

Machine learning algorithms

ML algorithms help agentic workflows learn from experience and improve over time. These algorithms identify patterns in data, make predictions, and optimize processes without explicit programming.

This self-improvement capability means workflows get better with use. They learn which approaches work best in specific situations and adapt their strategies accordingly.

Prompt engineering

The performance of agentic workflows depends heavily on how agents are instructed. Prompt engineering shapes agent behavior through carefully designed instructions and examples.

Some advanced prompt engineering techniques include:

• Chain of thought: Guiding the agent through logical reasoning steps.
• Planning: Breaking complex tasks into manageable steps.
• Self-reflection: Enabling the agent to review and improve its work.

These techniques help LLMs understand complex requirements and produce more accurate, relevant outputs consistently.

Workflow orchestration

This coordinates all components within an agentic system. It defines execution sequences, manages dependencies, and ensures tasks happen at the right time.

Orchestration tools provide interfaces for designing, monitoring, and troubleshooting complex workflows. They connect multiple technologies and handle scheduling, error management, and resource allocation to keep processes running smoothly.

Integrations

These connect agentic workflows with existing business systems and data sources. Integrations ensure agents can access required information and take meaningful actions within your environment. They can be of any type, such as:

• Data integrations that consolidate information into accessible formats.
• Agent frameworks like LangChain, LangGraph, and crewAI that expand capabilities.

Tool integrations that give agents access to specialized functions.

Practical use cases for agentic workflows

We’ve covered enough about the mechanics behind autonomous AI workflows. Now, let’s look at them in action and how they benefit industries in the real world.

Supply chain

Managers in supply chains now implement agentic workflows for various use cases, one of which is to resolve backorder problems.

Traditional backorder handling involves multiple steps: A system notifies customer service about unavailable items, an employee checks the customer relationship management (CRM) and enterprise resource planning (ERP) system to find alternatives, and then manually coordinates with shipping teams.

Even during disruptions like natural disasters, the workflow helps agents identify safe shipping routes and alternative suppliers without requiring manual decisions at each step.

Finance

Financial institutions use agentic workflows to combat fraud.

In traditional financial fraud detection, a system flags suspicious transactions first. Analysts then step in to manually review account history, cross-check databases, and contact customers, often delaying action.

However, in an agentic workflow:

1. A monitoring agent scans transactions, flagging anomalies.
2. An analysis agent examines patterns, cross-references historical data, and assesses behavior.
3. A response agent alerts customers and freezes suspicious activity.

Microsoft has launched Agent Flows, where users can simply define their intent in everyday language to create flows. It uses multiple agents and aims to improve process management.

Marketing

Agentic workflows autonomously lead marketing tasks, a function that previously required multiple employees to participate. For example, sending a personalized marketing campaign in an AI-powered workflow involves:

1. A segmentation agent grouping customers by demographics, behavior, and purchase history.
2. An analysis agent predicting future actions using data patterns.
3. A delivery agent sending tailored emails and social media recommendations.

Plivo CX's Audiences feature manages your customer data across touchpoints. You can import contacts from e-commerce platforms, segment them based on behaviors, and create targeted workflows.

This integration lets your AI agents access comprehensive customer profiles, enabling more personalized interactions based on purchase history and engagement patterns.

Healthcare

Agentic workflows improve healthcare by monitoring and adapting patient care. And it’s not just the clerical tasks like documentation, insurance compliance checks, or form submissions that agentic AI in healthcare can automate.

AI workflow optimization also helps with real-time patient care and ensures timely expert intervention. Here’s a quick look:

1. A diagnostic agent analyzes medical images to identify anomalies, such as early-stage cancer.
2. A care agent evaluates sensor data and recommends adjustments to personalized plans.
3. A monitoring agent tracks condition changes, alerting doctors to bigger issues.

Coding assistance

Agentic coding assistants execute codes, debug errors, refine outputs, and even create commits and pull requests with minimal input. For example, tools like Claude Code can automate software development by autonomously submitting code changes.

Here’s how it looks:

1. An AI agent generates code based on user input, using LLMs to create the required functionality.
2. A second agent reviews the code, checking for errors, style issues, and adherence to best practices.
3. The original agent refines the code based on feedback, iterating until the code meets the desired quality.

Unlike early coding assistants that only generated snippets, agentic workflows continually test and improve their work.

Customer support

AI agents streamline customer support by handling routine tasks and escalating complex issues. A typical workflow looks like this:

1. An inquiry agent receives and categorizes the customer’s query, using NLP to identify the issue.
2. The response agent generates an appropriate response based on past interactions and customer data, offering a personalized solution.
3. If unresolved, the escalation agent escalates the query to a human agent, providing context and previous interaction history.

This multi-agent approach ensures faster resolutions, improves customer satisfaction, and optimizes the workload for human agents.

Steps for Implementing AI Agentic Workflows

Now, let’s start building agentic workflows that think, adapt, and improve on their own. Here are the steps for effective implementation.

Step #1: Set specific, actionable goals

Check your existing infrastructure, available budget, and your team's technical expertise. Make sure everyone, from employees to executives and investors, understands why you're adopting AI agentic workflows.

For AI agents to deliver results, they need precise directions.

Generic goals like "improve operational efficiency" won't work. You must define exactly what you want to accomplish with measurable outcomes.

For example, if you want faster customer service, specify "reduce response time from 10 minutes to 2 minutes" or "increase first-contact resolution by 25%." This will give the workflow and the agents the direction needed.

Step #2: Build teams of specialized AI agents

AI agents work best when they focus on specific tasks. And just like skilled employees, each agent should handle what it does best.

For healthcare workflows, this means having one agent analyze medical data while another manages appointment scheduling. In financial systems, one agent might detect fraud patterns while another communicates with customers.

Identify what each step in your workflow needs, then assign the right agent with the right tools for each job.

Step #3: Ensure strict data governance

As agentic workflows become more prominent across industries, it’s important to ensure strict data governance and security policies. Apply metadata to build audit trails that track data from its origin through every access and transformation, ensuring accountability and compliance with privacy regulations.

Then, develop clear policies on how data moves through your workflow system. Define who can access what information and how it's used to avoid data breaches.

Regular audits also ensure your workflows maintain data integrity and stay within legal boundaries, even as regulations evolve.

Step #4: Start small with test runs

Test your AI workflow on contained projects before going all-in. Select a specific process with clear success metrics that allow you to see results quickly.

In this limited rollout, you’ll spot unexpected issues, so adjust your approach based on real feedback and calculate the actual return on investment (ROI) before making a larger investment.

Once your pilot confirms the workflow delivers value, use those lessons to scale gradually across more departments and processes.

Step #5: Prepare your team for AI collaboration

Get your employees ready to work alongside AI agents. Provide training that focuses on effective prompting, when to trust agent outputs and when to verify them, and understanding the workflow boundaries between human and AI tasks.

This knowledge helps staff see AI as a productivity tool rather than a threat.

After setting up your workflow structure, deploy Plivo's AI agents to handle specific customer interactions across all communication channels.

Choose from prebuilt agents that support the entire customer journey:

• Convert: Sales conversion and shopping assistant agents help customers complete purchases.

• Engage: Loyalty, upsell, and retention agents deliver personalized offers at the right moment.

• Delight: Support, order tracking, and appointment scheduling agents provide instant service.

How Plivo streamlines your workflows with AI agents

With Plivo’s AI agents, you can implement agentic workflows with zero technical complexities.

The system connects with your existing tools (CRMs, helpdesks, payment processors) to create workflows that take action. Agents access your knowledge base to deliver accurate, consistent responses in your customers' preferred languages.

Implementation is simple and easy:

• Select prebuilt agents designed for different customer journey stages (convert, engage, and delight).
• Connect to your existing business tools (say, Shopify, Stripe, or any CRM) without developer help.
• Import your knowledge base for accurate responses (no prompt engineering required).
• Launch your agents to respond, resolve, or convert.

Plivo supports all major AI models (OpenAI, Google, Anthropic, Meta), letting you choose what works best for your specific needs.

Request a trial to access Plivo’s features before you dive in.